WorldWideScience

Sample records for global observation assimilation

  1. Application of Observed Precipitation in NCEP Global and Regional Data Assimilation Systems, Including Reanalysis and Land Data Assimilation

    Science.gov (United States)

    Mitchell, K. E.

    2006-12-01

    The Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) applies several different analyses of observed precipitation in both the data assimilation and validation components of NCEP's global and regional numerical weather and climate prediction/analysis systems (including in NCEP global and regional reanalysis). This invited talk will survey these data assimilation and validation applications and methodologies, as well as the temporal frequency, spatial domains, spatial resolution, data sources, data density and data quality control in the precipitation analyses that are applied. Some of the precipitation analyses applied by EMC are produced by NCEP's Climate Prediction Center (CPC), while others are produced by the River Forecast Centers (RFCs) of the National Weather Service (NWS), or by automated algorithms of the NWS WSR-88D Radar Product Generator (RPG). Depending on the specific type of application in data assimilation or model forecast validation, the temporal resolution of the precipitation analyses may be hourly, daily, or pentad (5-day) and the domain may be global, continental U.S. (CONUS), or Mexico. The data sources for precipitation include ground-based gauge observations, radar-based estimates, and satellite-based estimates. The precipitation analyses over the CONUS are analyses of either hourly, daily or monthly totals of precipitation, and they are of two distinct types: gauge-only or primarily radar-estimated. The gauge-only CONUS analysis of daily precipitation utilizes an orographic-adjustment technique (based on the well-known PRISM precipitation climatology of Oregon State University) developed by the NWS Office of Hydrologic Development (OHD). The primary NCEP global precipitation analysis is the pentad CPC Merged Analysis of Precipitation (CMAP), which blends both gauge observations and satellite estimates. The presentation will include a brief comparison between the CMAP analysis and other global

  2. Synthesis and Assimilation Systems - Essential Adjuncts to the Global Ocean Observing System

    Science.gov (United States)

    Rienecker, Michele M.; Balmaseda, Magdalena; Awaji, Toshiyuki; Barnier, Bernard; Behringer, David; Bell, Mike; Bourassa, Mark; Brasseur, Pierre; Breivik, Lars-Anders; Carton, James; hide

    2009-01-01

    Ocean assimilation systems synthesize diverse in situ and satellite data streams into four-dimensional state estimates by combining the various observations with the model. Assimilation is particularly important for the ocean where subsurface observations, even today, are sparse and intermittent compared with the scales needed to represent ocean variability and where satellites only sense the surface. Developments in assimilation and in the observing system have advanced our understanding and prediction of ocean variations at mesoscale and climate scales. Use of these systems for assessing the observing system helps identify the strengths of each observation type. Results indicate that the ocean remains under-sampled and that further improvements in the observing system are needed. Prospects for future advances lie in improved models and better estimates of error statistics for both models and observations. Future developments will be increasingly towards consistent analyses across components of the Earth system. However, even today ocean synthesis and assimilation systems are providing products that are useful for many applications and should be considered an integral part of the global ocean observing and information system.

  3. Performance and Evaluation of the Global Modeling and Assimilation Office Observing System Simulation Experiment

    Science.gov (United States)

    Prive, Nikki; Errico, R. M.; Carvalho, D.

    2018-01-01

    The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) has spent more than a decade developing and implementing a global Observing System Simulation Experiment framework for use in evaluting both new observation types as well as the behavior of data assimilation systems. The NASA/GMAO OSSE has constantly evolved to relect changes in the Gridpoint Statistical Interpolation data assimiation system, the Global Earth Observing System model, version 5 (GEOS-5), and the real world observational network. Software and observational datasets for the GMAO OSSE are publicly available, along with a technical report. Substantial modifications have recently been made to the NASA/GMAO OSSE framework, including the character of synthetic observation errors, new instrument types, and more sophisticated atmospheric wind vectors. These improvements will be described, along with the overall performance of the current OSSE. Lessons learned from investigations into correlated errors and model error will be discussed.

  4. Effective Assimilation of Global Precipitation

    Science.gov (United States)

    Lien, G.; Kalnay, E.; Miyoshi, T.; Huffman, G. J.

    2012-12-01

    Assimilating precipitation observations by modifying the moisture and sometimes temperature profiles has been shown successful in forcing the model precipitation to be close to the observed precipitation, but only while the assimilation is taking place. After the forecast start, the model tends to "forget" the assimilation changes and lose their extra skill after few forecast hours. This suggests that this approach is not an efficient way to modify the potential vorticity field, since this is the variable that the model would remember. In this study, the ensemble Kalman filter (EnKF) method is used to effectively change the potential vorticity field by allowing ensemble members with better precipitation to receive higher weights. In addition to using an EnKF, two other changes in the precipitation assimilation process are proposed to solve the problems related to the highly non-Gaussian nature of the precipitation variable: a) transform precipitation into a Gaussian distribution based on its climatological distribution, and b) only assimilate precipitation at the location where some ensemble members have positive precipitation. The idea is first tested by the observing system simulation experiments (OSSEs) using SPEEDY, a simplified but realistic general circulation model. When the global precipitation is assimilated in addition to conventional rawinsonde observations, both the analyses and the medium range forecasts are significantly improved as compared to only having rawinsonde observations. The improvement is much reduced when only modifying the moisture field with the same approach, which shows the importance of the error covariance between precipitation and all other model variables. The effect of precipitation assimilation is larger in the Southern Hemisphere than that in the Northern Hemisphere because the Northern Hemisphere analyses are already accurate as a result of denser rawinsonde stations. Assimilation of precipitation using a more comprehensive

  5. Global Data Assimilation System (GDAS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Data Assimilation System (GDAS) is the system used by the Global Forecast System (GFS) model to place observations into a gridded model space for the...

  6. Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME

    Science.gov (United States)

    Pi, Xiaoqing; Mannucci, Anthony J.; Verkhoglyadova, Olga; Stephens, Philip; Iijima, Bryron A.

    2013-01-01

    Modeling and imaging the Earth's ionosphere as well as understanding its structures, inhomogeneities, and disturbances is a key part of NASA's Heliophysics Directorate science roadmap. This invention provides a design tool for scientific missions focused on the ionosphere. It is a scientifically important and technologically challenging task to assess the impact of a new observation system quantitatively on our capability of imaging and modeling the ionosphere. This question is often raised whenever a new satellite system is proposed, a new type of data is emerging, or a new modeling technique is developed. The proposed constellation would be part of a new observation system with more low-Earth orbiters tracking more radio occultation signals broadcast by Global Navigation Satellite System (GNSS) than those offered by the current GPS and COSMIC observation system. A simulation system was developed to fulfill this task. The system is composed of a suite of software that combines the Global Assimilative Ionospheric Model (GAIM) including first-principles and empirical ionospheric models, a multiple- dipole geomagnetic field model, data assimilation modules, observation simulator, visualization software, and orbit design, simulation, and optimization software.

  7. Assimilation of GNSS radio occultation observations in GRAPES

    Science.gov (United States)

    Liu, Y.; Xue, J.

    2014-07-01

    This paper reviews the development of the global navigation satellite system (GNSS) radio occultation (RO) observations assimilation in the Global/Regional Assimilation and PrEdiction System (GRAPES) of China Meteorological Administration, including the choice of data to assimilate, the data quality control, the observation operator, the tuning of observation error, and the results of the observation impact experiments. The results indicate that RO data have a significantly positive effect on analysis and forecast at all ranges in GRAPES not only in the Southern Hemisphere where conventional observations are lacking but also in the Northern Hemisphere where data are rich. It is noted that a relatively simple assimilation and forecast system in which only the conventional and RO observation are assimilated still has analysis and forecast skill even after nine months integration, and the analysis difference between both hemispheres is gradually reduced with height when compared with NCEP (National Centers for Enviromental Prediction) analysis. Finally, as a result of the new onboard payload of the Chinese FengYun-3 (FY-3) satellites, the research status of the RO of FY-3 satellites is also presented.

  8. Global assimilation of X Project Loon stratospheric balloon observations

    Science.gov (United States)

    Coy, L.; Schoeberl, M. R.; Pawson, S.; Candido, S.; Carver, R. W.

    2017-12-01

    Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agree well and assimilating the Loon winds have only a small impact on short-term forecasting of the Loon winds, however, in the tropics the loon winds and DAS winds often disagree substantially (8 m/s or more in magnitude) and in these cases assimilating the loon winds significantly improves the forecast of the loon winds. By highlighting cases where the Loon and DAS winds differ, these results can lead to improved understanding of stratospheric winds, especially in the tropics.

  9. Obtaining Global Picture From Single Point Observations by Combining Data Assimilation and Machine Learning Tools

    Science.gov (United States)

    Shprits, Y.; Zhelavskaya, I. S.; Kellerman, A. C.; Spasojevic, M.; Kondrashov, D. A.; Ghil, M.; Aseev, N.; Castillo Tibocha, A. M.; Cervantes Villa, J. S.; Kletzing, C.; Kurth, W. S.

    2017-12-01

    Increasing volume of satellite measurements requires deployment of new tools that can utilize such vast amount of data. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the space environment. In this study we show how measurements can be combined by means of data assimilation and how machine learning can help analyze large amounts of data and can help develop global models that are trained on single point measurement. Data Assimilation: Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude. We show results of the long term reanalysis of radiation belt measurements along with fully operational real-time predictions using data assimilative VERB code. Machine Learning: We present application of the machine learning tools for the analysis of NASA Van Allen Probes upper-hybrid frequency measurements. Using the obtained data set we train a new global predictive neural network. The results for the Van Allen Probes based neural network are compared with historical IMAGE satellite observations. We also show examples of predictions of geomagnetic indices using neural networks. Combination of machine learning and data assimilation: We discuss how data assimilation tools and machine learning tools can be combine so that physics-based insight into the dynamics of the particular system can be combined with empirical knowledge of it's non-linear behavior.

  10. Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

    Science.gov (United States)

    Lievens, H.; Martens, B.; Verhoest, N. E. C.; Hahn, S.; Reichle, R. H.; Miralles, D. G.

    2017-01-01

    Active radar backscatter (s?) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s? and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s? or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s? and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

  11. Effective assimilation of global precipitation: simulation experiments

    Directory of Open Access Journals (Sweden)

    Guo-Yuan Lien

    2013-07-01

    Full Text Available Past attempts to assimilate precipitation by nudging or variational methods have succeeded in forcing the model precipitation to be close to the observed values. However, the model forecasts tend to lose their additional skill after a few forecast hours. In this study, a local ensemble transform Kalman filter (LETKF is used to effectively assimilate precipitation by allowing ensemble members with better precipitation to receive higher weights in the analysis. In addition, two other changes in the precipitation assimilation process are found to alleviate the problems related to the non-Gaussianity of the precipitation variable: (a transform the precipitation variable into a Gaussian distribution based on its climatological distribution (an approach that could also be used in the assimilation of other non-Gaussian observations and (b only assimilate precipitation at the location where at least some ensemble members have precipitation. Unlike many current approaches, both positive and zero rain observations are assimilated effectively. Observing system simulation experiments (OSSEs are conducted using the Simplified Parametrisations, primitivE-Equation DYnamics (SPEEDY model, a simplified but realistic general circulation model. When uniformly and globally distributed observations of precipitation are assimilated in addition to rawinsonde observations, both the analyses and the medium-range forecasts of all model variables, including precipitation, are significantly improved as compared to only assimilating rawinsonde observations. The effect of precipitation assimilation on the analyses is retained on the medium-range forecasts and is larger in the Southern Hemisphere (SH than that in the Northern Hemisphere (NH because the NH analyses are already made more accurate by the denser rawinsonde stations. These improvements are much reduced when only the moisture field is modified by the precipitation observations. Both the Gaussian transformation and

  12. Data assimilation of CALIPSO aerosol observations

    Directory of Open Access Journals (Sweden)

    T. T. Sekiyama

    2010-01-01

    Full Text Available We have developed an advanced data assimilation system for a global aerosol model with a four-dimensional ensemble Kalman filter in which the Level 1B data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO were successfully assimilated for the first time, to the best of the authors' knowledge. A one-month data assimilation cycle experiment for dust, sulfate, and sea-salt aerosols was performed in May 2007. The results were validated via two independent observations: 1 the ground-based lidar network in East Asia, managed by the National Institute for Environmental Studies of Japan, and 2 weather reports of aeolian dust events in Japan. Detailed four-dimensional structures of aerosol outflows from source regions over oceans and continents for various particle types and sizes were well reproduced. The intensity of dust emission at each grid point was also corrected by this data assimilation system. These results are valuable for the comprehensive analysis of aerosol behavior as well as aerosol forecasting.

  13. Observability of global rivers with future SWOT observations

    Science.gov (United States)

    Fisher, Colby; Pan, Ming; Wood, Eric

    2017-04-01

    The Surface Water and Ocean Topography (SWOT) mission is designed to provide global observations of water surface elevation and slope from which river discharge can be estimated using a data assimilation system. This mission will provide increased spatial and temporal coverage compared to current altimeters, with an expected accuracy for water level elevations of 10 cm on rivers greater than 100 m wide. Within the 21-day repeat cycle, a river reach will be observed 2-4 times on average. Due to the relationship between the basin orientation and the orbit, these observations are not evenly distributed in time, which will impact the derived discharge values. There is, then, a need for a better understanding of how the mission will observe global river basins. In this study, we investigate how SWOT will observe global river basins and how the temporal and spatial sampling impacts the discharge estimated from assimilation. SWOT observations can be assimilated using the Inverse Streamflow Routing (ISR) model of Pan and Wood [2013] with a fixed interval Kalman smoother. Previous work has shown that the ISR assimilation method can be used to reproduce the spatial and temporal dynamics of discharge within many global basins: however, this performance was strongly impacted by the spatial and temporal availability of discharge observations. In this study, we apply the ISR method to 32 global basins with different geometries and crossing patterns for the future orbit, assimilating theoretical SWOT-retrieved "gauges". Results show that the model performance varies significantly across basins and is driven by the orientation, flow distance, and travel time in each. Based on these properties, we quantify the "observability" of each basin and relate this to the performance of the assimilation. Applying this metric globally to a large variety of basins we can gain a better understanding of the impact that SWOT observations may have across basin scales. By determining the

  14. Data Assimilation: Making Sense of Earth Observation

    Directory of Open Access Journals (Sweden)

    William Albert Lahoz

    2014-05-01

    Full Text Available Climate change, air quality and environmental degradation are important societal challenges for the 21st Century. These challenges require an intelligent response from society, which in turn requires access to information about the Earth System. This information comes from observations and prior knowledge, the latter typically embodied in a model describing relationships between variables of the Earth System. Data assimilation provides an objective methodology to combine observational and model information to provide an estimate of the most likely state and its uncertainty for the whole Earth System. This approach adds value to the observations – by filling in the spatio-temporal gaps in observations; and to the model – by constraining it with the observations. In this review paper we motivate data assimilation as a methodology to fill in the gaps in observational information; illustrate the data assimilation approach with examples that span a broad range of features of the Earth System (atmosphere, including chemistry; ocean; land surface; and discuss the outlook for data assimilation, including the novel application of data assimilation ideas to observational information obtained using Citizen Science. Ultimately, a strong motivation of data assimilation is the many benefits it provides to users. These include: providing the initial state for weather and air quality forecasts; providing analyses and reanalyses for studying the Earth System; evaluating observations, instruments and models; assessing the relative value of elements of the Global Observing System (GOS; and assessing the added value of future additions to the GOS.

  15. Assimilation of global radar backscatter and radiometer brightness temperature observations to improve soil moisture and land evaporation estimates

    NARCIS (Netherlands)

    Lievens, H.; Martens, B.; Verhoest, N.E.C.; Hahn, S.; Reichle, R.H.; Gonzalez Miralles, D.

    2016-01-01

    Active radar backscatter (σ°) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model

  16. Comparison between assimilated and non-assimilated experiments of the MACCii global reanalysis near surface ozone

    Science.gov (United States)

    Tsikerdekis, Athanasios; Katragou, Eleni; Zanis, Prodromos; Melas, Dimitrios; Eskes, Henk; Flemming, Johannes; Huijnen, Vincent; Inness, Antje; Kapsomenakis, Ioannis; Schultz, Martin; Stein, Olaf; Zerefos, Christos

    2014-05-01

    In this work we evaluate near surface ozone concentrations of the MACCii global reanalysis using measurements from the EMEP and AIRBASE database. The eight-year long reanalysis of atmospheric composition data covering the period 2003-2010 was constructed as part of the FP7-funded Monitoring Atmospheric Composition and Climate project by assimilating satellite data into a global model and data assimilation system (Inness et al., 2013). The study mainly focuses in the differences between the assimilated and the non-assimilated experiments and aims to identify and quantify any improvements achieved by adding data assimilation to the system. Results are analyzed in eight European sub-regions and region-specific Taylor plots illustrate the evaluation and the overall predictive skill of each experiment. The diurnal and annual cycles of near surface ozone are evaluated for both experiments. Furthermore ozone exposure indices for crop growth (AOT40), human health (SOMO35) and the number of days that 8-hour ozone averages exceeded 60ppb and 90ppb have been calculated for each station based on both observed and simulated data. Results indicate mostly improvement of the assimilated experiment with respect to the high near surface ozone concentrations, the diurnal cycle and range and the bias in comparison to the non-assimilated experiment. The limitations of the comparison between assimilated and non-assimilated experiments for near surface ozone are also discussed.

  17. Initializing carbon cycle predictions from the Community Land Model by assimilating global biomass observations

    Science.gov (United States)

    Fox, A. M.; Hoar, T. J.; Smith, W. K.; Moore, D. J.

    2017-12-01

    The locations and longevity of terrestrial carbon sinks remain uncertain, however it is clear that in order to predict long-term climate changes the role of the biosphere in surface energy and carbon balance must be understood and incorporated into earth system models (ESMs). Aboveground biomass, the amount of carbon stored in vegetation, is a key component of the terrestrial carbon cycle, representing the balance of uptake through gross primary productivity (GPP), losses from respiration, senescence and mortality over hundreds of years. The best predictions of current and future land-atmosphere fluxes are likely from the integration of process-based knowledge contained in models and information from observations of changes in carbon stocks using data assimilation (DA). By exploiting long times series, it is possible to accurately detect variability and change in carbon cycle dynamics through monitoring ecosystem states, for example biomass derived from vegetation optical depth (VOD), and use this information to initialize models before making predictions. To make maximum use of information about the current state of global ecosystems when using models we have developed a system that combines the Community Land Model (CLM) with the Data Assimilation Research Testbed (DART), a community tool for ensemble DA. This DA system is highly innovative in its complexity, completeness and capabilities. Here we described a series of activities, using both Observation System Simulation Experiments (OSSEs) and real observations, that have allowed us to quantify the potential impact of assimilating VOD data into CLM-DART on future land-atmosphere fluxes. VOD data are particularly suitable to use in this activity due to their long temporal coverage and appropriate scale when combined with CLM, but their absolute values rely on many assumptions. Therefore, we have had to assess the implications of the VOD retrieval algorithms, with an emphasis on detecting uncertainty due to

  18. Data Assimilation to Extract Soil Moisture Information from SMAP Observations

    Directory of Open Access Journals (Sweden)

    Jana Kolassa

    2017-11-01

    Full Text Available This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP observations. Neural network (NN and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE by 0.005 m 3 m − 3 and 0.001 m 3 m − 3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m − 3 , but increased the root zone bias by 0.014 m 3 m − 3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a

  19. Understanding the Global Water and Energy Cycle Through Assimilation of Precipitation-Related Observations: Lessons from TRMM and Prospects for GPM

    Science.gov (United States)

    Hou, Arthur; Zhang, Sara; daSilva, Arlindo; Li, Frank; Atlas, Robert (Technical Monitor)

    2002-01-01

    Understanding the Earth's climate and how it responds to climate perturbations relies on what we know about how atmospheric moisture, clouds, latent heating, and the large-scale circulation vary with changing climatic conditions. The physical process that links these key climate elements is precipitation. Improving the fidelity of precipitation-related fields in global analyses is essential for gaining a better understanding of the global water and energy cycle. In recent years, research and operational use of precipitation observations derived from microwave sensors such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Special Sensor Microwave/Imager (SSM/I) have shown the tremendous potential of using these data to improve global modeling, data assimilation, and numerical weather prediction. We will give an overview of the benefits of assimilating TRMM and SSM/I rain rates and discuss developmental strategies for using space-based rainfall and rainfall-related observations to improve forecast models and climate datasets in preparation for the proposed multi-national Global Precipitation Mission (GPM).

  20. The OSSE Framework at the NASA Global Modeling and Assimilation Office (GMAO)

    Science.gov (United States)

    Moradi, I.; Prive, N.; McCarty, W.; Errico, R. M.; Gelaro, R.

    2017-12-01

    This abstract summarizes the OSSE framework developed at the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA/GMAO). Some of the OSSE techniques developed at GMAO including simulation of realistic observations, e.g., adding errors to simulated observations, are now widely used by the community to evaluate the impact of new observations on the weather forecasts. This talk presents some of the recent progresses and challenges in simulating realistic observations, radiative transfer modeling support for the GMAO OSSE activities, assimilation of OSSE observations into data assimilation systems, and evaluating the impact of simulated observations on the forecast skills.

  1. The Effects of Chlorophyll Assimilation on Carbon Fluxes in a Global Biogeochemical Model. [Technical Report Series on Global Modeling and Data Assimilation

    Science.gov (United States)

    Koster, Randal D. (Editor); Rousseaux, Cecile Severine; Gregg, Watson W.

    2014-01-01

    In this paper, we investigated whether the assimilation of remotely-sensed chlorophyll data can improve the estimates of air-sea carbon dioxide fluxes (FCO2). Using a global, established biogeochemical model (NASA Ocean Biogeochemical Model, NOBM) for the period 2003-2010, we found that the global FCO2 values produced in the free-run and after assimilation were within -0.6 mol C m(sup -2) y(sup -1) of the observations. The effect of satellite chlorophyll assimilation was assessed in 12 major oceanographic regions. The region with the highest bias was the North Atlantic. Here the model underestimated the fluxes by 1.4 mol C m(sup -2) y(sup -1) whereas all the other regions were within 1 mol C m(sup -2) y(sup -1) of the data. The FCO2 values were not strongly impacted by the assimilation, and the uncertainty in FCO2 was not decreased, despite the decrease in the uncertainty in chlorophyll concentration. Chlorophyll concentrations were within approximately 25% of the database in 7 out of the 12 regions, and the assimilation improved the chlorophyll concentration in the regions with the highest bias by 10-20%. These results suggest that the assimilation of chlorophyll data does not considerably improve FCO2 estimates and that other components of the carbon cycle play a role that could further improve our FCO2 estimates.

  2. Development of KIAPS Observation Processing Package for Data Assimilation System

    Science.gov (United States)

    Kang, Jeon-Ho; Chun, Hyoung-Wook; Lee, Sihye; Han, Hyun-Jun; Ha, Su-Jin

    2015-04-01

    The Korea Institute of Atmospheric Prediction Systems (KIAPS) was founded in 2011 by the Korea Meteorological Administration (KMA) to develop Korea's own global Numerical Weather Prediction (NWP) system as nine year (2011-2019) project. Data assimilation team at KIAPS has been developing the observation processing system (KIAPS Package for Observation Processing: KPOP) to provide optimal observations to the data assimilation system for the KIAPS Global Model (KIAPS Integrated Model - Spectral Element method based on HOMME: KIM-SH). Currently, the KPOP is capable of processing the satellite radiance data (AMSU-A, IASI), GPS Radio Occultation (GPS-RO), AIRCRAFT (AMDAR, AIREP, and etc…), and synoptic observation (SONDE and SURFACE). KPOP adopted Radiative Transfer for TOVS version 10 (RTTOV_v10) to get brightness temperature (TB) for each channel at top of the atmosphere (TOA), and Radio Occultation Processing Package (ROPP) 1-dimensional forward module to get bending angle (BA) at each tangent point. The observation data are obtained from the KMA which has been composited with BUFR format to be converted with ODB that are used for operational data assimilation and monitoring at the KMA. The Unified Model (UM), Community Atmosphere - Spectral Element (CAM-SE) and KIM-SH model outputs are used for the bias correction (BC) and quality control (QC) of the observations, respectively. KPOP provides radiance and RO data for Local Ensemble Transform Kalman Filter (LETKF) and also provides SONDE, SURFACE and AIRCRAFT data for Three-Dimensional Variational Assimilation (3DVAR). We are expecting all of the observation type which processed in KPOP could be combined with both of the data assimilation method as soon as possible. The preliminary results from each observation type will be introduced with the current development status of the KPOP.

  3. Scalar and Vector Spherical Harmonics for Assimilation of Global Datasets in the Ionosphere and Thermosphere

    Science.gov (United States)

    Miladinovich, D.; Datta-Barua, S.; Bust, G. S.; Ramirez, U.

    2017-12-01

    Understanding physical processes during storm time in the ionosphere-thermosphere (IT) system is limited, in part, due to the inability to obtain accurate estimates of IT states on a global scale. One reason for this inability is the sparsity of spatially distributed high quality data sets. Data assimilation is showing promise toward enabling global estimates by blending high quality observational data sets with established climate models. We are continuing development of an algorithm called Estimating Model Parameters for Ionospheric Reverse Engineering (EMPIRE) to enable assimilation of global datasets for storm time estimates of IT drivers. EMPIRE is a data assimilation algorithm that uses a Kalman filtering routine to ingest model and observational data. The EMPIRE algorithm is based on spherical harmonics which provide a spherically symmetric, smooth, continuous, and orthonormal set of basis functions suitable for a spherical domain such as Earth's IT region (200-600 km altitude). Once the basis function coefficients are determined, the newly fitted function represents the disagreement between observational measurements and models. We apply spherical harmonics to study the March 17, 2015 storm. Data sources include Fabry-Perot interferometer neutral wind measurements and global Ionospheric Data Assimilation 4 Dimensional (IDA4D) assimilated total electron content (TEC). Models include Weimer 2000 electric potential, International Geomagnetic Reference Field (IGRF) magnetic field, and Horizontal Wind Model 2014 (HWM14) neutral winds. We present the EMPIRE assimilation results of Earth's electric potential and thermospheric winds. We also compare EMPIRE storm time E cross B ion drift estimates to measured drifts produced from the Super Dual Auroral Radar Network (SuperDARN) and Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) measurement datasets. The analysis from these results will enable the generation of globally assimilated

  4. Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

    Science.gov (United States)

    Scholze, Marko; Buchwitz, Michael; Dorigo, Wouter; Guanter, Luis; Quegan, Shaun

    2017-07-01

    The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al.(2005), emphasising the rapid advance in relevant space-based observations.

  5. Assimilation of global versus local data sets into a regional model of the Gulf Stream system. 1. Data effectiveness

    Science.gov (United States)

    Malanotte-Rizzoli, Paola; Young, Roberta E.

    1995-12-01

    The primary objective of this paper is to assess the relative effectiveness of data sets with different space coverage and time resolution when they are assimilated into an ocean circulation model. We focus on obtaining realistic numerical simulations of the Gulf Stream system typically of the order of 3-month duration by constructing a "synthetic" ocean simultaneously consistent with the model dynamics and the observations. The model used is the Semispectral Primitive Equation Model. The data sets are the "global" Optimal Thermal Interpolation Scheme (OTIS) 3 of the Fleet Numerical Oceanography Center providing temperature and salinity fields with global coverage and with bi-weekly frequency, and the localized measurements, mostly of current velocities, from the central and eastern array moorings of the Synoptic Ocean Prediction (SYNOP) program, with daily frequency but with a very small spatial coverage. We use a suboptimal assimilation technique ("nudging"). Even though this technique has already been used in idealized data assimilation studies, to our knowledge this is the first study in which the effectiveness of nudging is tested by assimilating real observations of the interior temperature and salinity fields. This is also the first work in which a systematic assimilation is carried out of the localized, high-quality SYNOP data sets in numerical experiments longer than 1-2 weeks, that is, not aimed to forecasting. We assimilate (1) the global OTIS 3 alone, (2) the local SYNOP observations alone, and (3) both OTIS 3 and SYNOP observations. We assess the success of the assimilations with quantitative measures of performance, both on the global and local scale. The results can be summarized as follows. The intermittent assimilation of the global OTIS 3 is necessary to keep the model "on track" over 3-month simulations on the global scale. As OTIS 3 is assimilated at every model grid point, a "gentle" weight must be prescribed to it so as not to overconstrain

  6. Global SWOT Data Assimilation of River Hydrodynamic Model; the Twin Simulation Test of CaMa-Flood

    Science.gov (United States)

    Ikeshima, D.; Yamazaki, D.; Kanae, S.

    2016-12-01

    CaMa-Flood is a global scale model for simulating hydrodynamics in large scale rivers. It can simulate river hydrodynamics such as river discharge, flooded area, water depth and so on by inputting water runoff derived from land surface model. Recently many improvements at parameters or terrestrial data are under process to enhance the reproducibility of true natural phenomena. However, there are still some errors between nature and simulated result due to uncertainties in each model. SWOT (Surface water and Ocean Topography) is a satellite, which is going to be launched in 2021, can measure open water surface elevation. SWOT observed data can be used to calibrate hydrodynamics model at river flow forecasting and is expected to improve model's accuracy. Combining observation data into model to calibrate is called data assimilation. In this research, we developed data-assimilated river flow simulation system in global scale, using CaMa-Flood as river hydrodynamics model and simulated SWOT as observation data. Generally at data assimilation, calibrating "model value" with "observation value" makes "assimilated value". However, the observed data of SWOT satellite will not be available until its launch in 2021. Instead, we simulated the SWOT observed data using CaMa-Flood. Putting "pure input" into CaMa-Flood produce "true water storage". Extracting actual daily swath of SWOT from "true water storage" made simulated observation. For "model value", we made "disturbed water storage" by putting "noise disturbed input" to CaMa-Flood. Since both "model value" and "observation value" are made by same model, we named this twin simulation. At twin simulation, simulated observation of "true water storage" is combined with "disturbed water storage" to make "assimilated value". As the data assimilation method, we used ensemble Kalman filter. If "assimilated value" is closer to "true water storage" than "disturbed water storage", the data assimilation can be marked effective. Also

  7. An Initial Assessment of the Impact of CYGNSS Ocean Surface Wind Assimilation on Navy Global and Mesoscale Numerical Weather Prediction

    Science.gov (United States)

    Baker, N. L.; Tsu, J.; Swadley, S. D.

    2017-12-01

    We assess the impact of assimilation of CYclone Global Navigation Satellite System (CYGNSS) ocean surface winds observations into the NAVGEM[i] global and COAMPS®[ii] mesoscale numerical weather prediction (NWP) systems. Both NAVGEM and COAMPS® used the NRL 4DVar assimilation system NAVDAS-AR[iii]. Long term monitoring of the NAVGEM Forecast Sensitivity Observation Impact (FSOI) indicates that the forecast error reduction for ocean surface wind vectors (ASCAT and WindSat) are significantly larger than for SSMIS wind speed observations. These differences are larger than can be explained by simply two pieces of information (for wind vectors) versus one (wind speed). To help understand these results, we conducted a series of Observing System Experiments (OSEs) to compare the assimilation of ASCAT wind vectors with the equivalent (computed) ASCAT wind speed observations. We found that wind vector assimilation was typically 3 times more effective at reducing the NAVGEM forecast error, with a higher percentage of beneficial observations. These results suggested that 4DVar, in the absence of an additional nonlinear outer loop, has limited ability to modify the analysis wind direction. We examined several strategies for assimilating CYGNSS ocean surface wind speed observations. In the first approach, we assimilated CYGNSS as wind speed observations, following the same methodology used for SSMIS winds. The next two approaches converted CYGNSS wind speed to wind vectors, using NAVGEM sea level pressure fields (following Holton, 1979), and using NAVGEM 10-m wind fields with the AER Variational Analysis Method. Finally, we compared these methods to CYGNSS wind speed assimilation using multiple outer loops with NAVGEM Hybrid 4DVar. Results support the earlier studies suggesting that NAVDAS-AR wind speed assimilation is sub-optimal. We present detailed results from multi-month NAVGEM assimilation runs along with case studies using COAMPS®. Comparisons include the fit of

  8. Dynamic Responses of the Earth's Outer Core to Assimilation of Observed Geomagnetic Secular Variation

    Science.gov (United States)

    Kuang, Weijia; Tangborn, Andrew

    2014-01-01

    Assimilation of surface geomagnetic observations and geodynamo models has advanced very quickly in recent years. However, compared to advanced data assimilation systems in meteorology, geomagnetic data assimilation (GDAS) is still in an early stage. Among many challenges ranging from data to models is the disparity between the short observation records and the long time scales of the core dynamics. To better utilize available observational information, we have made an effort in this study to directly assimilate the Gauss coefficients of both the core field and its secular variation (SV) obtained via global geomagnetic field modeling, aiming at understanding the dynamical responses of the core fluid to these additional observational constraints. Our studies show that the SV assimilation helps significantly to shorten the dynamo model spin-up process. The flow beneath the core-mantle boundary (CMB) responds significantly to the observed field and its SV. The strongest responses occur in the relatively small scale flow (of the degrees L is approx. 30 in spherical harmonic expansions). This part of the flow includes the axisymmetric toroidal flow (of order m = 0) and non-axisymmetric poloidal flow with m (is) greater than 5. These responses can be used to better understand the core flow and, in particular, to improve accuracies of predicting geomagnetic variability in future.

  9. Technical Report Series on Global Modeling and Data Assimilation, Volume 43. MERRA-2; Initial Evaluation of the Climate

    Science.gov (United States)

    Koster, Randal D. (Editor); Bosilovich, Michael G.; Akella, Santha; Lawrence, Coy; Cullather, Richard; Draper, Clara; Gelaro, Ronald; Kovach, Robin; Liu, Qing; Molod, Andrea; hide

    2015-01-01

    The years since the introduction of MERRA have seen numerous advances in the GEOS-5 Data Assimilation System as well as a substantial decrease in the number of observations that can be assimilated into the MERRA system. To allow continued data processing into the future, and to take advantage of several important innovations that could improve system performance, a decision was made to produce MERRA-2, an updated retrospective analysis of the full modern satellite era. One of the many advances in MERRA-2 is a constraint on the global dry mass balance; this allows the global changes in water by the analysis increment to be near zero, thereby minimizing abrupt global interannual variations due to changes in the observing system. In addition, MERRA-2 includes the assimilation of interactive aerosols into the system, a feature of the Earth system absent from previous reanalyses. Also, in an effort to improve land surface hydrology, observations-corrected precipitation forcing is used instead of model-generated precipitation. Overall, MERRA-2 takes advantage of numerous updates to the global modeling and data assimilation system. In this document, we summarize an initial evaluation of the climate in MERRA-2, from the surface to the stratosphere and from the tropics to the poles. Strengths and weaknesses of the MERRA-2 climate are accordingly emphasized.

  10. Accelerating assimilation development for new observing systems using EFSO

    Science.gov (United States)

    Lien, Guo-Yuan; Hotta, Daisuke; Kalnay, Eugenia; Miyoshi, Takemasa; Chen, Tse-Chun

    2018-03-01

    To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

  11. Assessment of Global Forecast Ocean Assimilation Model (FOAM) using new satellite SST data

    Science.gov (United States)

    Ascione Kenov, Isabella; Sykes, Peter; Fiedler, Emma; McConnell, Niall; Ryan, Andrew; Maksymczuk, Jan

    2016-04-01

    There is an increased demand for accurate ocean weather information for applications in the field of marine safety and navigation, water quality, offshore commercial operations, monitoring of oil spills and pollutants, among others. The Met Office, UK, provides ocean forecasts to customers from governmental, commercial and ecological sectors using the Global Forecast Ocean Assimilation Model (FOAM), an operational modelling system which covers the global ocean and runs daily, using the NEMO (Nucleus for European Modelling of the Ocean) ocean model with horizontal resolution of 1/4° and 75 vertical levels. The system assimilates salinity and temperature profiles, sea surface temperature (SST), sea surface height (SSH), and sea ice concentration observations on a daily basis. In this study, the FOAM system is updated to assimilate Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) SST data. Model results from one month trials are assessed against observations using verification tools which provide a quantitative description of model performance and error, based on statistical metrics, including mean error, root mean square error (RMSE), correlation coefficient, and Taylor diagrams. A series of hindcast experiments is used to run the FOAM system with AMSR2 and SEVIRI SST data, using a control run for comparison. Results show that all trials perform well on the global ocean and that largest SST mean errors were found in the Southern hemisphere. The geographic distribution of the model error for SST and temperature profiles are discussed using statistical metrics evaluated over sub-regions of the global ocean.

  12. Assimilation of wind speed and direction observations: results from real observation experiments

    Directory of Open Access Journals (Sweden)

    Feng Gao

    2015-06-01

    Full Text Available The assimilation of wind observations in the form of speed and direction (asm_sd by the Weather Research and Forecasting Model Data Assimilation System (WRFDA was performed using real data and employing a series of cycling assimilation experiments for a 2-week period, as a follow-up for an idealised post hoc assimilation experiment. The satellite-derived Atmospheric Motion Vectors (AMV and surface dataset in Meteorological Assimilation Data Ingest System (MADIS were assimilated. This new method takes into account the observation errors of both wind speed (spd and direction (dir, and WRFDA background quality control (BKG-QC influences the choice of wind observations, due to data conversions between (u,v and (spd, dir. The impacts of BKG-QC, as well as the new method, on the wind analysis were analysed separately. Because the dir observational errors produced by different platforms are not known or tuned well in WRFDA, a practical method, which uses similar assimilation weights in comparative trials, was employed to estimate the spd and dir observation errors. The asm_sd produces positive impacts on analyses and short-range forecasts of spd and dir with smaller root-mean-square errors than the u,v-based system. The bias of spd analysis decreases by 54.8%. These improvements result partly from BKG-QC screening of spd and dir observations in a direct way, but mainly from the independent impact of spd (dir data assimilation on spd (dir analysis, which is the primary distinction from the standard WRFDA method. The potential impacts of asm_sd on precipitation forecasts were evaluated. Results demonstrate that the asm_sd is able to indirectly improve the precipitation forecasts by improving the prediction accuracies of key wind-related factors leading to precipitation (e.g. warm moist advection and frontogenesis.

  13. Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

    Science.gov (United States)

    Girotto, Manuela

    2018-01-01

    Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

  14. Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

    Directory of Open Access Journals (Sweden)

    Brian P. Reen

    2018-01-01

    Full Text Available Aircraft-based observations are a promising source of above-surface observations for assimilation into mesoscale model simulations. The Tropospheric Airborne Meteorological Data Reporting (TAMDAR observations have potential advantages over some other aircraft observations including the presence of water vapor observations. The impact of assimilating TAMDAR observations via observation nudging in 1 km horizontal grid spacing Weather Research and Forecasting model simulations is evaluated using five cases centered over California. Overall, the impact of assimilating the observations is mixed, with the layer with the greatest benefit being above the surface in the lowest 1000 m above ground level and the variable showing the most consistent benefit being temperature. Varying the nudging configuration demonstrates the sensitivity of the results to details of the assimilation, but does not clearly demonstrate the superiority of a specific configuration.

  15. Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations

    KAUST Repository

    Viswanadhapalli, Yesubabu

    2015-08-20

    The impact of variational data assimilation for predicting two heavy rainfall events that caused devastating floods in Jeddah, Saudi Arabia is studied using the Weather Research and Forecasting (WRF) model. On 25 November 2009 and 26 January 2011, the city was deluged with more than double the annual rainfall amount caused by convective storms. We used a high resolution, two-way nested domain WRF model to simulate the two rainfall episodes. Simulations include control runs initialized with National Center for Environmental Prediction (NCEP) Global Forecasting System (GFS) data and 3-Dimensional Variational (3DVAR) data assimilation experiments conducted by assimilating NCEP prepbufr and radiance observations. Observations from Automated Weather Stations (AWS), synoptic charts, radar reflectivity and satellite pictures from the Presidency of Meteorology and Environment (PME), Jeddah, Saudi Arabia are used to assess the forecasting results. To evaluate the impact of the different assimilated observational datasets on the simulation of the major flooding event of 2009, we conducted 3DVAR experiments assimilating individual sources and a combination of all data sets. Results suggest that while the control run had a tendency to predict the storm earlier than observed, the assimilation of profile observations greatly improved the model\\'s thermodynamic structure and lead to better representation of simulated rainfall both in timing and amount. The experiment with assimilation of all available observations compared best with observed rainfall in terms of timing of the storm and rainfall distribution, demonstrating the importance of assimilating different types of observations. Retrospective experiments with and without data assimilation, for three different model lead times (48, 72 and 96-h), were performed to examine the skill of WRF model to predict the heavy rainfall events. Quantitative rainfall analysis of these simulations suggests that 48-h lead time runs with

  16. Assessing the impact of multiple altimeter missions and Argo in a global eddy-permitting data assimilation system

    Science.gov (United States)

    Verrier, Simon; Le Traon, Pierre-Yves; Remy, Elisabeth

    2017-12-01

    A series of observing system simulation experiments (OSSEs) is carried out with a global data assimilation system at 1/4° resolution using simulated data derived from a 1/12° resolution free-run simulation. The objective is to not only quantify how well multiple altimeter missions and Argo profiling floats can constrain the global ocean analysis and 7-day forecast at 1/4° resolution but also to better understand the sensitivity of results to data assimilation techniques used in Mercator Ocean operational systems. The impact of multiple altimeter data is clearly evidenced even at a 1/4° resolution. Seven-day forecasts of sea level and ocean currents are significantly improved when moving from one altimeter to two altimeters not only on the sea level, but also on the 3-D thermohaline structure and currents. In high-eddy-energy regions, sea level and surface current 7-day forecast errors when assimilating one altimeter data set are respectively 20 and 45 % of the error of the simulation without assimilation. Seven-day forecasts of sea level and ocean currents continue to be improved when moving from one altimeter to two altimeters with a relative error reduction of almost 30 %. The addition of a third altimeter still improves the 7-day forecasts even at this medium 1/4° resolution and brings an additional relative error reduction of about 10 %. The error level of the analysis with one altimeter is close to the 7-day forecast error level when two or three altimeter data sets are assimilated. Assimilating altimeter data also improves the representation of the 3-D ocean fields. The addition of Argo has a major impact on improving temperature and demonstrates the essential role of Argo together with altimetry in constraining a global data assimilation system. Salinity fields are only marginally improved. Results derived from these OSSEs are consistent with those derived from experiments with real data (observing system evaluations, OSEs) but they allow for more

  17. Advances In Global Aerosol Modeling Applications Through Assimilation of Satellite-Based Lidar Measurements

    Science.gov (United States)

    Campbell, James; Hyer, Edward; Zhang, Jianglong; Reid, Jeffrey; Westphal, Douglas; Xian, Peng; Vaughan, Mark

    2010-05-01

    Modeling the instantaneous three-dimensional aerosol field and its downwind transport represents an endeavor with many practical benefits foreseeable to air quality, aviation, military and science agencies. The recent proliferation of multi-spectral active and passive satellite-based instruments measuring aerosol physical properties has served as an opportunity to develop and refine the techniques necessary to make such numerical modeling applications possible. Spurred by high-resolution global mapping of aerosol source regions, and combined with novel multivariate data assimilation techniques designed to consider these new data streams, operational forecasts of visibility and aerosol optical depths are now available in near real-time1. Active satellite-based aerosol profiling, accomplished using lidar instruments, represents a critical element for accurate analysis and transport modeling. Aerosol source functions, alone, can be limited in representing the macrophysical structure of injection scenarios within a model. Two-dimensional variational (2D-VAR; x, y) assimilation of aerosol optical depth from passive satellite observations significantly improves the analysis of the initial state. However, this procedure can not fully compensate for any potential vertical redistribution of mass required at the innovation step. The expense of an inaccurate vertical analysis of aerosol structure is corresponding errors downwind, since trajectory paths within successive forecast runs will likely diverge with height. In this paper, the application of a newly-designed system for 3D-VAR (x,y,z) assimilation of vertical aerosol extinction profiles derived from elastic-scattering lidar measurements is described [Campbell et al., 2009]. Performance is evaluated for use with the U. S. Navy Aerosol Analysis and Prediction System (NAAPS) by assimilating NASA/CNES satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 0.532 μm measurements [Winker et al., 2009

  18. Impact of Assimilation on Heavy Rainfall Simulations Using WRF Model: Sensitivity of Assimilation Results to Background Error Statistics

    Science.gov (United States)

    Rakesh, V.; Kantharao, B.

    2017-03-01

    Data assimilation is considered as one of the effective tools for improving forecast skill of mesoscale models. However, for optimum utilization and effective assimilation of observations, many factors need to be taken into account while designing data assimilation methodology. One of the critical components that determines the amount and propagation observation information into the analysis, is model background error statistics (BES). The objective of this study is to quantify how BES in data assimilation impacts on simulation of heavy rainfall events over a southern state in India, Karnataka. Simulations of 40 heavy rainfall events were carried out using Weather Research and Forecasting Model with and without data assimilation. The assimilation experiments were conducted using global and regional BES while the experiment with no assimilation was used as the baseline for assessing the impact of data assimilation. The simulated rainfall is verified against high-resolution rain-gage observations over Karnataka. Statistical evaluation using several accuracy and skill measures shows that data assimilation has improved the heavy rainfall simulation. Our results showed that the experiment using regional BES outperformed the one which used global BES. Critical thermo-dynamic variables conducive for heavy rainfall like convective available potential energy simulated using regional BES is more realistic compared to global BES. It is pointed out that these results have important practical implications in design of forecast platforms while decision-making during extreme weather events

  19. Assessment of Two Types of Observations (SATWND and GPSRO) for the Operational Global 4DVAR System

    Science.gov (United States)

    Leng, H.

    2017-12-01

    The performance of a data assimilation system is significantly dependent on the quality and quantity of observations assimilated. In these years, more and more satellite observations have been applied in many operational assimilation systems. In this paper, the assessment of satellite-derived winds (SATWND) and GPS radio occultation (GPSRO) bending angles has been performed using a range of diagnostics. The main positive impacts are made when satellite-derived cloud data (GOES cloud data and MODIS cloud data) is assimilated, but benefit is hardly obtained from GPSRO data in the Operational Global 4DVAR System. In a full system configuration, the assimilation of satellite-derived observations is globally beneficial on the analysis, and the benefit can be well propagated into the forecast. The assimilation of the GPSRO observations has a slightly positive impact in the Tropics, but is neutral in the Northern Hemisphere and in the Southern Hemisphere. To assess the synergies of satellite-derived observations with other types of observation, experiments assimilating satellite-derived data and AMSU-A and AMSU-B observations were run. The results show that the analysis increments structure is not modified when AMSU-A and AMSU-B observations are also assimilated. This suggests that the impact of satellite-derived observations is not limited by the large impact of satellite radiance observations.

  20. Global NOx emission estimates derived from an assimilation of OMI tropospheric NO2 columns

    Directory of Open Access Journals (Sweden)

    K. Sudo

    2012-03-01

    Full Text Available A data assimilation system has been developed to estimate global nitrogen oxides (NOx emissions using OMI tropospheric NO2 columns (DOMINO product and a global chemical transport model (CTM, the Chemical Atmospheric GCM for Study of Atmospheric Environment and Radiative Forcing (CHASER. The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NOx emissions with a horizontal resolution of 2.8° during the years 2005 and 2006. The background error covariance estimated from the ensemble CTM forecasts explicitly represents non-direct relationships between the emissions and tropospheric columns caused by atmospheric transport and chemical processes. In comparison to the a priori emissions based on bottom-up inventories, the optimized emissions were higher over eastern China, the eastern United States, southern Africa, and central-western Europe, suggesting that the anthropogenic emissions are mostly underestimated in the inventories. In addition, the seasonality of the estimated emissions differed from that of the a priori emission over several biomass burning regions, with a large increase over Southeast Asia in April and over South America in October. The data assimilation results were validated against independent data: SCIAMACHY tropospheric NO2 columns and vertical NO2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO2 fields; this implies that the data assimilation system efficiently derives NOx emissions from concentration observations. We also demonstrated that biases in the satellite retrieval and model settings used in the data assimilation largely affect the magnitude of estimated emissions. These dependences should be carefully considered for better understanding NOx sources from top-down approaches.

  1. The Impact of the Assimilation of Aquarius Sea Surface Salinity Data in the GEOS Ocean Data Assimilation System

    Science.gov (United States)

    Vernieres, Guillaume Rene Jean; Kovach, Robin M.; Keppenne, Christian L.; Akella, Santharam; Brucker, Ludovic; Dinnat, Emmanuel Phillippe

    2014-01-01

    Ocean salinity and temperature differences drive thermohaline circulations. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius alongtrack retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with insitu salinity observations from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in-situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in-situ (Argo) and space-borne surface (SSS) observations

  2. Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

    Science.gov (United States)

    Isern-Fontanet, Jordi; Ballabrera-Poy, Joaquim; Turiel, Antonio; García-Ladona, Emilio

    2017-10-01

    Ocean currents play a key role in Earth's climate - they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

  3. Sequential assimilation of multi-mission dynamical topography into a global finite-element ocean model

    Directory of Open Access Journals (Sweden)

    S. Skachko

    2008-12-01

    Full Text Available This study focuses on an accurate estimation of ocean circulation via assimilation of satellite measurements of ocean dynamical topography into the global finite-element ocean model (FEOM. The dynamical topography data are derived from a complex analysis of multi-mission altimetry data combined with a referenced earth geoid. The assimilation is split into two parts. First, the mean dynamic topography is adjusted. To this end an adiabatic pressure correction method is used which reduces model divergence from the real evolution. Second, a sequential assimilation technique is applied to improve the representation of thermodynamical processes by assimilating the time varying dynamic topography. A method is used according to which the temperature and salinity are updated following the vertical structure of the first baroclinic mode. It is shown that the method leads to a partially successful assimilation approach reducing the rms difference between the model and data from 16 cm to 2 cm. This improvement of the mean state is accompanied by significant improvement of temporal variability in our analysis. However, it remains suboptimal, showing a tendency in the forecast phase of returning toward a free run without data assimilation. Both the mean difference and standard deviation of the difference between the forecast and observation data are reduced as the result of assimilation.

  4. Assimilation of satellite color observations in a coupled ocean GCM-ecosystem model

    Science.gov (United States)

    Sarmiento, Jorge L.

    1992-01-01

    Monthly average coastal zone color scanner (CZCS) estimates of chlorophyll concentration were assimilated into an ocean global circulation model(GCM) containing a simple model of the pelagic ecosystem. The assimilation was performed in the simplest possible manner, to allow the assessment of whether there were major problems with the ecosystem model or with the assimilation procedure. The current ecosystem model performed well in some regions, but failed in others to assimilate chlorophyll estimates without disrupting important ecosystem properties. This experiment gave insight into those properties of the ecosystem model that must be changed to allow data assimilation to be generally successful, while raising other important issues about the assimilation procedure.

  5. Observing the Global Water Cycle from Space

    Science.gov (United States)

    Hildebrand, P. H.

    2004-01-01

    This paper presents an approach to measuring all major components of the water cycle from space. Key elements of the global water cycle are discussed in terms of the storage of water-in the ocean, air, cloud and precipitation, in soil, ground water, snow and ice, and in lakes and rivers, and in terms of the global fluxes of water between these reservoirs. Approaches to measuring or otherwise evaluating the global water cycle are presented, and the limitations on known accuracy for many components of the water cycle are discussed, as are the characteristic spatial and temporal scales of the different water cycle components. Using these observational requirements for a global water cycle observing system, an approach to measuring the global water cycle from space is developed. The capabilities of various active and passive microwave instruments are discussed, as is the potential of supporting measurements from other sources. Examples of space observational systems, including TRMM/GPM precipitation measurement, cloud radars, soil moisture, sea surface salinity, temperature and humidity profiling, other measurement approaches and assimilation of the microwave and other data into interpretative computer models are discussed to develop the observational possibilities. The selection of orbits is then addressed, for orbit selection and antenna size/beamwidth considerations determine the sampling characteristics for satellite measurement systems. These considerations dictate a particular set of measurement possibilities, which are then matched to the observational sampling requirements based on the science. The results define a network of satellite instrumentation systems, many in low Earth orbit, a few in geostationary orbit, and all tied together through a sampling network that feeds the observations into a data-assimilative computer model.

  6. Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics

    Science.gov (United States)

    Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

    2018-01-01

    The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

  7. Global Three-Dimensional Ionospheric Data Assimilation Model Using Ground-based GPS and Radio Occultation Total Electron Content

    Science.gov (United States)

    Jann-Yenq Liu, Tiger; Lin, Chi-Yen; Matsuo, Tomoko; Lin, Charles C. H.; Tsai, Ho-Fang; Chen, Chao-Yen

    2017-04-01

    An ionospheric data assimilation approach presented here is based on the Gauss-Markov Kalman filter with International Reference Ionosphere (IRI) as the background model and designed to assimilate the total electron content (TEC) observed from ground-based GPS receivers and space-based radio occultation (RO) of FORMOSAT-3/COSMIC (F3/C) or FORMOSAT-7/COSMIC-2 (F7/C2). The Kalman filter consists of the forecast step according to Gauss-Markov process and measurement update step. Observing System Simulation Experiments (OSSEs) show that the Gauss-Markov Kalman filter procedure can increase the accuracy of the data assimilation analysis over the procedure consisting of the measurement update step alone. Moreover, in comparing to F3/C, the dense F7/C2 RO observation can further increase the model accuracy significantly. Validating the data assimilation results with the vertical TEC in Global Ionosphere Maps and that derived from ground-based GPS measurements, as well as the ionospheric F2-peak height and electron density sounded by ionosondes is also carried out. Both the OSSE results and the observation validations confirm that the developed data assimilation model can be used to reconstruct the three-dimensional electron density in the ionosphere satisfactorily.

  8. An Observing System Simulation Experiment of assimilating leaf area index and soil moisture over cropland

    Science.gov (United States)

    Lafont, Sebastien; Barbu, Alina; Calvet, Jean-Christophe

    2013-04-01

    A Land Data Assimilation System (LDAS) is an off-line data assimilation system featuring uncoupled land surface model which is driven by observation-based atmospheric forcing. In this study the experiments were conducted with a surface externalized (SURFEX) modelling platform developed at Météo-France. It encompasses the land surface model ISBA-A-gs that simulates photosynthesis and plant growth. The photosynthetic activity depends on the vegetation types. The input soil and vegetation parameters are provided by the ECOCLIMAP II global database which assigns the ecosystem classes in several plant functional types as grassland, crops, deciduous forest and coniferous forest. New versions of the model have been recently developed in order to better describe the agricultural plant functional types. We present a set of observing system simulation experiments (OSSE) which asses leaf area index (LAI) and soil moisture assimilation for improving the land surface estimates in a controlled synthetic environment. Synthetic data were assimilated into ISBA-A-gs using an Extended Kalman Filter (EKF). This allows for an understanding of model responses to an augmentation of the number of crop types and different parameters associated to this modification. In addition, the interactions between uncertainties in the model and in the observations were investigated. This study represents the first step of a process that envisages the extension of LDAS to the new versions of the ISBA-A-gs model in order to assimilate remote sensing observations.

  9. A preliminary experiment for the long-term regional reanalysis over Japan assimilating conventional observations with NHM-LETKF

    Science.gov (United States)

    Fukui, Shin; Iwasaki, Toshiki; Saito, Kazuo; Seko, Hiromu; Kunii, Masaru

    2016-04-01

    Several long-term global reanalyses have been produced by major operational centres and have contributed to the advance of weather and climate researches considerably. Although the horizontal resolutions of these global reanalyses are getting higher partly due to the development of computing technology, they are still too coarse to reproduce local circulations and precipitation realistically. To solve this problem, dynamical downscaling is often employed. However, the forcing from lateral boundaries only cannot necessarily control the inner fields especially in long-term dynamical downscaling. Regional reanalysis is expected to overcome the difficulty. To maintain the long-term consistency of the analysis quality, it is better to assimilate only the conventional observations that are available in long period. To confirm the effectiveness of the regional reanalysis, some assimilation experiments are performed. In the experiments, only conventional observations (SYNOP, SHIP, BUOY, TEMP, PILOT, TC-Bogus) are assimilated with the NHM-LETKF system, which consists of the nonhydrostatic model (NHM) of the Japan Meteorological Agency (JMA) and the local ensemble transform Kalman filter (LETKF). The horizontal resolution is 25 km and the domain covers Japan and its surroundings. Japanese 55-year reanalysis (JRA-55) is adopted as the initial and lateral boundary conditions for the NHM-LETKF forecast-analysis cycles. The ensemble size is 10. The experimental period is August 2014 as a representative of warm season for the region. The results are verified against the JMA's operational Meso-scale Analysis, which is produced with assimilating observation data including various remote sensing observations using a 4D-Var scheme, and compared with those of the simple dynamical downscaling experiment without data assimilation. Effects of implementation of lateral boundary perturbations derived from an EOF analysis of JRA-55 over the targeted domain are also examined. The comparison

  10. Deriving Global Discharge Records from SWOT Observations

    Science.gov (United States)

    Pan, M.; Fisher, C. K.; Wood, E. F.

    2017-12-01

    River flows are poorly monitored in many regions of the world, hindering our ability to accurately estimate water global water usage, and thus estimate global water and energy budgets or the variability in the global water cycle. Recent developments in satellite remote sensing, such as water surface elevations from radar altimetry or surface water extents from visible/infrared imagery, aim to fill this void; however, the streamflow estimates derived from these are inherently intermittent in both space and time. There is then a need for new methods that are able to derive spatially and temporally continuous records of discharge from the many available data sources. One particular application of this will be the Surface Water and Ocean Topography (SWOT) mission, which is designed to provide global observations of water surface elevation and slope from which river discharge can be estimated. Within the 21-day repeat cycle, a river reach will be observed 2-4 times on average. Due to the relationship between the basin orientation and the orbit, these observations are not evenly distributed in time or space. In this study, we investigate how SWOT will observe global river basins and how the temporal and spatial sampling impacts our ability to reconstruct discharge records.River flows can be estimated throughout a basin by assimilating SWOT observations using the Inverse Streamflow Routing (ISR) model of Pan and Wood [2013]. This method is applied to 32 global basins with different geometries and crossing patterns for the future orbit, assimilating theoretical SWOT-retrieved "gauges". Results show that the model is able to reconstruct basin-wide discharge from SWOT observations alone; however, the performance varies significantly across basins and is driven by the orientation, flow distance, and travel time in each, as well as the sensitivity of the reconstruction method to errors in the satellite retrieval. These properties are combined to estimate the "observability" of

  11. Coupled atmosphere and land-surface assimilation of surface observations with a single column model and ensemble data assimilation

    Science.gov (United States)

    Rostkier-Edelstein, Dorita; Hacker, Joshua P.; Snyder, Chris

    2014-05-01

    Numerical weather prediction and data assimilation models are composed of coupled atmosphere and land-surface (LS) components. If possible, the assimilation procedure should be coupled so that observed information in one module is used to correct fields in the coupled module. There have been some attempts in this direction using optimal interpolation, nudging and 2/3DVAR data assimilation techniques. Aside from satellite remote sensed observations, reference height in-situ observations of temperature and moisture have been used in these studies. Among other problems, difficulties in coupled atmosphere and LS assimilation arise as a result of the different time scales characteristic of each component and the unsteady correlation between these components under varying flow conditions. Ensemble data-assimilation techniques rely on flow dependent observations-model covariances. Provided that correlations and covariances between land and atmosphere can be adequately simulated and sampled, ensemble data assimilation should enable appropriate assimilation of observations simultaneously into the atmospheric and LS states. Our aim is to explore assimilation of reference height in-situ temperature and moisture observations into the coupled atmosphere-LS modules(simultaneously) in NCAR's WRF-ARW model using the NCAR's DART ensemble data-assimilation system. Observing system simulation experiments (OSSEs) are performed using the single column model (SCM) version of WRF. Numerical experiments during a warm season are centered on an atmospheric and soil column in the South Great Plains. Synthetic observations are derived from "truth" WRF-SCM runs for a given date,initialized and forced using North American Regional Reanalyses (NARR). WRF-SCM atmospheric and LS ensembles are created by mixing the atmospheric and soil NARR profile centered on a given date with that from another day (randomly chosen from the same season) with weights drawn from a logit-normal distribution. Three

  12. Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

    Science.gov (United States)

    Mazzoleni, Maurizio; Alfonso, Leonardo; Chacon-Hurtado, Juan; Solomatine, Dimitri

    2015-09-01

    Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

  13. Patterns and Variability in Global Ocean Chlorophyll: Satellite Observations and Modeling

    Science.gov (United States)

    Gregg, Watson

    2004-01-01

    Recent analyses of SeaWiFS data have shown that global ocean chlorophyll has increased more than 4% since 1998. The North Pacific ocean basin has increased nearly 19%. These trend analyses follow earlier results showing decadal declines in global ocean chlorophyll and primary production. To understand the causes of these changes and trends we have applied the newly developed NASA Ocean Biogeochemical Assimilation Model (OBAM), which is driven in mechanistic fashion by surface winds, sea surface temperature, atmospheric iron deposition, sea ice, and surface irradiance. The model utilizes chlorophyll from SeaWiFS in a daily assimilation. The model has in place many of the climatic variables that can be expected to produce the changes observed in SeaWiFS data. This enables us to diagnose the model performance, the assimilation performance, and possible causes for the increase in chlorophyll. A full discussion of the changes and trends, possible causes, modeling approaches, and data assimilation will be the focus of the seminar.

  14. A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

    Directory of Open Access Journals (Sweden)

    S. Kolberg

    2006-01-01

    Full Text Available A method for assimilating remotely sensed snow covered area (SCA into the snow subroutine of a grid distributed precipitation-runoff model (PRM is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC, which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E, based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started

  15. Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

    Science.gov (United States)

    Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

    2012-01-01

    The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters

  16. Global heating distributions for January 1979 calculated from GLA assimilated and simulated model-based datasets

    Science.gov (United States)

    Schaack, Todd K.; Lenzen, Allen J.; Johnson, Donald R.

    1991-01-01

    This study surveys the large-scale distribution of heating for January 1979 obtained from five sources of information. Through intercomparison of these distributions, with emphasis on satellite-derived information, an investigation is conducted into the global distribution of atmospheric heating and the impact of observations on the diagnostic estimates of heating derived from assimilated datasets. The results indicate a substantial impact of satellite information on diagnostic estimates of heating in regions where there is a scarcity of conventional observations. The addition of satellite data provides information on the atmosphere's temperature and wind structure that is important for estimation of the global distribution of heating and energy exchange.

  17. Evaluation of tropical Pacific observing systems using NCEP and GFDL ocean data assimilation systems

    Science.gov (United States)

    Xue, Yan; Wen, Caihong; Yang, Xiaosong; Behringer, David; Kumar, Arun; Vecchi, Gabriel; Rosati, Anthony; Gudgel, Rich

    2017-08-01

    The TAO/TRITON array is the cornerstone of the tropical Pacific and ENSO observing system. Motivated by the recent rapid decline of the TAO/TRITON array, the potential utility of TAO/TRITON was assessed for ENSO monitoring and prediction. The analysis focused on the period when observations from Argo floats were also available. We coordinated observing system experiments (OSEs) using the global ocean data assimilation system (GODAS) from the National Centers for Environmental Prediction and the ensemble coupled data assimilation (ECDA) from the Geophysical Fluid Dynamics Laboratory for the period 2004-2011. Four OSE simulations were conducted with inclusion of different subsets of in situ profiles: all profiles (XBT, moorings, Argo), all except the moorings, all except the Argo and no profiles. For evaluation of the OSE simulations, we examined the mean bias, standard deviation difference, root-mean-square difference (RMSD) and anomaly correlation against observations and objective analyses. Without assimilation of in situ observations, both GODAS and ECDA had large mean biases and RMSD in all variables. Assimilation of all in situ data significantly reduced mean biases and RMSD in all variables except zonal current at the equator. For GODAS, the mooring data is critical in constraining temperature in the eastern and northwestern tropical Pacific, while for ECDA both the mooring and Argo data is needed in constraining temperature in the western tropical Pacific. The Argo data is critical in constraining temperature in off-equatorial regions for both GODAS and ECDA. For constraining salinity, sea surface height and surface current analysis, the influence of Argo data was more pronounced. In addition, the salinity data from the TRITON buoys played an important role in constraining salinity in the western Pacific. GODAS was more sensitive to withholding Argo data in off-equatorial regions than ECDA because it relied on local observations to correct model biases and

  18. The Impact of Prior Biosphere Models in the Inversion of Global Terrestrial CO2 Fluxes by Assimilating OCO-2 Retrievals

    Science.gov (United States)

    Philip, Sajeev; Johnson, Matthew S.

    2018-01-01

    Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emissions and biospheric fluxes. The processes controlling terrestrial biosphere-atmosphere carbon exchange are currently not fully understood, resulting in terrestrial biospheric models having significant differences in the quantification of biospheric CO2 fluxes. Atmospheric transport models assimilating measured (in situ or space-borne) CO2 concentrations to estimate "top-down" fluxes, generally use these biospheric CO2 fluxes as a priori information. Most of the flux inversion estimates result in substantially different spatio-temporal posteriori estimates of regional and global biospheric CO2 fluxes. The Orbiting Carbon Observatory 2 (OCO-2) satellite mission dedicated to accurately measure column CO2 (XCO2) allows for an improved understanding of global biospheric CO2 fluxes. OCO-2 provides much-needed CO2 observations in data-limited regions facilitating better global and regional estimates of "top-down" CO2 fluxes through inversion model simulations. The specific objectives of our research are to: 1) conduct GEOS-Chem 4D-Var assimilation of OCO-2 observations, using several state-of-the-science biospheric CO2 flux models as a priori information, to better constrain terrestrial CO2 fluxes, and 2) quantify the impact of different biospheric model prior fluxes on OCO-2-assimilated a posteriori CO2 flux estimates. Here we present our assessment of the importance of these a priori fluxes by conducting Observing System Simulation Experiments (OSSE) using simulated OCO-2 observations with known "true" fluxes.

  19. Application of data assimilation methods for analysis and integration of observed and modeled Arctic Sea ice motions

    Science.gov (United States)

    Meier, Walter Neil

    This thesis demonstrates the applicability of data assimilation methods to improve observed and modeled ice motion fields and to demonstrate the effects of assimilated motion on Arctic processes important to the global climate and of practical concern to human activities. Ice motions derived from 85 GHz and 37 GHz SSM/I imagery and estimated from two-dimensional dynamic-thermodynamic sea ice models are compared to buoy observations. Mean error, error standard deviation, and correlation with buoys are computed for the model domain. SSM/I motions generally have a lower bias, but higher error standard deviations and lower correlation with buoys than model motions. There are notable variations in the statistics depending on the region of the Arctic, season, and ice characteristics. Assimilation methods are investigated and blending and optimal interpolation strategies are implemented. Blending assimilation improves error statistics slightly, but the effect of the assimilation is reduced due to noise in the SSM/I motions and is thus not an effective method to improve ice motion estimates. However, optimal interpolation assimilation reduces motion errors by 25--30% over modeled motions and 40--45% over SSM/I motions. Optimal interpolation assimilation is beneficial in all regions, seasons and ice conditions, and is particularly effective in regimes where modeled and SSM/I errors are high. Assimilation alters annual average motion fields. Modeled ice products of ice thickness, ice divergence, Fram Strait ice volume export, transport across the Arctic and interannual basin averages are also influenced by assimilated motions. Assimilation improves estimates of pollutant transport and corrects synoptic-scale errors in the motion fields caused by incorrect forcings or errors in model physics. The portability of the optimal interpolation assimilation method is demonstrated by implementing the strategy in an ice thickness distribution (ITD) model. This research presents an

  20. On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state

    Science.gov (United States)

    Androsov, Alexey; Nerger, Lars; Schnur, Reiner; Schröter, Jens; Albertella, Alberta; Rummel, Reiner; Savcenko, Roman; Bosch, Wolfgang; Skachko, Sergey; Danilov, Sergey

    2018-05-01

    General ocean circulation models are not perfect. Forced with observed atmospheric fluxes they gradually drift away from measured distributions of temperature and salinity. We suggest data assimilation of absolute dynamical ocean topography (DOT) observed from space geodetic missions as an option to reduce these differences. Sea surface information of DOT is transferred into the deep ocean by defining the analysed ocean state as a weighted average of an ensemble of fully consistent model solutions using an error-subspace ensemble Kalman filter technique. Success of the technique is demonstrated by assimilation into a global configuration of the ocean circulation model FESOM over 1 year. The dynamic ocean topography data are obtained from a combination of multi-satellite altimetry and geoid measurements. The assimilation result is assessed using independent temperature and salinity analysis derived from profiling buoys of the AGRO float data set. The largest impact of the assimilation occurs at the first few analysis steps where both the model ocean topography and the steric height (i.e. temperature and salinity) are improved. The continued data assimilation over 1 year further improves the model state gradually. Deep ocean fields quickly adjust in a sustained manner: A model forecast initialized from the model state estimated by the data assimilation after only 1 month shows that improvements induced by the data assimilation remain in the model state for a long time. Even after 11 months, the modelled ocean topography and temperature fields show smaller errors than the model forecast without any data assimilation.

  1. Development Of A Data Assimilation Capability For RAPID

    Science.gov (United States)

    Emery, C. M.; David, C. H.; Turmon, M.; Hobbs, J.; Allen, G. H.; Famiglietti, J. S.

    2017-12-01

    The global decline of in situ observations associated with the increasing ability to monitor surface water from space motivates the creation of data assimilation algorithms that merge computer models and space-based observations to produce consistent estimates of terrestrial hydrology that fill the spatiotemporal gaps in observations. RAPID is a routing model based on the Muskingum method that is capable of estimating river streamflow over large scales with a relatively short computing time. This model only requires limited inputs: a reach-based river network, and lateral surface and subsurface flow into the rivers. The relatively simple model physics imply that RAPID simulations could be significantly improved by including a data assimilation capability. Here we present the early developments of such data assimilation approach into RAPID. Given the linear and matrix-based structure of the model, we chose to apply a direct Kalman filter, hence allowing for the preservation of high computational speed. We correct the simulated streamflows by assimilating streamflow observations and our early results demonstrate the feasibility of the approach. Additionally, the use of in situ gauges at continental scales motivates the application of our new data assimilation scheme to altimetry measurements from existing (e.g. EnviSat, Jason 2) and upcoming satellite missions (e.g. SWOT), and ultimately apply the scheme globally.

  2. High-Resolution Assimilation of GRACE Terrestrial Water Storage Observations to Represent Local-Scale Water Table Depths

    Science.gov (United States)

    Stampoulis, D.; Reager, J. T., II; David, C. H.; Famiglietti, J. S.; Andreadis, K.

    2017-12-01

    Despite the numerous advances in hydrologic modeling and improvements in Land Surface Models, an accurate representation of the water table depth (WTD) still does not exist. Data assimilation of observations of the joint NASA and DLR mission, Gravity Recovery and Climate Experiment (GRACE) leads to statistically significant improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of water storage. However, the usually shallow groundwater compartment of the models presents a problem with GRACE assimilation techniques, as these satellite observations account for much deeper aquifers. To improve the accuracy of groundwater estimates and allow the representation of the WTD at fine spatial scales we implemented a novel approach that enables a large-scale data integration system to assimilate GRACE data. This was achieved by augmenting the Variable Infiltration Capacity (VIC) hydrologic model, which is the core component of the Regional Hydrologic Extremes Assessment System (RHEAS), a high-resolution modeling framework developed at the Jet Propulsion Laboratory (JPL) for hydrologic modeling and data assimilation. The model has insufficient subsurface characterization and therefore, to reproduce groundwater variability not only in shallow depths but also in deep aquifers, as well as to allow GRACE assimilation, a fourth soil layer of varying depth ( 1000 meters) was added in VIC as the bottom layer. To initialize a water table in the model we used gridded global WTD data at 1 km resolution which were spatially aggregated to match the model's resolution. Simulations were then performed to test the augmented model's ability to capture seasonal and inter-annual trends of groundwater. The 4-layer version of VIC was run with and without assimilating GRACE Total Water Storage anomalies (TWSA) over the Central Valley in California. This is the first-ever assimilation of GRACE TWSA for the determination of realistic water table depths, at

  3. Empowering Geoscience with Improved Data Assimilation Using the Data Assimilation Research Testbed "Manhattan" Release.

    Science.gov (United States)

    Raeder, K.; Hoar, T. J.; Anderson, J. L.; Collins, N.; Hendricks, J.; Kershaw, H.; Ha, S.; Snyder, C.; Skamarock, W. C.; Mizzi, A. P.; Liu, H.; Liu, J.; Pedatella, N. M.; Karspeck, A. R.; Karol, S. I.; Bitz, C. M.; Zhang, Y.

    2017-12-01

    The capabilities of the Data Assimilation Research Testbed (DART) at NCAR have been significantly expanded with the recent "Manhattan" release. DART is an ensemble Kalman filter based suite of tools, which enables researchers to use data assimilation (DA) without first becoming DA experts. Highlights: significant improvement in efficient ensemble DA for very large models on thousands of processors, direct read and write of model state files in parallel, more control of the DA output for finer-grained analysis, new model interfaces which are useful to a variety of geophysical researchers, new observation forward operators and the ability to use precomputed forward operators from the forecast model. The new model interfaces and example applications include the following: MPAS-A; Model for Prediction Across Scales - Atmosphere is a global, nonhydrostatic, variable-resolution mesh atmospheric model, which facilitates multi-scale analysis and forecasting. The absence of distinct subdomains eliminates problems associated with subdomain boundaries. It demonstrates the ability to consistently produce higher-quality analyses than coarse, uniform meshes do. WRF-Chem; Weather Research and Forecasting + (MOZART) Chemistry model assimilates observations from FRAPPÉ (Front Range Air Pollution and Photochemistry Experiment). WACCM-X; Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension assimilates observations of electron density to investigate sudden stratospheric warming. CESM (weakly) coupled assimilation; NCAR's Community Earth System Model is used for assimilation of atmospheric and oceanic observations into their respective components using coupled atmosphere+land+ocean+sea+ice forecasts. CESM2.0; Assimilation in the atmospheric component (CAM, WACCM) of the newly released version is supported. This version contains new and extensively updated components and software environment. CICE; Los Alamos sea ice model (in CESM) is used to assimilate

  4. Land Surface Data Assimilation

    Science.gov (United States)

    Houser, P. R.

    2012-12-01

    Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land

  5. Assimilation of TOPEX/Poseidon altimeter data into a global ocean circulation model: How good are the results?

    Science.gov (United States)

    Fukumori, Ichiro; Raghunath, Ramanujam; Fu, Lee-Lueng; Chao, Yi

    1999-11-01

    The feasibility of assimilating satellite altimetry data into a global ocean general circulation model is studied. Three years of TOPEX/Poseidon data are analyzed using a global, three-dimensional, nonlinear primitive equation model. The assimilation's success is examined by analyzing its consistency and reliability measured by formal error estimates with respect to independent measurements. Improvements in model solution are demonstrated, in particular, properties not directly measured. Comparisons are performed with sea level measured by tide gauges, subsurface temperatures and currents from moorings, and bottom pressure measurements. Model representation errors dictate what can and cannot be resolved by assimilation, and its identification is emphasized.

  6. Numerical simulation of rainfall with assimilation of conventional and GPS observations over north of Iran

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Sharifi

    2016-07-01

    Full Text Available In this work, the effect of assimilation of synoptic, radiosonde and ground-based GPS precipitable water vapor (PWV data has been investigated on the short-term prediction of precipitation, vertical relative humidity and PWV fields over north of Iran. We selected two rainfall events (i.e. February 1, 2014, and September 17, 2014 caused by synoptic systems affecting the southern coasts of the Caspian Sea. These systems are often associated with a shallow and cold high pressure located over Russia that extends towards the southern Caspian Sea. The three dimensional variational (3DVAR data assimilation system of the weather research and forecasting (WRF model is used in two rainfall cases. In each case, three numerical experiments, namely CTRL, CONVDA and GPSCONVDA, are performed. The CTRL experiment uses the global analysis as the initial and boundary conditions of the model. In the second experiment, surface and radiosonde observations are inserted into the model. Finally, the GPSCONVDA experiment uses the GPS PWV data in the assimilation process in addition to the conventional observations. It is found that in CONVDA experiment, the mean absolute error (MAE of the accumulated precipitation is reduced about 5 and 13 percent in 24h model simulation of February and September cases, respectively, when compared to CTRL. Also, the results in both cases suggest that the assimilation of GPS data has the greatest impact on model PWV simulations, with maximum root mean squares error (RMSE reduction of 0.7 mm. In the GPSCONVDA experiment, comparison of the vertical profiles of 12h simulated relative humidity with the corresponding radiosonde observations shows a slight improvement in the lower levels.

  7. Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization.

    Directory of Open Access Journals (Sweden)

    Xujun Han

    Full Text Available The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL; the other is observation localization (OL. Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.

  8. Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization.

    Science.gov (United States)

    Han, Xujun; Li, Xin; Rigon, Riccardo; Jin, Rui; Endrizzi, Stefano

    2015-01-01

    The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.

  9. Bio-Optical Data Assimilation With Observational Error Covariance Derived From an Ensemble of Satellite Images

    Science.gov (United States)

    Shulman, Igor; Gould, Richard W.; Frolov, Sergey; McCarthy, Sean; Penta, Brad; Anderson, Stephanie; Sakalaukus, Peter

    2018-03-01

    An ensemble-based approach to specify observational error covariance in the data assimilation of satellite bio-optical properties is proposed. The observational error covariance is derived from statistical properties of the generated ensemble of satellite MODIS-Aqua chlorophyll (Chl) images. The proposed observational error covariance is used in the Optimal Interpolation scheme for the assimilation of MODIS-Aqua Chl observations. The forecast error covariance is specified in the subspace of the multivariate (bio-optical, physical) empirical orthogonal functions (EOFs) estimated from a month-long model run. The assimilation of surface MODIS-Aqua Chl improved surface and subsurface model Chl predictions. Comparisons with surface and subsurface water samples demonstrate that data assimilation run with the proposed observational error covariance has higher RMSE than the data assimilation run with "optimistic" assumption about observational errors (10% of the ensemble mean), but has smaller or comparable RMSE than data assimilation run with an assumption that observational errors equal to 35% of the ensemble mean (the target error for satellite data product for chlorophyll). Also, with the assimilation of the MODIS-Aqua Chl data, the RMSE between observed and model-predicted fractions of diatoms to the total phytoplankton is reduced by a factor of two in comparison to the nonassimilative run.

  10. Description of Atmospheric Conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

    Energy Technology Data Exchange (ETDEWEB)

    Abreu, P.; /Lisbon, IST; Aglietta, M.; /Turin U. /INFN, Turin; Ahlers, M.; /Wisconsin U., Madison; Ahn, E.J.; /Fermilab; Albuquerque, I.F.M.; /Sao Paulo U.; Allard, D.; /APC, Paris; Allekotte, I.; /Buenos Aires, CONICET; Allen, J.; /New York U.; Allison, P.; /Ohio State U.; Almela, A.; /Natl. Tech. U., San Nicolas /Buenos Aires, CONICET; Alvarez Castillo, J.; /Mexico U., ICN /Santiago de Compostela U.

    2012-01-01

    Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargue and averaged monthly models, the utility of the GDAS data is shown.

  11. Results of the Simulation and Assimilation of Doppler Wind Lidar Observations in Preparation for European Space Agency's Aeolus Mission

    Science.gov (United States)

    McCarty, Will

    2011-01-01

    With the launch of the European Space Agency's Aeolus Mission in 2013, direct spaceborne measurements of vertical wind profiles are imminent via Doppler wind lidar technology. Part of the preparedness for such missions is the development of the proper data assimilation methodology for handling such observations. Since no heritage measurements exist in space, the Joint Observing System Simulation Experiment (Joint OSSE) framework has been utilized to generate a realistic proxy dataset as a precursor to flight. These data are being used for the development of the Gridpoint Statistical Interpolation (GSI) data assimilation system utilized at a number of centers through the United States including the Global Modeling and Assimilation Office (GMAO) at NASA/Goddard Space Flight Center and at the National Centers for Environmental Prediction (NOAA/NWS/NCEP) as an activity through the Joint Center for Satellite Data Assimilation. An update of this ongoing effort will be presented, including the methodology of proxy data generation, the limitations of the proxy data, the handling of line-of-sight wind measurements within the GSI, and the impact on both analyses and forecasts with the addition of the new data type.

  12. Simultaneous assimilation of ozone profiles from multiple UV-VIS satellite instruments

    Science.gov (United States)

    van Peet, Jacob C. A.; van der A, Ronald J.; Kelder, Hennie M.; Levelt, Pieternel F.

    2018-02-01

    A three-dimensional global ozone distribution has been derived from assimilation of ozone profiles that were observed by satellites. By simultaneous assimilation of ozone profiles retrieved from the nadir looking satellite instruments Global Ozone Monitoring Experiment 2 (GOME-2) and Ozone Monitoring Instrument (OMI), which measure the atmosphere at different times of the day, the quality of the derived atmospheric ozone field has been improved. The assimilation is using an extended Kalman filter in which chemical transport model TM5 has been used for the forecast. The combined assimilation of both GOME-2 and OMI improves upon the assimilation results of a single sensor. The new assimilation system has been demonstrated by processing 4 years of data from 2008 to 2011. Validation of the assimilation output by comparison with sondes shows that biases vary between -5 and +10 % between the surface and 100 hPa. The biases for the combined assimilation vary between -3 and +3 % in the region between 100 and 10 hPa where GOME-2 and OMI are most sensitive. This is a strong improvement compared to direct retrievals of ozone profiles from satellite observations.

  13. Assimilation of lake water surface temperature observations using an extended Kalman filter

    Directory of Open Access Journals (Sweden)

    Ekaterina Kourzeneva

    2014-10-01

    Full Text Available A new extended Kalman filter (EKF-based algorithm to assimilate lake water surface temperature (LWST observations into the lake model/parameterisation scheme Freshwater Lake (FLake has been developed. The data assimilation algorithm has been implemented into the stand-alone offline version of FLake. The mixed and non-mixed regimes in lakes are treated separately by the EKF algorithm. The timing of the ice period is indicated implicitly: no ice if water surface temperature is measured. Numerical experiments are performed using operational in-situ observations for 27 lakes and merged observations (in-situ plus satellite for 4 lakes in Finland. Experiments are analysed, potential problems are discussed, and the role of early spring observations is studied. In general, results of experiments are promising: (1 the impact of observations (calculated as the normalised reduction of the LWST root mean square error comparing to the free model run is more than 90% and (2 in cross-validation (when observations are partly assimilated, partly used for validation the normalised reduction of the LWST error standard deviation is more than 65%. The new data assimilation algorithm will allow prognostic variables in the lake parameterisation scheme to be initialised in operational numerical weather prediction models and the effects of model errors to be corrected by using LWST observations.

  14. Chemical Source Inversion using Assimilated Constituent Observations in an Idealized Two-dimensional System

    Science.gov (United States)

    Tangborn, Andrew; Cooper, Robert; Pawson, Steven; Sun, Zhibin

    2009-01-01

    We present a source inversion technique for chemical constituents that uses assimilated constituent observations rather than directly using the observations. The method is tested with a simple model problem, which is a two-dimensional Fourier-Galerkin transport model combined with a Kalman filter for data assimilation. Inversion is carried out using a Green's function method and observations are simulated from a true state with added Gaussian noise. The forecast state uses the same spectral spectral model, but differs by an unbiased Gaussian model error, and emissions models with constant errors. The numerical experiments employ both simulated in situ and satellite observation networks. Source inversion was carried out by either direct use of synthetically generated observations with added noise, or by first assimilating the observations and using the analyses to extract observations. We have conducted 20 identical twin experiments for each set of source and observation configurations, and find that in the limiting cases of a very few localized observations, or an extremely large observation network there is little advantage to carrying out assimilation first. However, in intermediate observation densities, there decreases in source inversion error standard deviation using the Kalman filter algorithm followed by Green's function inversion by 50% to 95%.

  15. Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

    Science.gov (United States)

    Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

    2018-01-01

    Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

  16. Data Assimilation of AirSWOT and Synthetically Derived SWOT Observations of Water Surface Elevation in a Multichannel River

    Science.gov (United States)

    Altenau, E. H.; Pavelsky, T.; Andreadis, K.; Bates, P. D.; Neal, J. C.

    2017-12-01

    Multichannel rivers continue to be challenging features to quantify, especially at regional and global scales, which is problematic because accurate representations of such environments are needed to properly monitor the earth's water cycle as it adjusts to climate change. It has been demonstrated that higher-complexity, 2D models outperform lower-complexity, 1D models in simulating multichannel river hydraulics at regional scales due to the inclusion of the channel network's connectivity. However, new remote sensing measurements from the future Surface Water and Ocean Topography (SWOT) mission and it's airborne analog AirSWOT offer new observations that can be used to try and improve the lower-complexity, 1D models to achieve accuracies closer to the higher-complexity, 2D codes. Here, we use an Ensemble Kalman Filter (EnKF) to assimilate AirSWOT water surface elevation (WSE) measurements from a 2015 field campaign into a 1D hydrodynamic model along a 90 km reach of Tanana River, AK. This work is the first to test data assimilation methods using real SWOT-like data from AirSWOT. Additionally, synthetic SWOT observations of WSE are generated across the same study site using a fine-resolution 2D model and assimilated into the coarser-resolution 1D model. Lastly, we compare the abilities of AirSWOT and the synthetic-SWOT observations to improve spatial and temporal model outputs in WSEs. Results indicate 1D model outputs of spatially distributed WSEs improve as observational coverage increases, and improvements in temporal fluctuations in WSEs depend on the number of observations. Furthermore, results reveal that assimilation of AirSWOT observations produce greater error reductions in 1D model outputs compared to synthetic SWOT observations due to lower measurement errors. Both AirSWOT and the synthetic SWOT observations significantly lower spatial and temporal errors in 1D model outputs of WSEs.

  17. Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists

    Science.gov (United States)

    Crumley, R. L.; Hill, D. F.; Arendt, A. A.; Wikstrom Jones, K.; Wolken, G. J.; Setiawan, L.

    2017-12-01

    Modeling seasonal snow pack in alpine environments includes a multiplicity of challenges caused by a lack of spatially extensive and temporally continuous observational datasets. This is partially due to the difficulty of collecting measurements in harsh, remote environments where extreme gradients in topography exist, accompanied by large model domains and inclement weather. Engaging snow enthusiasts, snow professionals, and community members to participate in the process of data collection may address some of these challenges. In this study, we use SnowModel to estimate seasonal snow water equivalence (SWE) in the Thompson Pass region of Alaska while incorporating snow depth measurements collected by citizen scientists. We develop a modeling approach to assimilate hundreds of snow depth measurements from participants in the Community Snow Observations (CSO) project (www.communitysnowobs.org). The CSO project includes a mobile application where participants record and submit geo-located snow depth measurements while working and recreating in the study area. These snow depth measurements are randomly located within the model grid at irregular time intervals over the span of four months in the 2017 water year. This snow depth observation dataset is converted into a SWE dataset by employing an empirically-based, bulk density and SWE estimation method. We then assimilate this data using SnowAssim, a sub-model within SnowModel, to constrain the SWE output by the observed data. Multiple model runs are designed to represent an array of output scenarios during the assimilation process. An effort to present model output uncertainties is included, as well as quantification of the pre- and post-assimilation divergence in modeled SWE. Early results reveal pre-assimilation SWE estimations are consistently greater than the post-assimilation estimations, and the magnitude of divergence increases throughout the snow pack evolution period. This research has implications beyond the

  18. A Study on the Impact of Observation Assimilation on the Numerical Simulation of Tropical Cyclones JAL and THANE Using 3DVAR

    KAUST Repository

    Viswanadhapalli, Yesubabu

    2013-12-08

    In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region of the Indian Ocean using a three-dimensional variational data assimilation (3DVAR) technique. The Weather Research and Forecasting (WRF)-Advanced Research WRF (ARW) mesoscale model is used to simulate the severe cyclone JAL: 5–8 November 2010 and the very severe cyclone THANE: 27–30 December 2011 with a double nested domain configuration and with a horizontal resolution of 27 × 9 km. Five numerical experiments are conducted for each cyclone. In the control run (CTL) the National Centers for Environmental Prediction global forecast system analysis and forecasts available at 50 km resolution were used for the initial and boundary conditions. In the second (VARAWS), third (VARSCAT), fourth (VARMODIS) and fifth (VARALL) experiments, the conventional surface observations, Oceansat-2 ocean surface wind vectors, temperature and humidity profiles of MODIS, and all observations were respectively used for assimilation. Results indicate meager impact with surface observations, and relatively higher impact with scatterometer wind data in the case of the JAL cyclone, and with MODIS temperature and humidity profiles in the case of THANE for the simulation of intensity and track parameters. These relative impacts are related to the area coverage of scatterometer winds and MODIS profiles in the respective storms, and are confirmed by the overall better results obtained with assimilation of all observations in both the cases. The improvements in track prediction are mainly contributed by the assimilation of scatterometer wind vector data, which reduced errors in the initial position and size of the cyclone vortices. The errors are reduced by 25, 21, 38 % in vector track position, and by 57, 36, 39 % in intensity, at 24, 48, 72

  19. Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

    Science.gov (United States)

    da Silva, Arlindo

    2010-01-01

    A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of

  20. Global Soil Moisture Estimation from L-Band Satellite Data: The Impact of Radiative Transfer Modeling in Assimilation and Retrieval Systems

    Science.gov (United States)

    De Lannoy, Gabrielle; Reichle, Rolf; Gruber, Alexander; Bechtold, Michel; Quets, Jan; Vrugt, Jasper; Wigneron, Jean-Pierre

    2018-01-01

    The SMOS and SMAP missions have collected a wealth of global L-band Brightness temperature (Tb) observations. The retrieval of surface Soil moisture estimates, and the estimation of other geophysical Variables, such as root-zone soil moisture and temperature, via data Assimilation into land surface models largely depends on accurate Radiative transfer modeling (RTM). This presentation will focus on various configuration aspects of the RTM (i) for the inversion of SMOS Tb to surface soil moisture, and (ii) for the forward modeling as part of a SMOS Tb data assimilation System to estimate a consistent set of geophysical land surface Variables, using the GEOS-5 Catchment Land Surface Model.

  1. Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

    Science.gov (United States)

    Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

    2007-12-01

    Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

  2. The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001-2015

    Science.gov (United States)

    van der Laan-Luijkx, Ingrid T.; van der Velde, Ivar R.; van der Veen, Emma; Tsuruta, Aki; Stanislawska, Karolina; Babenhauserheide, Arne; Zhang, Hui Fang; Liu, Yu; He, Wei; Chen, Huilin; Masarie, Kenneth A.; Krol, Maarten C.; Peters, Wouter

    2017-07-01

    Data assimilation systems are used increasingly to constrain the budgets of reactive and long-lived gases measured in the atmosphere. Each trace gas has its own lifetime, dominant sources and sinks, and observational network (from flask sampling and in situ measurements to space-based remote sensing) and therefore comes with its own optimal configuration of the data assimilation. The CarbonTracker Europe data assimilation system for CO2 estimates global carbon sources and sinks, and updates are released annually and used in carbon cycle studies. CarbonTracker Europe simulations are performed using the new modular implementation of the data assimilation system: the CarbonTracker Data Assimilation Shell (CTDAS). Here, we present and document this redesign of the data assimilation code that forms the heart of CarbonTracker, specifically meant to enable easy extension and modification of the data assimilation system. This paper also presents the setup of the latest version of CarbonTracker Europe (CTE2016), including the use of the gridded state vector, and shows the resulting carbon flux estimates. We present the distribution of the carbon sinks over the hemispheres and between the land biosphere and the oceans. We show that with equal fossil fuel emissions, 2015 has a higher atmospheric CO2 growth rate compared to 2014, due to reduced net land carbon uptake in later year. The European carbon sink is especially present in the forests, and the average net uptake over 2001-2015 was 0. 17 ± 0. 11 PgC yr-1 with reductions to zero during drought years. Finally, we also demonstrate the versatility of CTDAS by presenting an overview of the wide range of applications for which it has been used so far.

  3. The impact of different background errors in the assimilation of satellite radiances and in-situ observational data using WRFDA for three rainfall events over Iran

    Science.gov (United States)

    Zakeri, Zeinab; Azadi, Majid; Ghader, Sarmad

    2018-01-01

    Satellite radiances and in-situ observations are assimilated through Weather Research and Forecasting Data Assimilation (WRFDA) system into Advanced Research WRF (ARW) model over Iran and its neighboring area. Domain specific background error based on x and y components of wind speed (UV) control variables is calculated for WRFDA system and some sensitivity experiments are carried out to compare the impact of global background error and the domain specific background errors, both on the precipitation and 2-m temperature forecasts over Iran. Three precipitation events that occurred over the country during January, September and October 2014 are simulated in three different experiments and the results for precipitation and 2-m temperature are verified against the verifying surface observations. Results show that using domain specific background error improves 2-m temperature and 24-h accumulated precipitation forecasts consistently, while global background error may even degrade the forecasts compared to the experiments without data assimilation. The improvement in 2-m temperature is more evident during the first forecast hours and decreases significantly as the forecast length increases.

  4. Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration

    Directory of Open Access Journals (Sweden)

    K. Andrea Scott

    2015-09-01

    Full Text Available In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE. These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration, the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors.

  5. Data Assimilation of Lightning using 1D+3D/4D WRF Var Assimilation Schemes with Non-Linear Observation Operators

    Science.gov (United States)

    Navon, M. I.; Stefanescu, R.; Fuelberg, H. E.; Marchand, M.

    2012-12-01

    NASA's launch of the GOES-R Lightning Mapper (GLM) in 2015 will provide continuous, full disc, high resolution total lightning (IC + CG) data. The data will be available at a horizontal resolution of approximately 9 km. Compared to other types of data, the assimilation of lightning data into operational numerical models has received relatively little attention. Previous efforts of lightning assimilation mostly have employed nudging. This paper will describe the implementation of 1D+3D/4D Var assimilation schemes of existing ground-based WTLN (Worldwide Total Lightning Network) lightning observations using non-linear observation operators in the incremental WRFDA system. To mimic the expected output of GLM, the WTLN data were used to generate lightning super-observations characterized by flash rates/81 km2/20 min. A major difficulty associated with variational approaches is the complexity of the observation operator that defines the model equivalent of lightning. We use Convective Available Potential Energy (CAPE) as a proxy between lightning data and model variables. This operator is highly nonlinear. Marecal and Mahfouf (2003) have shown that nonlinearities can prevent direct assimilation of rainfall rates in the ECMWF 4D-VAR (using the incremental formulation proposed by Courtier et al. (1994)) from being successful. Using data from the 2011 Tuscaloosa, AL tornado outbreak, we have proved that the direct assimilation of lightning data into the WRF 3D/4D - Var systems is limited due to this incremental approach. Severe threshold limits must be imposed on the innovation vectors to obtain an improved analysis. We have implemented 1D+3D/4D Var schemes to assimilate lightning observations into the WRF model. Their use avoids innovation vector constrains from preventing the inclusion of a greater number of lightning observations Their use also minimizes the problem that nonlinearities in the moist convective scheme can introduce discontinuities in the cost function

  6. The role of ensemble-based statistics in variational assimilation of cloud-affected observations from infrared imagers

    Science.gov (United States)

    Hacker, Joshua; Vandenberghe, Francois; Jung, Byoung-Jo; Snyder, Chris

    2017-04-01

    Effective assimilation of cloud-affected radiance observations from space-borne imagers, with the aim of improving cloud analysis and forecasting, has proven to be difficult. Large observation biases, nonlinear observation operators, and non-Gaussian innovation statistics present many challenges. Ensemble-variational data assimilation (EnVar) systems offer the benefits of flow-dependent background error statistics from an ensemble, and the ability of variational minimization to handle nonlinearity. The specific benefits of ensemble statistics, relative to static background errors more commonly used in variational systems, have not been quantified for the problem of assimilating cloudy radiances. A simple experiment framework is constructed with a regional NWP model and operational variational data assimilation system, to provide the basis understanding the importance of ensemble statistics in cloudy radiance assimilation. Restricting the observations to those corresponding to clouds in the background forecast leads to innovations that are more Gaussian. The number of large innovations is reduced compared to the more general case of all observations, but not eliminated. The Huber norm is investigated to handle the fat tails of the distributions, and allow more observations to be assimilated without the need for strict background checks that eliminate them. Comparing assimilation using only ensemble background error statistics with assimilation using only static background error statistics elucidates the importance of the ensemble statistics. Although the cost functions in both experiments converge to similar values after sufficient outer-loop iterations, the resulting cloud water, ice, and snow content are greater in the ensemble-based analysis. The subsequent forecasts from the ensemble-based analysis also retain more condensed water species, indicating that the local environment is more supportive of clouds. In this presentation we provide details that explain the

  7. Air Quality Activities in the Global Modeling and Assimilation Office

    Science.gov (United States)

    Pawson, Steven

    2016-01-01

    GMAO's mission is to enhance the use of NASA's satellite observations in weather and climate modeling. This presentation will be discussing GMAO's mission, value of data assimilation, and some relevant (available) GMAO data products.

  8. Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

    Science.gov (United States)

    Ji, Lei; Senay, Gabriel B.; Verdin, James P.

    2015-01-01

    There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

  9. Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission

    Directory of Open Access Journals (Sweden)

    A. F. Arellano Jr.

    2007-11-01

    Full Text Available We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3 with simplified chemistry and the Data Assimilation Research Testbed (DART assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B. Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence. The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv. Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

  10. Assimilation of Remotely Sensed Leaf Area Index into the Community Land Model with Explicit Carbon and Nitrogen Components using Data Assimilation Research Testbed

    Science.gov (United States)

    Ling, X.; Fu, C.; Yang, Z. L.; Guo, W.

    2017-12-01

    Information of the spatial and temporal patterns of leaf area index (LAI) is crucial to understand the exchanges of momentum, carbon, energy, and water between the terrestrial ecosystem and the atmosphere, while both in-situ observation and model simulation usually show distinct deficiency in terms of LAI coverage and value. Land data assimilation, combined with observation and simulation together, is a promising way to provide variable estimation. The Data Assimilation Research Testbed (DART) developed and maintained by the National Centre for Atmospheric Research (NCAR) provides a powerful tool to facilitate the combination of assimilation algorithms, models, and real (as well as synthetic) observations to better understanding of all three. Here we systematically investigated the effects of data assimilation on improving LAI simulation based on NCAR Community Land Model with the prognostic carbon-nitrogen option (CLM4CN) linked with DART using the deterministic Ensemble Adjustment Kalman Filter (EAKF). Random 40-member atmospheric forcing was used to drive the CLM4CN with or without LAI assimilation. The Global Land Surface Satellite LAI data (GLASS LAI) LAI is assimilated into the CLM4CN at a frequency of 8 days, and LAI (and leaf carbon / nitrogen) are adjusted at each time step. The results show that assimilating remotely sensed LAI into the CLM4CN is an effective method for improving model performance. In detail, the CLM4-CN simulated LAI systematically overestimates global LAI, especially in low latitude with the largest bias of 5 m2/m2. While if updating both LAI and leaf carbon and leaf nitrogen simultaneously during assimilation, the analyzed LAI can be corrected, especially in low latitude regions with the bias controlled around ±1 m2/m2. Analyzed LAI could also represent the seasonal variation except for the Southern Temperate (23°S-90°S). The obviously improved regions located in the center of Africa, Amazon, the South of Eurasia, the northeast of

  11. Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

    Science.gov (United States)

    Larson, Jay W.

    1998-01-01

    Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

  12. Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

    Science.gov (United States)

    Kwon, H.; Kang, J.-S.; Jo, Y.; Kang, J. H.

    2015-03-01

    The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS package for observation processing (KPOP) system for data assimilation, preprocessing, and quality control modules for bending-angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. The GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending-angle operator, and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research Community Atmosphere Model with Spectral Element dynamical core (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS local ensemble transform Kalman filter (LETKF) data assimilation system, which has been successfully implemented to a cubed-sphere model with unstructured quadrilateral meshes. As a result of data processing, the bending-angle departure statistics between observation and background show significant improvement. Also, the first experiment in assimilating GPS-RO bending angle from KPOP within KIAPS-LETKF shows encouraging results.

  13. Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation

    Directory of Open Access Journals (Sweden)

    V. Shutyaev

    2018-06-01

    Full Text Available The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find unknown parameters of the model. The observation data, and hence the optimal solution, may contain uncertainties. A response function is considered as a functional of the optimal solution after assimilation. Based on the second-order adjoint techniques, the sensitivity of the response function to the observation data is studied. The gradient of the response function is related to the solution of a nonstandard problem involving the coupled system of direct and adjoint equations. The nonstandard problem is studied, based on the Hessian of the original cost function. An algorithm to compute the gradient of the response function with respect to observations is presented. A numerical example is given for the variational data assimilation problem related to sea surface temperature for the Baltic Sea thermodynamics model.

  14. The Global Structure of UTLS Ozone in GEOS-5: A Multi-Year Assimilation of EOS Aura Data

    Science.gov (United States)

    Wargan, Krzysztof; Pawson, Steven; Olsen, Mark A.; Witte, Jacquelyn C.; Douglass, Anne R.; Ziemke, Jerald R.; Strahan, Susan E.; Nielsen, J. Eric

    2015-01-01

    Eight years of ozone measurements retrieved from the Ozone Monitoring Instrument (OMI) and the Microwave Limb Sounder, both on the EOS Aura satellite, have been assimilated into the Goddard Earth Observing System version 5 (GEOS-5) data assimilation system. This study thoroughly evaluates this assimilated product, highlighting its potential for science. The impact of observations on the GEOS-5 system is explored by examining the spatial distribution of the observation-minus-forecast statistics. Independent data are used for product validation. The correlation coefficient of the lower-stratospheric ozone column with ozonesondes is 0.99 and the bias is 0.5%, indicating the success of the assimilation in reproducing the ozone variability in that layer. The upper-tropospheric assimilated ozone column is about 10% lower than the ozonesonde column but the correlation is still high (0.87). The assimilation is shown to realistically capture the sharp cross-tropopause gradient in ozone mixing ratio. Occurrence of transport-driven low ozone laminae in the assimilation system is similar to that obtained from the High Resolution Dynamics Limb Sounder (HIRDLS) above the 400 K potential temperature surface but the assimilation produces fewer laminae than seen by HIRDLS below that surface. Although the assimilation produces 5 - 8 fewer occurrences per day (up to approximately 20%) during the three years of HIRDLS data, the interannual variability is captured correctly. This data-driven assimilated product is complementary to ozone fields generated from chemistry and transport models. Applications include study of the radiative forcing by ozone and tracer transport near the tropopause.

  15. Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

    Science.gov (United States)

    Zaitchik, Benjamin F.; Rodell, Matthew

    2008-01-01

    Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

  16. Impact of GPM Rainrate Data Assimilation on Simulation of Hurricane Harvey (2017)

    Science.gov (United States)

    Li, Xuanli; Srikishen, Jayanthi; Zavodsky, Bradley; Mecikalski, John

    2018-01-01

    Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM was launched in February 2014 with Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) onboard. The GPM has a broad global coverage approximately 70deg S -70deg N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall To develop methodology to assimilate GPM surface precipitation data with Grid-point Statistical Interpolation (GSI) data assimilation system and WRF ARW model To investigate the potential and the value of utilizing GPM observation into NWP for operational environment The GPM rain rate data has been successfully assimilated using the GSI rain data assimilation package. Impacts of rain rate data have been found in temperature and moisture fields of initial conditions. 2.Assimilation of either GPM IMERG or GPROF rain product produces significant improvement in precipitation amount and structure for Hurricane Harvey (2017) forecast. Since IMERG data is available half-hourly, further forecast improvement is expected with continuous assimilation of IMERG data

  17. Screen-level data assimilation of observations and pseudo-observations in COSMO-I2

    Science.gov (United States)

    Milelli, Dr.; Turco, Dr.; Cane, Dr.; Oberto, Dr.; Pelosini, Dr.

    2009-09-01

    general a positive impact during the assimilation cycle and below 1000-1500 m respectively and a neutral impact elsewhere, because the effect of the nudging vanishes a few hours after the end of the assimilation. As a second step, we introduced the assimilation of the 2 m temperature forecasts given by the Multimodel SuperEnsemble technique for all the available stations of the ARPA Piemonte network into the model, as if they were observations (we call them pseudo-observations), from +12h to +24h. The Multimodel SuperEnsemble technique is a powerful post-processing method for the estimation of weather forecast parameters. Several model outputs are combined, using weights calculated during a so-called training period. This technique has already been tested and implemented in many works on limited-area models in order to obtain reliable forecasts in complex orography regions. Also in this case we observe a positive impact mainly on the surface variables, but the effect lasts up to +24h.

  18. A study on assimilating potential vorticity data

    Science.gov (United States)

    Li, Yong; Ménard, Richard; Riishøjgaard, Lars Peter; Cohn, Stephen E.; Rood, Richard B.

    1998-08-01

    The correlation that exists between the potential vorticity (PV) field and the distribution of chemical tracers such as ozone suggests the possibility of using tracer observations as proxy PV data in atmospheric data assimilation systems. Especially in the stratosphere, there are plentiful tracer observations but a general lack of reliable wind observations, and the correlation is most pronounced. The issue investigated in this study is how model dynamics would respond to the assimilation of PV data. First, numerical experiments of identical-twin type were conducted with a simple univariate nuding algorithm and a global shallow water model based on PV and divergence (PV-D model). All model fields are successfully reconstructed through the insertion of complete PV data alone if an appropriate value for the nudging coefficient is used. A simple linear analysis suggests that slow modes are recovered rapidly, at a rate nearly independent of spatial scale. In a more realistic experiment, appropriately scaled total ozone data from the NIMBUS-7 TOMS instrument were assimilated as proxy PV data into the PV-D model over a 10-day period. The resulting model PV field matches the observed total ozone field relatively well on large spatial scales, and the PV, geopotential and divergence fields are dynamically consistent. These results indicate the potential usefulness that tracer observations, as proxy PV data, may offer in a data assimilation system.

  19. Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

    Science.gov (United States)

    Allen, Douglas R.; Hoppel, Karl W.; Kuhl, David D.

    2018-03-01

    Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM) hybrid 4-D variational assimilation (4D-Var) data assimilation (DA) system. Ozone can improve the wind and temperature through two different DA mechanisms: (1) through the flow-of-the-day ensemble background error covariance that is blended together with the static background error covariance and (2) via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE), which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error) global hourly ozone data constrains the stratospheric wind and temperature to within ˜ 2 m s-1 and ˜ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study), rather than 0.0 (no ensemble background error covariance) or 1.0 (no static background error covariance), which is consistent with other hybrid DA studies. When perfect global ozone is

  20. Operational assimilation of ASCAT surface soil wetness at the Met Office

    Directory of Open Access Journals (Sweden)

    I. Dharssi

    2011-08-01

    Full Text Available Currently, no extensive, near real time, global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness from the Advanced Scatterometer (ASCAT on the meteorological operational (MetOp satellite contain good quality information about surface soil moisture. This study describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010.

  1. AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

    Science.gov (United States)

    Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.

    2009-01-01

    Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.

  2. Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

    Science.gov (United States)

    Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.

    2012-01-01

    Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

  3. Data Assimilation by Conditioning of Driving Noise on Future Observations

    KAUST Repository

    Lee, Wonjung

    2014-08-01

    Conventional recursive filtering approaches, designed for quantifying the state of an evolving stochastic dynamical system with intermittent observations, use a sequence of i) an uncertainty propagation step followed by ii) a step where the associated data is assimilated using Bayes\\' rule. Alternatively, the order of the steps can be switched to i) one step ahead data assimilation followed by ii) uncertainty propagation. In this paper, we apply this smoothing-based sequential filter to systems driven by random noise, however with the conditioning on future observation not only to the system variable but to the driving noise. Our research reveals that, for the nonlinear filtering problem, the conditioned driving noise is biased by a nonzero mean and in turn pushes forward the filtering solution in time closer to the true state when it drives the system. As a result our proposed method can yield a more accurate approximate solution for the state estimation problem. © 1991-2012 IEEE.

  4. Evaluating the Long-term Water Cycle Trends at a Global-scale using Satellite and Assimilation Datasets

    Science.gov (United States)

    Kim, H.; Lakshmi, V.

    2017-12-01

    Global-scale soil moisture and rainfall products retrieved from remotely sensed and assimilation datasets provide an effective way to monitor near surface soil moisture content and precipitation with sub-daily temporal resolution. In the present study, we employed the concept of the stored precipitation fraction Fp(f) in order to examine the long-term water cycle trends at a global-scale. The analysis was done for Fp(f) trends with the various geophysical aspects such as climate zone, land use classifications, amount of vegetation, and soil properties. Furthermore, we compared a global-scale Fp(f) using different microwave-based satellite soil moisture datasets. The Fp(f) is calculated by utilized surface soil moisture dataset from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity, Advanced Scatterometer, Advanced Microwave Scanning Radiometer 2, and precipitation information from Global Precipitation Measurement Mission and Global Land Data Assimilation System. Different results from microwave-based soil moisture dataset showed discordant results particularly over arid and highly vegetated regions. The results of this study provide us new insights of the long-term water cycle trends over different land surface areas. Thereby also highlighting the advantages of the recently available GPM and SMAP datasets for the uses in various hydrometeorological applications.

  5. Impacts of distinct observations during the 2009 Prince William Sound field experiment: A data assimilation study

    Science.gov (United States)

    Li, Z.; Chao, Y.; Farrara, J.; McWilliams, J. C.

    2012-12-01

    A set of data assimilation experiments, known as Observing System Experiments (OSEs), are performed to assess the relative impacts of different types of observations acquired during the 2009 Prince William Sound Field Experiment. The observations assimilated consist primarily of three types: High Frequency (HF) radar surface velocities, vertical profiles of temperature/salinity (T/S) measured by ships, moorings, Autonomous Underwater Vehicles and gliders, and satellite sea surface temperatures (SSTs). The impact of all the observations, HF radar surface velocities, and T/S profiles is assessed. Without data assimilation, a frequently occurring cyclonic eddy in the central Sound is overly persistent and intense. The assimilation of the HF radar velocities effectively reduces these biases and improves the representation of the velocities as well as the T/S fields in the Sound. The assimilation of the T/S profiles improves the large scale representation of the temperature/salinity and also the velocity field in the central Sound. The combination of the HF radar surface velocities and sparse T/S profiles results in an observing system capable of representing the circulation in the Sound reliably and thus producing analyses and forecasts with useful skill. It is suggested that a potentially promising observing network could be based on satellite SSHs and SSTs along with sparse T/S profiles, and future satellite SSHs with wide swath coverage and higher resolution may offer excellent data that will be of great use for predicting the circulation in the Sound.

  6. Improving 7-Day Forecast Skill by Assimilation of Retrieved AIRS Temperature Profiles

    Science.gov (United States)

    Susskind, Joel; Rosenberg, Bob

    2016-01-01

    We conducted a new set of Data Assimilation Experiments covering the period January 1 to February 29, 2016 using the GEOS-5 DAS. Our experiments assimilate all data used operationally by GMAO (Control) with some modifications. Significant improvement in Global and Southern Hemisphere Extra-tropical 7-day forecast skill was obtained when: We assimilated AIRS Quality Controlled temperature profiles in place of observed AIRS radiances, and also did not assimilate CrISATMS radiances, nor did we assimilate radiosonde temperature profiles or aircraft temperatures. This new methodology did not improve or degrade 7-day Northern Hemispheric Extra-tropical forecast skill. We are conducting experiments aimed at further improving of Northern Hemisphere Extra-tropical forecast skill.

  7. Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

    Directory of Open Access Journals (Sweden)

    D. R. Allen

    2018-03-01

    Full Text Available Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM hybrid 4-D variational assimilation (4D-Var data assimilation (DA system. Ozone can improve the wind and temperature through two different DA mechanisms: (1 through the flow-of-the-day ensemble background error covariance that is blended together with the static background error covariance and (2 via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE, which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error global hourly ozone data constrains the stratospheric wind and temperature to within ∼ 2 m s−1 and ∼ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study, rather than 0.0 (no ensemble background error covariance or 1.0 (no static background error covariance, which is consistent with other hybrid DA studies. When

  8. Kalman filter data assimilation: targeting observations and parameter estimation.

    Science.gov (United States)

    Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

    2014-06-01

    This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

  9. Kalman filter data assimilation: Targeting observations and parameter estimation

    International Nuclear Information System (INIS)

    Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex

    2014-01-01

    This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation

  10. Limitations of wind extraction from 4D-Var assimilation of ozone

    Directory of Open Access Journals (Sweden)

    D. R. Allen

    2013-03-01

    Full Text Available Time-dependent variational data assimilation allows the possibility of extracting wind information from observations of ozone or other trace gases. Since trace gas observations are not available at sufficient resolution for deriving feature-track winds, they must be combined with model background information to produce an analysis. If done with time-dependent variational assimilation, wind information may be extracted via the adjoint of the linearized tracer continuity equation. This paper presents idealized experiments that illustrate the mechanics of tracer–wind extraction and demonstrate some of the limitations of this procedure. We first examine tracer–wind extraction using a simple one-dimensional advection equation. The analytic solution for a single trace gas observation is discussed along with numerical solutions for multiple observations. The limitations of tracer–wind extraction are then explored using highly idealized ozone experiments performed with a development version of the Navy Global Environmental Model (NAVGEM in which globally distributed hourly stratospheric ozone profiles are assimilated in a single 6 h update cycle in January 2009. Starting with perfect background ozone conditions, but imperfect dynamical conditions, ozone errors develop over the 6 h background window. Wind increments are introduced in the analysis in order to reduce the differences between background ozone and ozone observations. For "perfect" observations (unbiased and no random error, this results in root-mean-square (RMS vector wind error reductions of up to ~4 m s−1 in the winter hemisphere and tropics. Wind extraction is more difficult in the summer hemisphere due to weak ozone gradients and smaller background wind errors. The limitations of wind extraction are also explored for observations with imposed random errors and for limited sampling patterns. As expected, the amount of wind information extracted degrades as observation errors or

  11. Assimilation of microwave brightness temperatures for soil moisture estimation using particle filter

    International Nuclear Information System (INIS)

    Bi, H Y; Ma, J W; Qin, S X; Zeng, J Y

    2014-01-01

    Soil moisture plays a significant role in global water cycles. Both model simulations and remote sensing observations have their limitations when estimating soil moisture on a large spatial scale. Data assimilation (DA) is a promising tool which can combine model dynamics and remote sensing observations to obtain more precise ground soil moisture distribution. Among various DA methods, the particle filter (PF) can be applied to non-linear and non-Gaussian systems, thus holding great potential for DA. In this study, a data assimilation scheme based on the residual resampling particle filter (RR-PF) was developed to assimilate microwave brightness temperatures into the macro-scale semi-distributed Variance Infiltration Capacity (VIC) Model to estimate surface soil moisture. A radiative transfer model (RTM) was used to link brightness temperatures with surface soil moisture. Finally, the data assimilation scheme was validated by experimental data obtained at Arizona during the Soil Moisture Experiment 2004 (SMEX04). The results show that the estimation accuracy of soil moisture can be improved significantly by RR-PF through assimilating microwave brightness temperatures into VIC model. Both the overall trends and specific values of the assimilation results are more consistent with ground observations compared with model simulation results

  12. Efforts in assimilating Indian satellite data in the NGFS and monitoring of their quality

    Science.gov (United States)

    Prasad, V. S.; Singh, Sanjeev Kumar

    2016-05-01

    Megha-Tropiques (MT) is an Indo-French Joint Satellite Mission, launched on 12 October 2011. MT-SAPHIR is a sounding instrument with 6 channels near the absorption band of water vapor at 183 GHz, for studying the water cycle and energy exchanges in the tropics. The main objective of this mission is to understand the life cycle of convective systems that influence the tropical weather and climate and their role in associated energy and moisture budget of the atmosphere in tropical regions. India also has a prestigious space programme and has launched the INSAT-3D satellite on 26 July 2013 which has an atmospheric sounder for the first time along with improved VHRR imager. NCMRWF (National Centre for Medium Range Weather Forecasting) is regularly receiving these new datasets and also making changes to its Global Data Assimilation Forecasting (GDAF) system from time-to-time to assimilate these new datasets. A well planned strategy involving various steps such as monitoring of data quality, development of observation operator and quality control procedures, and finally then studying its impact on forecasts is developed to include new observations in global data analysis system. By employing this strategy observations having positive impact on forecast quality such as MT-SAPHIR, and INSAT-3D Clear Sky Radiance (CSR) products are identified and being assimilated in the Global Data Assimilation and Forecasting (GDAF) system.

  13. Ionospheric Data Assimilation and Targeted Observation Strategies: Proof of Concept Analysis in a Geomagnetic Storm Event

    Science.gov (United States)

    Kostelich, Eric; Durazo, Juan; Mahalov, Alex

    2017-11-01

    The dynamics of the ionosphere involve complex interactions between the atmosphere, solar wind, cosmic radiation, and Earth's magnetic field. Geomagnetic storms arising from solar activity can perturb these dynamics sufficiently to disrupt radio and satellite communications. Efforts to predict ``space weather,'' including ionospheric dynamics, require the development of a data assimilation system that combines observing systems with appropriate forecast models. This talk will outline a proof-of-concept targeted observation strategy, consisting of the Local Ensemble Transform Kalman Filter, coupled with the Thermosphere Ionosphere Electrodynamics Global Circulation Model, to select optimal locations where additional observations can be made to improve short-term ionospheric forecasts. Initial results using data and forecasts from the geomagnetic storm of 26-27 September 2011 will be described. Work supported by the Air Force Office of Scientific Research (Grant Number FA9550-15-1-0096) and by the National Science Foundation (Grant Number DMS-0940314).

  14. Assimilative and non-assimilative color spreading in the watercolor configuration

    Directory of Open Access Journals (Sweden)

    Eiji eKimura

    2014-09-01

    Full Text Available A colored line flanking a darker contour will appear to spread its color onto an area enclosed by the line (watercolor effect. The watercolor effect has been characterized as an assimilative effect, but non-assimilative color spreading has also been demonstrated in the same spatial configuration; e.g., when a black inner contour (IC is paired with a blue outer contour (OC, yellow color spreading can be observed. To elucidate visual mechanisms underlying these different color spreading effects, this study investigated the effects of luminance ratio between the double contours on the induced color by systematically manipulating the IC and OC luminances (Experiment 1 as well as the background luminance (Experiment 2. The results showed that the luminance conditions suitable for assimilative and non-assimilative color spreading were nearly opposite. When the Weber contrast of the IC to the background luminances (IC contrast was smaller than that of the OC (OC contrast, the induced color became similar to the IC color (assimilative spreading. In contrast, when the OC contrast was smaller than or equal to the IC contrast, the induced color became yellow (non-assimilative spreading. Extending these findings, Experiment 3 showed that bilateral color spreading, e.g., assimilative spreading on one side and non-assimilative spreading on the other side, can also be observed in the watercolor configuration. These results suggest that the assimilative and non-assimilative spreading were mediated by different visual mechanisms. The properties of the assimilative spreading are consistent with the model proposed to account for neon color spreading [Grossberg, S. & Mingolla, E. (1985 Percept. Psychophys., 38, 141-171] and extended for the watercolor effect [Pinna, B., & Grossberg, S. (2005 J. Opt. Soc. Am. A, 22, 2207-2221]. However, the present results suggest that additional mechanisms are needed to account for the non-assimilative color spreading.

  15. Assimilative and non-assimilative color spreading in the watercolor configuration.

    Science.gov (United States)

    Kimura, Eiji; Kuroki, Mikako

    2014-01-01

    A colored line flanking a darker contour will appear to spread its color onto an area enclosed by the line (watercolor effect). The watercolor effect has been characterized as an assimilative effect, but non-assimilative color spreading has also been demonstrated in the same spatial configuration; e.g., when a black inner contour (IC) is paired with a blue outer contour (OC), yellow color spreading can be observed. To elucidate visual mechanisms underlying these different color spreading effects, this study investigated the effects of luminance ratio between the double contours on the induced color by systematically manipulating the IC and the OC luminance (Experiment 1) as well as the background luminance (Experiment 2). The results showed that the luminance conditions suitable for assimilative and non-assimilative color spreading were nearly opposite. When the Weber contrast of the IC to the background luminance (IC contrast) was smaller in size than that of the OC (OC contrast), the induced color became similar to the IC color (assimilative spreading). In contrast, when the OC contrast was smaller than or equal to the IC contrast, the induced color became yellow (non-assimilative spreading). Extending these findings, Experiment 3 showed that bilateral color spreading, i.e., assimilative spreading on one side and non-assimilative spreading on the other side, can also be observed in the watercolor configuration. These results suggest that the assimilative and the non-assimilative spreading were mediated by different visual mechanisms. The properties of the assimilative spreading are consistent with the model proposed to account for neon color spreading (Grossberg and Mingolla, 1985) and extended for the watercolor effect (Pinna and Grossberg, 2005). However, the present results suggest that additional mechanisms are needed to account for the non-assimilative color spreading.

  16. Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model

    Science.gov (United States)

    Vira, J.; Sofiev, M.

    2015-02-01

    This paper describes the assimilation of trace gas observations into the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) using the 3D-Var method. Assimilation results for the year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the AirBase observation database, which provides the observational data set used in this study. Attention was paid to the background and observation error covariance matrices, which were obtained primarily by the iterative application of a posteriori diagnostics. The diagnostics were computed separately for 2 months representing summer and winter conditions, and further disaggregated by time of day. This enabled the derivation of background and observation error covariance definitions, which included both seasonal and diurnal variation. The consistency of the obtained covariance matrices was verified using χ2 diagnostics. The analysis scores were computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values was improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

  17. Ocean Data Assimilation in the Gulf of Mexico Using 3D VAR Approach - Preliminary Results

    Science.gov (United States)

    Paturi, S.; Garraffo, Z. D.; Cummings, J. A.; Rivin, I.; Mehra, A.; Kim, H. C.

    2016-12-01

    Approaches to ocean data assimilation vary widely, both in terms of the sophistication of the method and the observations assimilated.A three-dimensional variational (3DVAR) data assimilation system, part of the Navy Coupled Ocean Data Assimilation (NCODA) system developed at Navy Research Laboratory (NRL), is used for assimilating Sea Surface Temperature (SST) and Sea Surface Height (SSH) in the Gulf of Mexico (GoM). The NCODA 3DVAR produces simultaneous analyses of temperature, salinity, and vector velocity and uses all possible sources of ocean data observations.The Hybrid Coordinate Ocean Model (HYCOM) is used for the simulations, at 1/25o grid resolution for July 2011 period. After successful implementation of NCODA 3DVAR in the GoM, the system will be extended to the global ocean with the intent of making it operational.

  18. Improving carbon model phenology using data assimilation

    Science.gov (United States)

    Exrayat, Jean-François; Smallman, T. Luke; Bloom, A. Anthony; Williams, Mathew

    2015-04-01

    Carbon cycle dynamics is significantly impacted by ecosystem phenology, leading to substantial seasonal and inter-annual variation in the global carbon balance. Representing inter-annual variability is key for predicting the response of the terrestrial ecosystem to climate change and disturbance. Existing terrestrial ecosystem models (TEMs) often struggle to accurately simulate observed inter-annual variability. TEMs often use different phenological models based on plant functional type (PFT) assumptions. Moreover, due to a high level of computational overhead in TEMs they are unable to take advantage of globally available datasets to calibrate their models. Here we describe the novel CARbon DAta MOdel fraMework (CARDAMOM) for data assimilation. CARDAMOM is used to calibrate the Data Assimilation Linked Ecosystem Carbon version 2 (DALEC2) model using Bayes' Theorem within a Metropolis Hastings - Markov Chain Monte Carlo (MH-MCMC). CARDAMOM provides a framework which combines knowledge from observations, such as remotely sensed LAI, and heuristic information in the form of Ecological and Dynamical Constraints (EDCs). The EDCs are representative of real world processes and constrain parameter interdependencies and constrain carbon dynamics. We used CARDAMOM to bring together globally spanning datasets of LAI and the DALEC2 and DALEC2-GSI models. These analyses allow us to investigate the sensitivity ecosystem processes to the representation of phenology. DALEC2 uses an analytically solved model of phenology which is invariant between years. In contrast DALEC2-GSI uses a growing season index (GSI) calculated as a function of temperature, vapour pressure deficit (VPD) and photoperiod to calculate bud-burst and leaf senescence, allowing the model to simulate inter-annual variability in response to climate. Neither model makes any PFT assumptions about the phenological controls of a given ecosystem, allowing the data alone to determine the impact of the meteorological

  19. Century long observation constrained global dynamic downscaling and hydrologic implication

    Science.gov (United States)

    Kim, H.; Yoshimura, K.; Chang, E.; Famiglietti, J. S.; Oki, T.

    2012-12-01

    It has been suggested that greenhouse gas induced warming climate causes the acceleration of large scale hydrologic cycles, and, indeed, many regions on the Earth have been suffered by hydrologic extremes getting more frequent. However, historical observations are not able to provide enough information in comprehensive manner to understand their long-term variability and/or global distributions. In this study, a century long high resolution global climate data is developed in order to break through existing limitations. 20th Century Reanalysis (20CR) which has relatively low spatial resolution (~2.0°) and longer term availability (140 years) is dynamically downscaled into global T248 (~0.5°) resolution using Experimental Climate Prediction Center (ECPC) Global Spectral Model (GSM) by spectral nudging data assimilation technique. Also, Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU) observational data are adopted to reduce model dependent uncertainty. Downscaled product successfully represents realistic geographical detail keeping low frequency signal in mean state and spatiotemporal variability, while previous bias correction method fails to reproduce high frequency variability. Newly developed data is used to investigate how long-term large scale terrestrial hydrologic cycles have been changed globally and how they have been interacted with various climate modes, such as El-Niño Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO). As a further application, it will be used to provide atmospheric boundary condition of multiple land surface models in the Global Soil Wetness Project Phase 3 (GSWP3).

  20. Assimilation of Doppler weather radar observations in a mesoscale ...

    Indian Academy of Sciences (India)

    Research (PSU–NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation ... investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present ...... Results presented in this paper are based on.

  1. Modeling Global Ocean Biogeochemistry With Physical Data Assimilation: A Pragmatic Solution to the Equatorial Instability

    Science.gov (United States)

    Park, Jong-Yeon; Stock, Charles A.; Yang, Xiaosong; Dunne, John P.; Rosati, Anthony; John, Jasmin; Zhang, Shaoqing

    2018-03-01

    Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled-climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model-data weights that removed spurious biogeochemical signals while benefitting from off-equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.

  2. Study of the combined effects of data assimilation and grid nesting in ocean models – application to the Gulf of Lions

    Directory of Open Access Journals (Sweden)

    L. Vandenbulcke

    2006-01-01

    Full Text Available Modern operational ocean forecasting systems routinely use data assimilation techniques in order to take observations into account in the hydrodynamic model. Moreover, as end users require higher and higher resolution predictions, especially in coastal zones, it is now common to run nested models, where the coastal model gets its open-sea boundary conditions from a low-resolution global model. This configuration is used in the "Mediterranean Forecasting System: Towards environmental predictions" (MFSTEP project. A global model covering the whole Mediterranean Sea is run weekly, performing 1 week of hindcast and a 10-day forecast. Regional models, using different codes and covering different areas, then use this forecast to implement boundary conditions. Local models in turn use the regional model forecasts for their own boundary conditions. This nested system has proven to be a viable and efficient system to achieve high-resolution weekly forecasts. However, when observations are available in some coastal zone, it remains unclear whether it is better to assimilate them in the global or local model. We perform twin experiments and assimilate observations in the global or in the local model, or in both of them together. We show that, when interested in the local models forecast and provided the global model fields are approximately correct, the best results are obtained when assimilating observations in the local model.

  3. Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

    Directory of Open Access Journals (Sweden)

    Y. Zhao

    2017-11-01

    Full Text Available Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

  4. Carbon cycling of European croplands: A framework for the assimilation of optical and microwave Earth observation data

    Science.gov (United States)

    Revill, Andrew; Sus, Oliver; Williams, Mathew

    2013-04-01

    Croplands are traditionally managed to maximise the production of food, feed, fibre and bioenergy. Advancements in agricultural technologies, together with land-use change, have approximately doubled World grain harvests over the past 50 years. Cropland ecosystems also play a significant role in the global carbon (C) cycle and, through changes to C storage in response to management activities, they can provide opportunities for climate change mitigation. However, quantifying and understanding the cropland C cycle is complex, due to variable environmental drivers, varied management practices and often highly heterogeneous landscapes. Efforts to upscale processes using simulation models must resolve these challenges. Here we show how data assimilation (DA) approaches can link C cycle modelling to Earth observation (EO) and reduce uncertainty in upscaling. We evaluate a framework for the assimilation of leaf area index (LAI) time series, empirically derived from EO optical and radar sensors, for state-updating a model of crop development and C fluxes. Sensors are selected with fine spatial resolutions (20-50 m) to resolve variability across field sizes typically used in European agriculture. Sequential DA is used to improve the canopy development simulation, which is validated by comparing time-series LAI and net ecosystem exchange (NEE) predictions to independent ground measurements and eddy covariance observations at multiple European cereal crop sites. Significant empirical relationships were established between the LAI ground measurements and the optical reflectance and radar backscatter, which allowed for single LAI calibrations being valid for all the cropland sites for each sensor. The DA of all EO LAI estimates results indicated clear adjustments in LAI and an enhanced representation of daily CO2 exchanges, particularly around the time of peak C uptake. Compared to the simulation without DA, the assimilation of all EO LAI estimates improved the predicted at

  5. Correcting Biases in a lower resolution global circulation model with data assimilation

    Science.gov (United States)

    Canter, Martin; Barth, Alexander

    2016-04-01

    With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model's equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Variational Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on topography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have

  6. A storm-time plasmasphere evolution study using data assimilation

    Science.gov (United States)

    Nikoukar, R.; Bust, G. S.; Bishop, R. L.; Coster, A. J.; Lemon, C.; Turner, D. L.; Roeder, J. L.

    2017-12-01

    In this work, we study the evolution of the Earth's plasmasphere during geomagnetic active periods using the Plasmasphere Data Assimilation (PDA) model. The total electron content (TEC) measurements from an extensive network of global ground-based GPS receivers as well as GPS receivers on-board Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellites and Communications/Navigation Outage Forecasting System (C/NOFS) satellite are ingested into the model. Global Core Plasma model, which is an empirical plasmasphere model, is utilized as the background model. Based on the 3D-VAR optimization, the PDA assimilative model benefits from incorporation of regularization techniques to prevent non-physical altitudinal variation in density estimates due to the limited-angle observational geometry. This work focuses on the plasmapause location, plasmasphere erosion time scales and refilling rates during the main and recovery phases of geomagnetic storms as estimated from the PDA 3-dimensional global maps of electron density in the ionosphere/plasmasphere. The comparison between the PDA results with in-situ density measurements from THEMIS and Van Allen Probes, and the RCM-E first-principle model will be also presented.

  7. Effects of model chemistry and data biases on stratospheric ozone assimilation

    Directory of Open Access Journals (Sweden)

    L. Coy

    2007-06-01

    Full Text Available The innovations or observation minus forecast (O–F residuals produced by a data assimilation system provide a convenient metric of evaluating global analyses. In this study, O–F statistics from the Global Ozone Assimilation Testing System (GOATS are used to examine how ozone assimilation products and their associated O–F statistics depend on input data biases and ozone photochemistry parameterizations (OPP. All the GOATS results shown are based on a 6-h forecast and analysis cycle using observations from SBUV/2 (Solar Backscatter UltraViolet instrument-2 during September–October 2002. Results show that zonal mean ozone analyses are more independent of observation biases and drifts when using an OPP, while the mean ozone O–Fs are more sensitive to observation drifts when using an OPP. In addition, SD O–Fs (standard deviations are reduced in the upper stratosphere when using an OPP due to a reduction of forecast model noise and to increased covariance between the forecast model and the observations. Experiments that changed the OPP reference state to match the observations by using an "adaptive" OPP scheme reduced the mean ozone O–Fs at the expense of zonal mean ozone analyses being more susceptible to data biases and drifts. Additional experiments showed that the upper boundary of the ozone DAS can affect the quality of the ozone analysis and therefore should be placed well above (at least a scale height the region of interest.

  8. Global CO emission estimates inferred from assimilation of MOPITT and IASI CO data, together with observations of O3, NO2, HNO3, and HCHO.

    Science.gov (United States)

    Zhang, X.; Jones, D. B. A.; Keller, M.; Jiang, Z.; Bourassa, A. E.; Degenstein, D. A.; Clerbaux, C.; Pierre-Francois, C.

    2017-12-01

    Atmospheric carbon monoxide (CO) emissions estimated from inverse modeling analyses exhibit large uncertainties, due, in part, to discrepancies in the tropospheric chemistry in atmospheric models. We attempt to reduce the uncertainties in CO emission estimates by constraining the modeled abundance of ozone (O3), nitrogen dioxide (NO2), nitric acid (HNO3), and formaldehyde (HCHO), which are constituents that play a key role in tropospheric chemistry. Using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system, we estimate CO emissions by assimilating observations of CO from the Measurement of Pollution In the Troposphere (MOPITT) and the Infrared Atmospheric Sounding Interferometer (IASI), together with observations of O3 from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and IASI, NO2 and HCHO from the Ozone Monitoring Instrument (OMI), and HNO3 from the Microwave Limb Sounder (MLS). Our experiments evaluate the inferred CO emission estimates from major anthropogenic, biomass burning and biogenic sources. Moreover, we also infer surface emissions of nitrogen oxides (NOx = NO + NO2) and isoprene. Our results reveal that this multiple species chemical data assimilation produces a chemical consistent state that effectively adjusts the CO-O3-OH coupling in the model. The O3-induced changes in OH are particularly large in the tropics. Overall, our analysis results in a better constrained tropospheric chemical state.

  9. Ecological Assimilation of Land and Climate Observations - the EALCO model

    Science.gov (United States)

    Wang, S.; Zhang, Y.; Trishchenko, A.

    2004-05-01

    Ecosystems are intrinsically dynamic and interact with climate at a highly integrated level. Climate variables are the main driving factors in controlling the ecosystem physical, physiological, and biogeochemical processes including energy balance, water balance, photosynthesis, respiration, and nutrient cycling. On the other hand, ecosystems function as an integrity and feedback on the climate system through their control on surface radiation balance, energy partitioning, and greenhouse gases exchange. To improve our capability in climate change impact assessment, a comprehensive ecosystem model is required to address the many interactions between climate change and ecosystems. In addition, different ecosystems can have very different responses to the climate change and its variation. To provide more scientific support for ecosystem impact assessment at national scale, it is imperative that ecosystem models have the capability of assimilating the large scale geospatial information including satellite observations, GIS datasets, and climate model outputs or reanalysis. The EALCO model (Ecological Assimilation of Land and Climate Observations) is developed for such purposes. EALCO includes the comprehensive interactions among ecosystem processes and climate, and assimilates a variety of remote sensing products and GIS database. It provides both national and local scale model outputs for ecosystem responses to climate change including radiation and energy balances, water conditions and hydrological cycles, carbon sequestration and greenhouse gas exchange, and nutrient (N) cycling. These results form the foundation for the assessment of climate change impact on ecosystems, their services, and adaptation options. In this poster, the main algorithms for the radiation, energy, water, carbon, and nitrogen simulations were diagrammed. Sample input data layers at Canada national scale were illustrated. Model outputs including the Canada wide spatial distributions of net

  10. Coupled assimilation for an intermediated coupled ENSO prediction model

    Science.gov (United States)

    Zheng, Fei; Zhu, Jiang

    2010-10-01

    The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind-ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997-2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.

  11. Photosynthesis and assimilate partitioning characteristics of the coconut palm as observed by carbon-14 labelling

    International Nuclear Information System (INIS)

    Jayasekara, K.S.; Jayaswkara, K.S.; Bowen, G.D.

    2000-01-01

    A technique was developed on the use of carbon dioxide(carbon-14 labelled) rapid labelling of foliage and to ascertain photosynthesis and partitioning characteristics of labelled assimilate into other parts of the coconut palm. An eight-year-old Tall x Tall young coconut palm growing under field conditions at Bandirippuwa Estate and with six developing bunches , was selected for this study. The labelling was carried out on a bright sunny day and soil was at field capacity. Seventh leaf from the youngest open leaf was used for labelling with 5 mCi of sodium bi carbonate (Carbon-14 labelled). The results revealed that within 24 hours, 60% of the labelled assimilate was partitioned into other parts of the palm and at the end of the seventh day about 18% of the labelled assimilate still remained in the labelled leaf. Among the developing bunches fifth and sixth bunches from the youngest developing bunch received more labelled assimilate than young developing bunches above them. It was revealed that partitioning of assimilate into various ''sinks'' is determined by the developmental stage or activeness of the ''sink''. The proportion of C-14 labelled carbon assimilate, partitioned into developing bunches was substantially low compared to the total amount of labelled carbon fixed by the labelled leaf. Further, it was observed that partitioning of assimilated labelled carbon into the young leaves above, as well as the mature leaves below the labelled leaf. The complex vascular anatomy of the palms could be attributed to this pattern of partitioning of assimilates into upper and lower leaves from the labelled leaf

  12. Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

    Science.gov (United States)

    Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Forman, Barton A.; Draper, Clara S.; Liu, Qing

    2013-01-01

    A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

  13. Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

    Directory of Open Access Journals (Sweden)

    Chih-Chien Tsai

    2014-03-01

    Full Text Available This study develops a Doppler radar data assimilation system, which couples the local ensemble transform Kalman filter with the Weather Research and Forecasting model. The benefits of this system to quantitative precipitation nowcasting (QPN are evaluated with observing system simulation experiments on Typhoon Morakot (2009, which brought record-breaking rainfall and extensive damage to central and southern Taiwan. The results indicate that the assimilation of radial velocity and reflectivity observations improves the three-dimensional winds and rain-mixing ratio most significantly because of the direct relations in the observation operator. The patterns of spiral rainbands become more consistent between different ensemble members after radar data assimilation. The rainfall intensity and distribution during the 6-hour deterministic nowcast are also improved, especially for the first 3 hours. The nowcasts with and without radar data assimilation have similar evolution trends driven by synoptic-scale conditions. Furthermore, we carry out a series of sensitivity experiments to develop proper assimilation strategies, in which a mixed localisation method is proposed for the first time and found to give further QPN improvement in this typhoon case.

  14. A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

    Science.gov (United States)

    Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

    2011-01-01

    Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

  15. Contribution of Anthropogenic and Natural Emissions to Global CH4 Balances by Utilizing δ13C-CH4 Observations in CarbonTracker Data Assimilation System (CTDAS)

    Science.gov (United States)

    Kangasaho, V. E.; Tsuruta, A.; Aalto, T.; Backman, L. B.; Houweling, S.; Krol, M. C.; Peters, W.; van der Laan-Luijkx, I. T.; Lienert, S.; Joos, F.; Dlugokencky, E. J.; Michael, S.; White, J. W. C.

    2017-12-01

    The atmospheric burden of CH4 has more than doubled since preindustrial time. Evaluating the contribution from anthropogenic and natural emissions to the global methane budget is of great importance to better understand the significance of different sources at the global scale, and their contribution to changes in growth rate of atmospheric CH4 before and after 2006. In addition, observations of δ13C-CH4 suggest an increase in natural sources after 2006, which matches the observed increase and variation of CH4 abudance. Methane emission sources can be identified using δ13C-CH4, because different sources produce methane with process-specific isotopic signatures. This study focuses on inversion model based estimates of global anthropogenic and natural methane emission rates to evaluate the existing methane emission estimates with a new δ13C-CH4 inversion system. In situ measurements of atmospheric methane and δ13C-CH4 isotopic signature, provided by the NOAA Global Monitoring Division and the Institute of Arctic and Alpine Research, will be assimilated into the CTDAS-13C-CH4. The system uses the TM5 atmospheric transport model as an observation operator, constrained by ECMWF ERA Interim meteorological fields, and off-line TM5 chemistry fields to account for the atmospheric methane sink. LPX-Bern DYPTOP ecosystem model is used for prior natural methane emissions from wetlands, peatlands and mineral soils, GFED v4 for prior fire emissions and EDGAR v4.2 FT2010 inventory for prior anthropogenic emissions. The EDGAR antropogenic emissions are re-divided into enteric fermentation and manure management, landfills and waste water, rice, coal, oil and gas, and residential emissions, and the trend of total emissions is scaled to match optimized anthropogenic emissions from CTE-CH4. In addition to these categories, emissions from termites and oceans are included. Process specific δ13C-CH4 isotopic signatures are assigned to each emission source to estimate 13CH4 fraction

  16. Data assimilation of GNSS zenith total delays from a Nordic processing centre

    Science.gov (United States)

    Lindskog, Magnus; Ridal, Martin; Thorsteinsson, Sigurdur; Ning, Tong

    2017-11-01

    Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

  17. Temporal Reference, Attentional Modulation, and Crossmodal Assimilation

    Directory of Open Access Journals (Sweden)

    Yingqi Wan

    2018-06-01

    Full Text Available Crossmodal assimilation effect refers to the prominent phenomenon by which ensemble mean extracted from a sequence of task-irrelevant distractor events, such as auditory intervals, assimilates/biases the perception (such as visual interval of the subsequent task-relevant target events in another sensory modality. In current experiments, using visual Ternus display, we examined the roles of temporal reference, materialized as the time information accumulated before the onset of target event, as well as the attentional modulation in crossmodal temporal interaction. Specifically, we examined how the global time interval, the mean auditory inter-intervals and the last interval in the auditory sequence assimilate and bias the subsequent percept of visual Ternus motion (element motion vs. group motion. We demonstrated that both the ensemble (geometric mean and the last interval in the auditory sequence contribute to bias the percept of visual motion. Longer mean (or last interval elicited more reports of group motion, whereas the shorter mean (or last auditory intervals gave rise to more dominant percept of element motion. Importantly, observers have shown dynamic adaptation to the temporal reference of crossmodal assimilation: when the target visual Ternus stimuli were separated by a long gap interval after the preceding sound sequence, the assimilation effect by ensemble mean was reduced. Our findings suggested that crossmodal assimilation relies on a suitable temporal reference on adaptation level, and revealed a general temporal perceptual grouping principle underlying complex audio-visual interactions in everyday dynamic situations.

  18. Initial Assessment of Cyclone Global Navigation Satellite System (CYGNSS) Observations

    Science.gov (United States)

    McKague, D. S.; Ruf, C. S.

    2017-12-01

    The NASA Cyclone Global Navigation Satellite System (CYNSS) mission provides high temporal resolution observations of cyclones from a constellation of eight low-Earth orbiting satellites. Using the relatively new technique of Global Navigation Satellite System reflectometry (GNSS-R), all-weather observations are possible, penetrating even deep convection within hurricane eye walls. The compact nature of the GNSS-R receivers permits the use of small satellites, which in turn enables the launch of a constellation of satellites from a single launch vehicle. Launched in December of 2016, the eight CYGNSS satellites provide 25 km resolution observations of mean square slope (surface roughness) and surface winds with a 2.8 hour median revisit time from 38 S to 38 N degrees latitude. In addition to the calibration and validation of CYGNSS sea state observations, the CYGNSS science team is assessing the ability of the mission to provide estimates of cyclone size, intensity, and integrated kinetic energy. With its all-weather ability and high temporal resolution, the CYGNSS mission will add significantly to our ability to monitor cyclone genesis and intensification and will significantly reduce uncertainties in our ability to estimate cyclone intensity, a key variable in predicting its destructive potential. Members of the CYGNSS Science Team are also assessing the assimilation of CYGNSS data into hurricane forecast models to determine the impact of the data on forecast skill, using the data to study extra-tropical cyclones, and looking at connections between tropical cyclones and global scale weather, including the global hydrologic cycle. This presentation will focus on the assessment of early on-orbit observations of cyclones with respect to these various applications.

  19. Prospects for development of unified global flood observation and prediction systems (Invited)

    Science.gov (United States)

    Lettenmaier, D. P.

    2013-12-01

    Floods are among the most damaging of natural hazards, with global flood losses in 2011 alone estimated to have exceeded $100B. Historically, flood economic damages have been highest in the developed world (due in part to encroachment on historical flood plains), but loss of life, and human impacts have been greatest in the developing world. However, as the 2011 Thailand floods show, industrializing countries, many of which do not have well developed flood protection systems, are increasingly vulnerable to economic damages as they become more industrialized. At present, unified global flood observation and prediction systems are in their infancy; notwithstanding that global weather forecasting is a mature field. The summary for this session identifies two evolving capabilities that hold promise for development of more sophisticated global flood forecast systems: global hydrologic models and satellite remote sensing (primarily of precipitation, but also of flood inundation). To this I would add the increasing sophistication and accuracy of global precipitation analysis (and forecast) fields from numerical weather prediction models. In this brief overview, I will review progress in all three areas, and especially the evolution of hydrologic data assimilation which integrates modeling and data sources. I will also comment on inter-governmental and inter-agency cooperation, and related issues that have impeded progress in the development and utilization of global flood observation and prediction systems.

  20. Data Assimilation of SMAP Observations and the Impact on Weather Forecasts and Heat Stress

    Science.gov (United States)

    Zavodsky, Bradley; Case, Jonathan; Blankenship, Clay; Crosson, William; White, Khristopher

    2014-01-01

    SPoRT produces real-time LIS soil moisture products for situational awareness and local numerical weather prediction over CONUS, Mesoamerica, and East Africa ?Currently interact/collaborate with operational partners on evaluation of soil moisture products ?Drought/fire ?Extreme heat ?Convective initiation ?Flood and water borne diseases ?Initial efforts to assimilate L2 soil moisture observations from SMOS (as a precursor for SMAP) have been successful ?Active/passive blended product from SMAP will be assimilated similarly and higher spatial resolution should improve on local-scale processes

  1. Challenges of coordinating global climate observations - Role of satellites in climate monitoring

    Science.gov (United States)

    Richter, C.

    2017-12-01

    Global observation of the Earth's atmosphere, ocean and land is essential for identifying climate variability and change, and for understanding their causes. Observation also provides data that are fundamental for evaluating, refining and initializing the models that predict how the climate system will vary over the months and seasons ahead, and that project how climate will change in the longer term under different assumptions concerning greenhouse gas emissions and other human influences. Long-term observational records have enabled the Intergovernmental Panel on Climate Change to deliver the message that warming of the global climate system is unequivocal. As the Earth's climate enters a new era, in which it is forced by human activities, as well as natural processes, it is critically important to sustain an observing system capable of detecting and documenting global climate variability and change over long periods of time. High-quality climate observations are required to assess the present state of the ocean, cryosphere, atmosphere and land and place them in context with the past. The global observing system for climate is not a single, centrally managed observing system. Rather, it is a composite "system of systems" comprising a set of climate-relevant observing, data-management, product-generation and data-distribution systems. Data from satellites underpin many of the Essential Climate Variables(ECVs), and their historic and contemporary archives are a key part of the global climate observing system. In general, the ECVs will be provided in the form of climate data records that are created by processing and archiving time series of satellite and in situ measurements. Early satellite data records are very valuable because they provide unique observations in many regions which were not otherwise observed during the 1970s and which can be assimilated in atmospheric reanalyses and so extend the satellite climate data records back in time.

  2. Snow water equivalent monitoring retrieved by assimilating passive microwave observations in a coupled snowpack evolution and microwave emission models over North-Eastern Canada

    Science.gov (United States)

    Royer, A.; Larue, F.; De Sève, D.; Roy, A.; Vionnet, V.; Picard, G.; Cosme, E.

    2017-12-01

    Over northern snow-dominated basins, the snow water equivalent (SWE) is of primary interest for spring streamflow forecasting. SWE retrievals from satellite data are still not well resolved, in particular from microwave (MW) measurements, the only type of data sensible to snow mass. Also, the use of snowpack models is challenging due to the large uncertainties in meteorological input forcings. This project aims to improve SWE prediction by assimilation of satellite brightness temperature (TB), without any ground-based observations. The proposed approach is the coupling of a detailed multilayer snowpack model (Crocus) with a MW snow emission model (DMRT-ML). The assimilation scheme is a Sequential Importance Resampling Particle filter, through ensembles of perturbed meteorological forcings according to their respective uncertainties. Crocus simulations driven by operational meteorological forecasts from the Canadian Global Environmental Multiscale model at 10 km spatial resolution were compared to continuous daily SWE measurements over Québec, North-Eastern Canada (56° - 45°N). The results show a mean bias of the maximum SWE overestimated by 16% with variations up to +32%. This observed large variability could lead to dramatic consequences on spring flood forecasts. Results of Crocus-DMRT-ML coupling compared to surface-based TB measurements (at 11, 19 and 37 GHz) show that the Crocus snowpack microstructure described by sticky hard spheres within DMRT has to be scaled by a snow stickiness of 0.18, significantly reducing the overall RMSE of simulated TBs. The ability of assimilation of daily TBs to correct the simulated SWE is first presented through twin experiments with synthetic data, and then with AMSR-2 satellite time series of TBs along the winter taking into account atmospheric and forest canopy interferences (absorption and emission). The differences between TBs at 19-37 GHz and at 11-19 GHz, in vertical polarization, were assimilated. This assimilation

  3. Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

    Science.gov (United States)

    Bosilovich, Michael G.; Radakovich, Jon D.; daSilva, Arlindo; Todling, Ricardo; Verter, Frances

    2006-01-01

    In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, an incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 Wm(sup -2) Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.

  4. Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

    Science.gov (United States)

    Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

    2014-01-01

    Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

  5. AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

    Science.gov (United States)

    Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

    2011-01-01

    A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

  6. Regional Data Assimilation Using a Stretched-Grid Approach and Ensemble Calculations

    Science.gov (United States)

    Fox-Rabinovitz, M. S.; Takacs, L. L.; Govindaraju, R. C.; Atlas, Robert (Technical Monitor)

    2002-01-01

    The global variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) incorporating the GEOS SG-GCM (Fox-Rabinovitz 2000, Fox-Rabinovitz et al. 2001a,b), has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The major area of interest with enhanced regional resolution used in different SG-DAS experiments includes a rectangle over the U.S. with 50 or 60 km horizontal resolution. The analyses and diagnostics are produced for all mandatory levels from the surface to 0.2 hPa. The assimilated regional mesoscale products are consistent with global scale circulation characteristics due to using the SG-approach. Both the stretched grid and basic uniform grid DASs use the same amount of global grid-points and are compared in terms of regional product quality.

  7. Implementation of Coupled Skin Temperature Analysis and Bias Correction in a Global Atmospheric Data Assimilation System

    Science.gov (United States)

    Radakovich, Jon; Bosilovich, M.; Chern, Jiun-dar; daSilva, Arlindo

    2004-01-01

    The NASA/NCAR Finite Volume GCM (fvGCM) with the NCAR CLM (Community Land Model) version 2.0 was integrated into the NASA/GMAO Finite Volume Data Assimilation System (fvDAS). A new method was developed for coupled skin temperature assimilation and bias correction where the analysis increment and bias correction term is passed into the CLM2 and considered a forcing term in the solution to the energy balance. For our purposes, the fvDAS CLM2 was run at 1 deg. x 1.25 deg. horizontal resolution with 55 vertical levels. We assimilate the ISCCP-DX (30 km resolution) surface temperature product. The atmospheric analysis was performed 6-hourly, while the skin temperature analysis was performed 3-hourly. The bias correction term, which was updated at the analysis times, was added to the skin temperature tendency equation at every timestep. In this presentation, we focus on the validation of the surface energy budget at the in situ reference sites for the Coordinated Enhanced Observation Period (CEOP). We will concentrate on sites that include independent skin temperature measurements and complete energy budget observations for the month of July 2001. In addition, MODIS skin temperature will be used for validation. Several assimilations were conducted and preliminary results will be presented.

  8. Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

    Science.gov (United States)

    Susskind, Joel; Reale, Oreste

    2010-01-01

    AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best

  9. Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

    Science.gov (United States)

    Haupt, Sue Ellen; Beyer-Lout, Anke; Long, Kerrie J.; Young, George S.

    Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.

  10. Inclusion of Linearized Moist Physics in Nasa's Goddard Earth Observing System Data Assimilation Tools

    Science.gov (United States)

    Holdaway, Daniel; Errico, Ronald; Gelaro, Ronaldo; Kim, Jong G.

    2013-01-01

    Inclusion of moist physics in the linearized version of a weather forecast model is beneficial in terms of variational data assimilation. Further, it improves the capability of important tools, such as adjoint-based observation impacts and sensitivity studies. A linearized version of the relaxed Arakawa-Schubert (RAS) convection scheme has been developed and tested in NASA's Goddard Earth Observing System data assimilation tools. A previous study of the RAS scheme showed it to exhibit reasonable linearity and stability. This motivates the development of a linearization of a near-exact version of the RAS scheme. Linearized large-scale condensation is included through simple conversion of supersaturation into precipitation. The linearization of moist physics is validated against the full nonlinear model for 6- and 24-h intervals, relevant to variational data assimilation and observation impacts, respectively. For a small number of profiles, sudden large growth in the perturbation trajectory is encountered. Efficient filtering of these profiles is achieved by diagnosis of steep gradients in a reduced version of the operator of the tangent linear model. With filtering turned on, the inclusion of linearized moist physics increases the correlation between the nonlinear perturbation trajectory and the linear approximation of the perturbation trajectory. A month-long observation impact experiment is performed and the effect of including moist physics on the impacts is discussed. Impacts from moist-sensitive instruments and channels are increased. The effect of including moist physics is examined for adjoint sensitivity studies. A case study examining an intensifying Northern Hemisphere Atlantic storm is presented. The results show a significant sensitivity with respect to moisture.

  11. The Computational Complexity, Parallel Scalability, and Performance of Atmospheric Data Assimilation Algorithms

    Science.gov (United States)

    Lyster, Peter M.; Guo, J.; Clune, T.; Larson, J. W.; Atlas, Robert (Technical Monitor)

    2001-01-01

    The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.

  12. Data Assimilation at FLUXNET to Improve Models towards Ecological Forecasting (Invited)

    Science.gov (United States)

    Luo, Y.

    2009-12-01

    Dramatically increased volumes of data from observational and experimental networks such as FLUXNET call for transformation of ecological research to increase its emphasis on quantitative forecasting. Ecological forecasting will also meet the societal need to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-based models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today’s ecological models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting is data assimilation, which uses data to inform initial conditions and to help constrain a model during simulation to yield results that approximate reality as closely as possible. In an era with dramatically increased availability of data from observational and experimental networks, data assimilation is a key technique that helps convert the raw data into ecologically meaningful products so as to accelerate our understanding of ecological processes, test ecological theory, forecast changes in ecological services, and better serve the society. This talk will use examples to illustrate how data from FLUXNET have been assimilated with process-based models to improve estimates of model parameters and state variables; to quantify uncertainties in ecological forecasting arising from observations, models and their interactions; and to evaluate information contributions of data and model toward short- and long-term forecasting of ecosystem responses to global change.

  13. I/O Parallelization for the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

    Science.gov (United States)

    Lucchesi, Rob; Sawyer, W.; Takacs, L. L.; Lyster, P.; Zero, J.

    1998-01-01

    The National Aeronautics and Space Administration (NASA) Data Assimilation Office (DAO) at the Goddard Space Flight Center (GSFC) has developed the GEOS DAS, a data assimilation system that provides production support for NASA missions and will support NASA's Earth Observing System (EOS) in the coming years. The GEOS DAS will be used to provide background fields of meteorological quantities to EOS satellite instrument teams for use in their data algorithms as well as providing assimilated data sets for climate studies on decadal time scales. The DAO has been involved in prototyping parallel implementations of the GEOS DAS for a number of years and is now embarking on an effort to convert the production version from shared-memory parallelism to distributed-memory parallelism using the portable Message-Passing Interface (MPI). The GEOS DAS consists of two main components, an atmospheric General Circulation Model (GCM) and a Physical-space Statistical Analysis System (PSAS). The GCM operates on data that are stored on a regular grid while PSAS works with observational data that are scattered irregularly throughout the atmosphere. As a result, the two components have different data decompositions. The GCM is decomposed horizontally as a checkerboard with all vertical levels of each box existing on the same processing element(PE). The dynamical core of the GCM can also operate on a rotated grid, which requires communication-intensive grid transformations during GCM integration. PSAS groups observations on PEs in a more irregular and dynamic fashion.

  14. Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

    Science.gov (United States)

    Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

    2015-12-01

    Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

  15. A global carbon assimilation system based on a dual optimization method

    Science.gov (United States)

    Zheng, H.; Li, Y.; Chen, J. M.; Wang, T.; Huang, Q.; Huang, W. X.; Wang, L. H.; Li, S. M.; Yuan, W. P.; Zheng, X.; Zhang, S. P.; Chen, Z. Q.; Jiang, F.

    2015-02-01

    Ecological models are effective tools for simulating the distribution of global carbon sources and sinks. However, these models often suffer from substantial biases due to inaccurate simulations of complex ecological processes. We introduce a set of scaling factors (parameters) to an ecological model on the basis of plant functional type (PFT) and latitudes. A global carbon assimilation system (GCAS-DOM) is developed by employing a dual optimization method (DOM) to invert the time-dependent ecological model parameter state and the net carbon flux state simultaneously. We use GCAS-DOM to estimate the global distribution of the CO2 flux on 1° × 1° grid cells for the period from 2001 to 2007. Results show that land and ocean absorb -3.63 ± 0.50 and -1.82 ± 0.16 Pg C yr-1, respectively. North America, Europe and China contribute -0.98 ± 0.15, -0.42 ± 0.08 and -0.20 ± 0.29 Pg C yr-1, respectively. The uncertainties in the flux after optimization by GCAS-DOM have been remarkably reduced by more than 60%. Through parameter optimization, GCAS-DOM can provide improved estimates of the carbon flux for each PFT. Coniferous forest (-0.97 ± 0.27 Pg C yr-1) is the largest contributor to the global carbon sink. Fluxes of once-dominant deciduous forest generated by the Boreal Ecosystems Productivity Simulator (BEPS) are reduced to -0.78 ± 0.23 Pg C yr-1, the third largest carbon sink.

  16. Atlantic Tropical Cyclogenetic Processes During SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System

    Science.gov (United States)

    Reale, Oreste; Lau, William K.; Kim, Kyu-Myong; Brin, Eugenia

    2009-01-01

    This article investigates the role of the Saharan air layer (SAL) in tropical cyclogenetic processes associated with a nondeveloping and a developing African easterly wave observed during the Special Observation Period (SOP-3) phase of the 2006 NASA African. Monsoon Multidisciplinary Analyses (NAMMA). The two waves are chosen because they both interact heavily with Saharan air. A glottal data assimilation and forecast system, the NASA Goddard Earth Observing System. version 5 (GEOS-5), is being run to produce a set of high-9 uality global analyses, inclusive of all observations used operationally but with additional satellite information. In particular, following previous works by the same authors, the duality-controlled data from the Atmospheric Infrared Sounder (AIRS) used to produce these analyses have a better coverage than the one adopted by operational centers. From these improved analyses, two sets of 31 five-day high-resolution forecasts, at horizontal resolutions of both half and quarter degrees, are produced. Results indicate that very steep moisture gradients are associated with the SAL in forecasts and analyses, even at great distances from their source over the Sahara. In addition, a thermal dipole in the vertiieat (warm above, cool below) is present in the nondeveloping case. The Moderate Resolution Imaging Spoctroradiometer (MODIS) aboard NASA's Terra and Aqua satellites shows that aerosol optical thickness, indicative of more dust as opposed to other factors, is higher in the nondeveloping case. Altogether, results suggest that the radiative effect of dust may play some role in producing a thermal structure less favorable to cyclogenesis. Results also indicate that only global horizontal resolutions on the order of 20-30 km can capture the large-scale transport and the tine thermal structure of the SAL, inclusive of the sharp moisture gradients, reproducing the effect of tropical cyclone suppression that has been hypothesized by previous authors

  17. Carbon and nitrogen assimilation in deep subseafloor microbial cells

    OpenAIRE

    Morono, Yuki; Terada, Takeshi; Nishizawa, Manabu; Ito, Motoo; Hillion, François; Takahata, Naoto; Sano, Yuji; Inagaki, Fumio

    2011-01-01

    Remarkable numbers of microbial cells have been observed in global shallow to deep subseafloor sediments. Accumulating evidence indicates that deep and ancient sediments harbor living microbial life, where the flux of nutrients and energy are extremely low. However, their physiology and energy requirements remain largely unknown. We used stable isotope tracer incubation and nanometer-scale secondary ion MS to investigate the dynamics of carbon and nitrogen assimilation activities in individua...

  18. Key aspects of stratospheric tracer modeling using assimilated winds

    Directory of Open Access Journals (Sweden)

    B. Bregman

    2006-01-01

    Full Text Available This study describes key aspects of global chemistry-transport models and their impact on stratospheric tracer transport. We concentrate on global models that use assimilated winds from numerical weather predictions, but the results also apply to tracer transport in general circulation models. We examined grid resolution, numerical diffusion, air parcel dispersion, the wind or mass flux update frequency, and time interpolation. The evaluation is performed with assimilated meteorology from the "operational analyses or operational data" (OD from the European Centre for Medium-Range Weather Forecasts (ECMWF. We also show the effect of the mass flux update frequency using the ECMWF 40-year re-analyses (ERA40. We applied the three-dimensional chemistry-transport Tracer Model version 5 (TM5 and a trajectory model and performed several diagnoses focusing on different transport regimes. Covering different time and spatial scales, we examined (1 polar vortex dynamics during the Arctic winter, (2 the large-scale stratospheric meridional circulation, and (3 air parcel dispersion in the tropical lower stratosphere. Tracer distributions inside the Arctic polar vortex show considerably worse agreement with observations when the model grid resolution in the polar region is reduced to avoid numerical instability. The results are sensitive to the diffusivity of the advection. Nevertheless, the use of a computational cheaper but diffusive advection scheme is feasible for tracer transport when the horizontal grid resolution is equal or smaller than 1 degree. The use of time interpolated winds improves the tracer distributions, particularly in the middle and upper stratosphere. Considerable improvement is found both in the large-scale tracer distribution and in the polar regions when the update frequency of the assimilated winds is increased from 6 to 3 h. It considerably reduces the vertical dispersion of air parcels in the tropical lower stratosphere. Strong

  19. Data Assimilation using observed streamflow and remotely-sensed soil moisture for improving sub-seasonal-to-seasonal forecasting

    Science.gov (United States)

    Arumugam, S.; Mazrooei, A.; Lakshmi, V.; Wood, A.

    2017-12-01

    Subseasonal-to-seasonal (S2S) forecasts of soil moisture and streamflow provides critical information for water and agricultural systems to support short-term planning and mangement. This study evaluates the role of observed streamflow and remotely-sensed soil moisture from SMAP (Soil Moisture Active Passive) mission in improving S2S streamflow and soil moisture forecasting using data assimilation (DA). We first show the ability to forecast soil moisture at monthly-to-seaasonal time scale by forcing climate forecasts with NASA's Land Information System and then compares the developed soil moisture forecast with the SMAP data over the Southeast US. Our analyses show significant skill in forecasting real-time soil moisture over 1-3 months using climate information. We also show that the developed soil moisture forecasts capture the observed severe drought conditions (2007-2008) over the Southeast US. Following that, we consider both SMAP data and observed streamflow for improving S2S streamflow and soil moisture forecasts for a pilot study area, Tar River basin, in NC. Towards this, we consider variational assimilation (VAR) of gauge-measured daily streamflow data in improving initial hydrologic conditions of Variable Infiltration Capacity (VIC) model. The utility of data assimilation is then assessed in improving S2S forecasts of streamflow and soil moisture through a retrospective analyses. Furthermore, the optimal frequency of data assimilation and optimal analysis window (number of past observations to use) are also assessed in order to achieve the maximum improvement in S2S forecasts of streamflow and soil moisture. Potential utility of updating initial conditions using DA and providing skillful forcings are also discussed.

  20. Data Assimilation for Applied Meteorology

    Science.gov (United States)

    Haupt, S. E.

    2012-12-01

    Although atmospheric models provide a best estimate of the future state of the atmosphere, due to sensitivity to initial condition, it is difficult to predict the precise future state. For applied problems, however, users often depend on having accurate knowledge of that future state. To improve prediction of a particular realization of an evolving flow field requires knowledge of the current state of that field and assimilation of local observations into the model. This talk will consider how dynamic assimilation can help address the concerns of users of atmospheric forecasts. First, we will look at the value of assimilation for the renewable energy industry. If the industry decision makers can have confidence in the wind and solar power forecasts, they can build their power allocations around the expected renewable resource, saving money for the ratepayers as well as reducing carbon emissions. We will assess the value to that industry of assimilating local real-time observations into the model forecasts and the value that is provided. The value of the forecasts with assimilation is important on both short (several hour) to medium range (within two days). A second application will be atmospheric transport and dispersion problems. In particular, we will look at assimilation of concentration data into a prediction model. An interesting aspect of this problem is that the dynamics are a one-way coupled system, with the fluid dynamic equations affecting the concentration equation, but not vice versa. So when the observations are of the concentration, one must infer the fluid dynamics. This one-way coupled system presents a challenge: one must first infer the changes in the flow field from observations of the contaminant, then assimilate that to recover both the advecting flow and information on the subgrid processes that provide the mixing. To accomplish such assimilation requires a robust method to match the observed contaminant field to that modeled. One approach is

  1. Data Assimilation in Integrated and Distributed Hydrological Models

    DEFF Research Database (Denmark)

    Zhang, Donghua

    processes and provide simulations in refined temporal and spatial resolutions. Recent developments in measurement and sensor technologies have significantly improved the coverage, quality, frequency and diversity of hydrological observations. Data assimilation provides a great potential in relation...... point of view, different assimilation methodologies and techniques have been developed or customized to better serve hydrological assimilation. From the application point of view, real data and real-world complex catchments are used with the focus of investigating the models’ improvements with data...... a variety of model uncertainty sources and scales. Next the groundwater head assimilation experiment was tested in a much more complex catchment with assimilation of biased real observations. In such cases, the bias-aware assimilation method significantly outperforms the standard assimilation method...

  2. A Computational Framework for Quantifying and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation

    Science.gov (United States)

    Cioaca, Alexandru

    A deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data assimila- tion is a rigorous procedure to fuse information from a priori knowledge of the system state, the physical laws governing the evolution of the system, and real measurements, all with associated error statistics. Data assimilation produces best (a posteriori) estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data assimilation raises several important scientific questions related to optimal sensor network design, quantification of data impact, pruning redundant data, and identifying the most beneficial additional observations. These questions originate in operational data assimilation practice, and have started to attract considerable interest in the recent past. This dissertation advances the state of knowledge in four dimensional variational (4D-Var) data assimilation by developing, implementing, and validating a novel computational framework for estimating observation impact and for optimizing sensor networks. The framework builds on the powerful methodologies of second-order adjoint modeling and the 4D-Var sensitivity equations. Efficient computational approaches for quantifying the observation impact include matrix free linear algebra algorithms and low-rank approximations of the sensitivities to observations. The sensor network configuration problem is formulated as a meta-optimization problem. Best values for parameters such as sensor location are obtained by optimizing a performance criterion, subject to the constraint posed by the 4D-Var optimization. Tractable computational solutions to this "optimization-constrained" optimization problem are provided. The results of this work can be directly applied to the deployment of intelligent sensors and adaptive observations, as well as

  3. IASI hyperspectral radiances in the NCMRWF 4D-VAR assimilation system: OSE

    Science.gov (United States)

    Sharma, Priti; Indira Rani, S.; Mallick, Swapan; Srinivas, D.; George, John P.; Dasgupta, Munmun

    2016-04-01

    Accuracy of global NWP depends more on the contribution of satellite data than the surface based observations. This is achieved through the better usage of satellite data within the data assimilation system. Efforts are going on at NCMRWF to add more and more satellite data in the assimilation system both from Indian and international satellites in geostationary and polar orbits. Impact of the new dataset is assessed through Observation System Experiments (OSEs), through which the impact of the data is evaluated comparing the forecast output with that of a control run. This paper discusses one such OSEs with Infrared Atmospheric Sounder Interferometer (IASI) onboard MetOp-A and B. IASI is the main payload instrument for the purpose of supporting NWP. IASI provides information on the vertical structure of the atmospheric temperature and humidity with an accuracy of 1K and a vertical resolution of 1 km, which is necessary to improve NWP. IASI measures the radiance emitted from the Earth in 8641 channels, covering the spectral interval 645-2760 cm-1. The high volume data resulting from IASI presents many challenges, particularly in the area of assimilation. Out of these 8641 channels, 314 channels are selected depending on the relevance of information in each channel to assimilate in the NCMRWF 4D-VAR assimilation system. Studies show that the use of IASI data in NWP accounts for 40% of the impact of all satellite observations in the NWP forecasts, especially microwave and hyperspectral infrared sounding techniques are found to give the largest impacts

  4. Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

    Science.gov (United States)

    Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

    2010-01-01

    Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation

  5. A study of regional-scale aerosol assimilation using a Stretch-NICAM

    Science.gov (United States)

    Misawa, S.; Dai, T.; Schutgens, N.; Nakajima, T.

    2013-12-01

    Although aerosol is considered to be harmful to human health and it became a social issue, aerosol models and emission inventories include large uncertainties. In recent studies, data assimilation is applied to aerosol simulation to get more accurate aerosol field and emission inventory. Most of these studies, however, are carried out only on global scale, and there are only a few researches about regional scale aerosol assimilation. In this study, we have created and verified an aerosol assimilation system on regional scale, in hopes to reduce an error associated with the aerosol emission inventory. Our aerosol assimilation system has been developed using an atmospheric climate model, NICAM (Non-hydrostaric ICosahedral Atmospheric Model; Satoh et al., 2008) with a stretch grid system and coupled with an aerosol transport model, SPRINTARS (Takemura et al., 2000). Also, this assimilation system is based on local ensemble transform Kalman filter (LETKF). To validate this system, we used a simulated observational data by adding some artificial errors to the surface aerosol fields constructed by Stretch-NICAM-SPRINTARS. We also included a small perturbation in original emission inventory. This assimilation with modified observational data and emission inventory was performed in Kanto-plane region around Tokyo, Japan, and the result indicates the system reducing a relative error of aerosol concentration by 20%. Furthermore, we examined a sensitivity of the aerosol assimilation system by varying the number of total ensemble (5, 10 and 15 ensembles) and local patch (domain) size (radius of 50km, 100km and 200km), both of which are the tuning parameters in LETKF. The result of the assimilation with different ensemble number 5, 10 and 15 shows that the larger the number of ensemble is, the smaller the relative error become. This is consistent with ensemble Kalman filter theory and imply that this assimilation system works properly. Also we found that assimilation system

  6. Data-based perfect-deficit approach to understanding climate extremes and forest carbon assimilation capacity

    International Nuclear Information System (INIS)

    Wei, Suhua; Yi, Chuixiang; Hendrey, George; Eaton, Timothy; Rustic, Gerald; Wang, Shaoqiang; Liu, Heping; Krakauer, Nir Y; Wang, Weiguo; Desai, Ankur R; Montagnani, Leonardo; Tha Paw U, Kyaw; Falk, Matthias; Black, Andrew; Bernhofer, Christian; Grünwald, Thomas; Laurila, Tuomas; Cescatti, Alessandro; Moors, Eddy

    2014-01-01

    Several lines of evidence suggest that the warming climate plays a vital role in driving certain types of extreme weather. The impact of warming and of extreme weather on forest carbon assimilation capacity is poorly known. Filling this knowledge gap is critical towards understanding the amount of carbon that forests can hold. Here, we used a perfect-deficit approach to identify forest canopy photosynthetic capacity (CPC) deficits and analyze how they correlate to climate extremes, based on observational data measured by the eddy covariance method at 27 forest sites over 146 site-years. We found that droughts severely affect the carbon assimilation capacities of evergreen broadleaf forest (EBF) and deciduous broadleaf forest. The carbon assimilation capacities of Mediterranean forests were highly sensitive to climate extremes, while marine forest climates tended to be insensitive to climate extremes. Our estimates suggest an average global reduction of forest CPC due to unfavorable climate extremes of 6.3 Pg C (∼5.2% of global gross primary production) per growing season over 2001–2010, with EBFs contributing 52% of the total reduction

  7. Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture

    Science.gov (United States)

    Martens, B.; Miralles, D.; Lievens, H.; Fernández-Prieto, D.; Verhoest, N. E. C.

    2016-06-01

    Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite soil moisture observations from the Soil Moisture and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite soil moisture that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010-December 2013. SMOS soil moisture observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface soil moisture and evaporation are validated against soil moisture probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite soil moisture products. Results indicate that the modelled soil moisture from GLEAM can be improved through the assimilation of SMOS soil moisture: the average correlation coefficient between in situ measurements and the modelled soil moisture over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of soil moisture data

  8. Is It Possible to Speak English Without Thinking American?: On Globalization and the Determinants of Cultural Assimilation

    OpenAIRE

    Alberto E. Chong

    2006-01-01

    Based on research in linguistics and psychology I use language speech as a reflection of acculturation. I use individual and city-level data from the Lake Ontario area in Canada and study the determinants of cultural assimilation. I focus on education, age, income, and in particular, on some variables typically discussed when globalization issues come up, such as immigration, television viewing, borders, and residence history of the individuals. I find that actual contact does matter as a det...

  9. Effects of Model Chemistry and Data Biases on Stratospheric Ozone Assimilation

    National Research Council Canada - National Science Library

    Coy, L; Allen, D. R; Eckermann, S. D; McCormack, J. P; Stajner, I; Hogan, T. F

    2007-01-01

    .... In this study, O-F statistics from the Global Ozone Assimilation Testing System (GOATS) are used to examine how ozone assimilation products and their associated O-F statistics depend on input data biases and ozone photochemistry parameterizations (OPP...

  10. Data Assimilation and Adjusted Spherical Harmonic Model of VTEC Map over Thailand

    Science.gov (United States)

    Klinngam, Somjai; Maruyama, Takashi; Tsugawa, Takuya; Ishii, Mamoru; Supnithi, Pornchai; Chiablaem, Athiwat

    2016-07-01

    The global navigation satellite system (GNSS) and high frequency (HF) communication are vulnerable to the ionospheric irregularities, especially when the signal travels through the low-latitude region and around the magnetic equator known as equatorial ionization anomaly (EIA) region. In order to study the ionospheric effects to the communications performance in this region, the regional map of the observed total electron content (TEC) can show the characteristic and irregularities of the ionosphere. In this work, we develop the two-dimensional (2D) map of vertical TEC (VTEC) over Thailand using the adjusted spherical harmonic model (ASHM) and the data assimilation technique. We calculate the VTEC from the receiver independent exchange (RINEX) files recorded by the dual-frequency global positioning system (GPS) receivers on July 8th, 2012 (quiet day) at 12 stations around Thailand: 0° to 25°E and 95°N to 110°N. These stations are managed by Department of Public Works and Town & Country Planning (DPT), Thailand, and the South East Asia Low-latitude ionospheric Network (SEALION) project operated by National Institute of Information and Communications Technology (NICT), Japan, and King Mongkut's Institute of Technology Ladkrabang (KMITL). We compute the median observed VTEC (OBS-VTEC) in the grids with the spatial resolution of 2.5°x5° in latitude and longitude and time resolution of 2 hours. We assimilate the OBS-VTEC with the estimated VTEC from the International Reference Ionosphere model (IRI-VTEC) as well as the ionosphere map exchange (IONEX) files provided by the International GNSS Service (IGS-VTEC). The results show that the estimation of the 15-degree ASHM can be improved when both of IRI-VTEC and IGS-VTEC are weighted by the latitude-dependent factors before assimilating with the OBS-VTEC. However, the IRI-VTEC assimilation can improve the ASHM estimation more than the IGS-VTEC assimilation. Acknowledgment: This work is partially funded by the

  11. Ozone data assimilation with GEOS-Chem: a comparison between 3-D-Var, 4-D-Var, and suboptimal Kalman filter approaches

    Science.gov (United States)

    Singh, K.; Sandu, A.; Bowman, K. W.; Parrington, M.; Jones, D. B. A.; Lee, M.

    2011-08-01

    Chemistry transport models determine the evolving chemical state of the atmosphere by solving the fundamental equations that govern physical and chemical transformations subject to initial conditions of the atmospheric state and surface boundary conditions, e.g., surface emissions. The development of data assimilation techniques synthesize model predictions with measurements in a rigorous mathematical framework that provides observational constraints on these conditions. Two families of data assimilation methods are currently widely used: variational and Kalman filter (KF). The variational approach is based on control theory and formulates data assimilation as a minimization problem of a cost functional that measures the model-observations mismatch. The Kalman filter approach is rooted in statistical estimation theory and provides the analysis covariance together with the best state estimate. Suboptimal Kalman filters employ different approximations of the covariances in order to make the computations feasible with large models. Each family of methods has both merits and drawbacks. This paper compares several data assimilation methods used for global chemical data assimilation. Specifically, we evaluate data assimilation approaches for improving estimates of the summertime global tropospheric ozone distribution in August 2006 based on ozone observations from the NASA Tropospheric Emission Spectrometer and the GEOS-Chem chemistry transport model. The resulting analyses are compared against independent ozonesonde measurements to assess the effectiveness of each assimilation method. All assimilation methods provide notable improvements over the free model simulations, which differ from the ozonesonde measurements by about 20 % (below 200 hPa). Four dimensional variational data assimilation with window lengths between five days and two weeks is the most accurate method, with mean differences between analysis profiles and ozonesonde measurements of 1-5 %. Two sequential

  12. Improving streamflow simulations and forecasting performance of SWAT model by assimilating remotely sensed soil moisture observations

    Science.gov (United States)

    Patil, Amol; Ramsankaran, RAAJ

    2017-12-01

    This article presents a study carried out using EnKF based assimilation of coarser-scale SMOS soil moisture retrievals to improve the streamflow simulations and forecasting performance of SWAT model in a large catchment. This study has been carried out in Munneru river catchment, India, which is about 10,156 km2. In this study, an EnkF based new approach is proposed for improving the inherent vertical coupling of soil layers of SWAT hydrological model during soil moisture data assimilation. Evaluation of the vertical error correlation obtained between surface and subsurface layers indicates that the vertical coupling can be improved significantly using ensemble of soil storages compared to the traditional static soil storages based EnKF approach. However, the improvements in the simulated streamflow are moderate, which is due to the limitations in SWAT model in reflecting the profile soil moisture updates in surface runoff computations. Further, it is observed that the durability of streamflow improvements is longer when the assimilation system effectively updates the subsurface flow component. Overall, the results of the present study indicate that the passive microwave-based coarser-scale soil moisture products like SMOS hold significant potential to improve the streamflow estimates when assimilating into large-scale distributed hydrological models operating at a daily time step.

  13. Development of airborne remote sensing data assimilation system

    International Nuclear Information System (INIS)

    Gudu, B R; Bi, H Y; Wang, H Y; Qin, S X; Ma, J W

    2014-01-01

    In this paper, an airborne remote sensing data assimilation system for China Airborne Remote Sensing System is introduced. This data assimilation system is composed of a land surface model, data assimilation algorithms, observation data and fundamental parameters forcing the land surface model. In this data assimilation system, Variable Infiltration Capacity hydrologic model is selected as the land surface model, which also serves as the main framework of the system. Three-dimensional variation algorithm, four-dimensional variation algorithms, ensemble Kalman filter and Particle filter algorithms are integrated in this system. Observation data includes ground observations and remotely sensed data. The fundamental forcing parameters include soil parameters, vegetation parameters and the meteorological data

  14. Technical Report Series on Global Modeling and Data Assimilation. Volume 40; Soil Moisture Active Passive (SMAP) Project Assessment Report for the Beta-Release L4_SM Data Product

    Science.gov (United States)

    Koster, Randal D.; Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Liu, Qing; Colliander, Andreas; Conaty, Austin; Jackson, Thomas; Kimball, John

    2015-01-01

    During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public beta release scheduled for 30 October 2015. The primary objective of the beta release is to allow users to familiarize themselves with the data product before the validated product becomes available. The beta release also allows users to conduct their own assessment of the data and to provide feedback to the L4_SM science data product team. The assessment of the L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 1 validation and supports the beta release of the data. The validation against

  15. Assimilation of stratospheric ozone in the chemical transport model STRATAQ

    Directory of Open Access Journals (Sweden)

    B. Grassi

    2004-09-01

    Full Text Available We describe a sequential assimilation approach useful for assimilating tracer measurements into a three-dimensional chemical transport model (CTM of the stratosphere. The numerical code, developed largely according to Kha00, uses parameterizations and simplifications allowing assimilation of sparse observations and the simultaneous evaluation of analysis errors, with reasonable computational requirements. Assimilation parameters are set by using χ2 and OmF (Observation minus Forecast statistics. The CTM used here is a high resolution three-dimensional model. It includes a detailed chemical package and is driven by UKMO (United Kingdom Meteorological Office analyses. We illustrate the method using assimilation of Upper Atmosphere Research Satellite/Microwave Limb Sounder (UARS/MLS ozone observations for three weeks during the 1996 antarctic spring. The comparison of results from the simulations with TOMS (Total Ozone Mapping Spectrometer measurements shows improved total ozone fields due to assimilation of MLS observations. Moreover, the assimilation gives indications on a possible model weakness in reproducing polar ozone values during springtime.

  16. Assimilation of stratospheric ozone in the chemical transport model STRATAQ

    Directory of Open Access Journals (Sweden)

    B. Grassi

    2004-09-01

    Full Text Available We describe a sequential assimilation approach useful for assimilating tracer measurements into a three-dimensional chemical transport model (CTM of the stratosphere. The numerical code, developed largely according to Kha00, uses parameterizations and simplifications allowing assimilation of sparse observations and the simultaneous evaluation of analysis errors, with reasonable computational requirements. Assimilation parameters are set by using χ2 and OmF (Observation minus Forecast statistics. The CTM used here is a high resolution three-dimensional model. It includes a detailed chemical package and is driven by UKMO (United Kingdom Meteorological Office analyses. We illustrate the method using assimilation of Upper Atmosphere Research Satellite/Microwave Limb Sounder (UARS/MLS ozone observations for three weeks during the 1996 antarctic spring. The comparison of results from the simulations with TOMS (Total Ozone Mapping Spectrometer measurements shows improved total ozone fields due to assimilation of MLS observations. Moreover, the assimilation gives indications on a possible model weakness in reproducing polar ozone values during springtime.

  17. DATA ASSIMILATION APPROACH FOR FORECAST OF SOLAR ACTIVITY CYCLES

    Energy Technology Data Exchange (ETDEWEB)

    Kitiashvili, Irina N., E-mail: irina.n.kitiashvili@nasa.gov [NASA Ames Research Center, Moffett Field, Mountain View, CA 94035 (United States)

    2016-11-01

    Numerous attempts to predict future solar cycles are mostly based on empirical relations derived from observations of previous cycles, and they yield a wide range of predicted strengths and durations of the cycles. Results obtained with current dynamo models also deviate strongly from each other, thus raising questions about criteria to quantify the reliability of such predictions. The primary difficulties in modeling future solar activity are shortcomings of both the dynamo models and observations that do not allow us to determine the current and past states of the global solar magnetic structure and its dynamics. Data assimilation is a relatively new approach to develop physics-based predictions and estimate their uncertainties in situations where the physical properties of a system are not well-known. This paper presents an application of the ensemble Kalman filter method for modeling and prediction of solar cycles through use of a low-order nonlinear dynamo model that includes the essential physics and can describe general properties of the sunspot cycles. Despite the simplicity of this model, the data assimilation approach provides reasonable estimates for the strengths of future solar cycles. In particular, the prediction of Cycle 24 calculated and published in 2008 is so far holding up quite well. In this paper, I will present my first attempt to predict Cycle 25 using the data assimilation approach, and discuss the uncertainties of that prediction.

  18. Impact of Assimilation of Conventional and Satellite Radiance GTS Observations on Simulation of Mesoscale Convective System Over Southeast India Using WRF-3DVar

    Science.gov (United States)

    Madhulatha, A.; Rajeevan, M.; Bhowmik, S. K. Roy; Das, A. K.

    2018-01-01

    The primary goal of present study is to investigate the impact of assimilation of conventional and satellite radiance observations in simulating the mesoscale convective system (MCS) formed over south east India. An assimilation methodology based on Weather Research and Forecasting model three dimensional variational data assimilation is considered. Few numerical experiments are carried out to examine the individual and combined impact of conventional and non-conventional (satellite radiance) observations. After the successful inclusion of additional observations, strong analysis increments of temperature and moisture fields are noticed and contributed to significant improvement in model's initial fields. The resulting model simulations are able to successfully reproduce the prominent synoptic features responsible for the initiation of MCS. Among all the experiments, the final experiment in which both conventional and satellite radiance observations assimilated has showed considerable impact on the prediction of MCS. The location, genesis, intensity, propagation and development of rain bands associated with the MCS are simulated reasonably well. The biases of simulated temperature, moisture and wind fields at surface and different pressure levels are reduced. Thermodynamic, dynamic and vertical structure of convective cells associated with the passage of MCS are well captured. Spatial distribution of rainfall is fairly reproduced and comparable to TRMM observations. It is demonstrated that incorporation of conventional and satellite radiance observations improved the local and synoptic representation of temperature, moisture fields from surface to different levels of atmosphere. This study highlights the importance of assimilation of conventional and satellite radiances in improving the models initial conditions and simulation of MCS.

  19. Assimilating GRACE terrestrial water storage data into a conceptual hydrology model for the River Rhine

    Science.gov (United States)

    Widiastuti, E.; Steele-Dunne, S. C.; Gunter, B.; Weerts, A.; van de Giesen, N.

    2009-12-01

    Terrestrial water storage (TWS) is a key component of the terrestrial and global hydrological cycles, and plays a major role in the Earth’s climate. The Gravity Recovery and Climate Experiment (GRACE) twin satellite mission provided the first space-based dataset of TWS variations, albeit with coarse resolution and limited accuracy. Here, we examine the value of assimilating GRACE observations into a well-calibrated conceptual hydrology model of the Rhine river basin. In this study, the ensemble Kalman filter (EnKF) and smoother (EnKS) were applied to assimilate the GRACE TWS variation data into the HBV-96 rainfall run-off model, from February 2003 to December 2006. Two GRACE datasets were used, the DMT-1 models produced at TU Delft, and the CSR-RL04 models produced by UT-Austin . Each center uses its own data processing and filtering methods, yielding two different estimates of TWS variations and therefore two sets of assimilated TWS estimates. To validate the results, the model estimated discharge after the data assimilation was compared with measured discharge at several stations. As expected, the updated TWS was generally somewhere between the modeled and observed TWS in both experiments and the variance was also lower than both the prior error covariance and the assumed GRACE observation error. However, the impact on the discharge was found to depend heavily on the assimilation strategy used, in particular on how the TWS increments were applied to the individual storage terms of the hydrology model.

  20. Using Data Assimilation Diagnostics to Assess the SMAP Level-4 Soil Moisture Product

    Science.gov (United States)

    Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

    2018-01-01

    The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx.2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

  1. Assimilation of time-averaged observations in a quasi-geostrophic atmospheric jet model

    Energy Technology Data Exchange (ETDEWEB)

    Huntley, Helga S. [University of Washington, Department of Applied Mathematics, Seattle, WA (United States); University of Delaware, School of Marine Science and Policy, Newark, DE (United States); Hakim, Gregory J. [University of Washington, Department of Atmospheric Sciences, Seattle, WA (United States)

    2010-11-15

    The problem of reconstructing past climates from a sparse network of noisy time-averaged observations is considered with a novel ensemble Kalman filter approach. Results for a sparse network of 100 idealized observations for a quasi-geostrophic model of a jet interacting with a mountain reveal that, for a wide range of observation averaging times, analysis errors are reduced by about 50% relative to the control case without assimilation. Results are robust to changes to observational error, the number of observations, and an imperfect model. Specifically, analysis errors are reduced relative to the control case for observations having errors up to three times the climatological variance for a fixed 100-station network, and for networks consisting of ten or more stations when observational errors are fixed at one-third the climatological variance. In the limit of small numbers of observations, station location becomes critically important, motivating an optimally determined network. A network of fifteen optimally determined observations reduces analysis errors by 30% relative to the control, as compared to 50% for a randomly chosen network of 100 observations. (orig.)

  2. Customer-oriented Data Formats and Services for Global Land Data Assimilation System (GLDAS) Products at the NASA GES DISC

    Science.gov (United States)

    Fang, Hongliang; Beaudoing, Hiroko; Rodell, Matthew; Teng, BIll; Vollmer, Bruce

    2008-01-01

    The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface Models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of NASA Goddard Earth Sciences Data and Information Services Center (GESDISC).

  3. Benefits and Pitfalls of GRACE Terrestrial Water Storage Data Assimilation

    Science.gov (United States)

    Girotto, Manuela

    2018-01-01

    Satellite observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) mission have a coarse resolution in time (monthly) and space (roughly 150,000 sq km at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Nonetheless, data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This presentation illustrates some of the benefits and drawbacks of assimilating TWS observations from GRACE into a land surface model over the continental United States and India. The assimilation scheme yields improved skill metrics for groundwater compared to the no-assimilation simulations. A smaller impact is seen for surface and root-zone soil moisture. Further, GRACE observes TWS depletion associated with anthropogenic groundwater extraction. Results from the assimilation emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems.

  4. Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS)

    Science.gov (United States)

    Chen, Zhuoqi; Chen, Jing M.; Zhang, Shupeng; Zheng, Xiaogu; Ju, Weiming; Mo, Gang; Lu, Xiaoliang

    2017-12-01

    The Global Carbon Assimilation System that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C (Vmax25), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at middle-high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

  5. Multiscale Data Assimilation for Large-Eddy Simulations

    Science.gov (United States)

    Li, Z.; Cheng, X.; Gustafson, W. I., Jr.; Xiao, H.; Vogelmann, A. M.; Endo, S.; Toto, T.

    2017-12-01

    Large-eddy simulation (LES) is a powerful tool for understanding atmospheric turbulence, boundary layer physics and cloud development, and there is a great need for developing data assimilation methodologies that can constrain LES models. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility has been developing the capability to routinely generate ensembles of LES. The LES ARM Symbiotic Simulation and Observation (LASSO) project (https://www.arm.gov/capabilities/modeling/lasso) is generating simulations for shallow convection days at the ARM Southern Great Plains site in Oklahoma. One of major objectives of LASSO is to develop the capability to observationally constrain LES using a hierarchy of ARM observations. We have implemented a multiscale data assimilation (MSDA) scheme, which allows data assimilation to be implemented separately for distinct spatial scales, so that the localized observations can be effectively assimilated to constrain the mesoscale fields in the LES area of about 15 km in width. The MSDA analysis is used to produce forcing data that drive LES. With such LES workflow we have examined 13 days with shallow convection selected from the period May-August 2016. We will describe the implementation of MSDA, present LES results, and address challenges and opportunities for applying data assimilation to LES studies.

  6. Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

    Science.gov (United States)

    Kumar, Sujay V.; Zaitchik, Benjamin F.; Peters-Lidard, Christa D.; Rodell, Matthew; Reichle, Rolf; Li, Bailing; Jasinski, Michael; Mocko, David; Getirana, Augusto; De Lannoy, Gabrielle; hide

    2016-01-01

    The objective of the North American Land Data Assimilation System (NLDAS) is to provide best available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

  7. Mathematical foundations of hybrid data assimilation from a synchronization perspective

    Science.gov (United States)

    Penny, Stephen G.

    2017-12-01

    The state-of-the-art data assimilation methods used today in operational weather prediction centers around the world can be classified as generalized one-way coupled impulsive synchronization. This classification permits the investigation of hybrid data assimilation methods, which combine dynamic error estimates of the system state with long time-averaged (climatological) error estimates, from a synchronization perspective. Illustrative results show how dynamically informed formulations of the coupling matrix (via an Ensemble Kalman Filter, EnKF) can lead to synchronization when observing networks are sparse and how hybrid methods can lead to synchronization when those dynamic formulations are inadequate (due to small ensemble sizes). A large-scale application with a global ocean general circulation model is also presented. Results indicate that the hybrid methods also have useful applications in generalized synchronization, in particular, for correcting systematic model errors.

  8. A new air quality modelling approach at the regional scale using lidar data assimilation

    International Nuclear Information System (INIS)

    Wang, Y.

    2013-01-01

    Assimilation of lidar observations for air quality modelling is investigated via the development of a new model, which assimilates ground-based lidar network measurements using optimal interpolation (OI) in a chemistry transport model. First, a tool for assimilating PM 10 (particulate matter with a diameter lower than 10 μm) concentration measurements on the vertical is developed in the air quality modelling platform POLYPHEMUS. It is applied to western Europe for one month from 15 July to 15 August 2001 to investigate the potential impact of future ground-based lidar networks on analysis and short-term forecasts (the description of the future) of PM 10 . The efficiency of assimilating lidar network measurements is compared to the efficiency of assimilating concentration measurements from the AirBase ground network, which includes about 500 stations in western Europe. A sensitivity study on the number and location of required lidars is also performed to help define an optimal lidar network for PM 10 forecasts. Secondly, a new model for simulating normalised lidar signals (PR 2 ) is developed and integrated in POLYPHEMUS. Simulated lidar signals are compared to hourly ground-based mobile and in-situ lidar observations performed during the MEGAPOLI (Mega-cities: Emissions, urban, regional and Global Atmospheric Pollution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009. It is found that the model correctly reproduces the vertical distribution of aerosol optical properties and their temporal variability. Additionally, two new algorithms for assimilating lidar signals are presented and evaluated during MEGAPOLI. The aerosol simulations without and with lidar data assimilation are evaluated using the AIRPARIF (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and the usefulness of assimilating lidar profiles for aerosol forecasts. Finally

  9. Assimilation of Altimeter Data into a Quasigeostrophic Model of the Gulf Stream System. Part 2; Assimilation Results

    Science.gov (United States)

    Capotondi, Antonietta; Holland, William R.; Malanotte-Rizzoli, Paola

    1995-01-01

    The improvement in the climatological behavior of a numerical model as a consequence of the assimilation of surface data is investigated. The model used for this study is a quasigeostrophic (QG) model of the Gulf Stream region. The data that have been assimilated are maps of sea surface height that have been obtained as the superposition of sea surface height variability deduced from the Geosat altimeter measurements and a mean field constructed from historical hydrographic data. The method used for assimilating the data is the nudging technique. Nudging has been implemented in such a way as to achieve a high degree of convergence of the surface model fields toward the observations. Comparisons of the assimilation results with available in situ observations show a significant improvement in the degree of realism of the climatological model behavior, with respect to the model in which no data are assimilated. The remaining discrepancies in the model mean circulation seem to be mainly associated with deficiencies in the mean component of the surface data that are assimilated. On the other hand, the possibility of building into the model more realistic eddy characteristics through the assimilation of the surface eddy field proves very successful in driving components of the mean model circulation that are in relatively good agreement with the available observations. Comparisons with current meter time series during a time period partially overlapping the Geosat mission show that the model is able to 'correctly' extrapolate the instantaneous surface eddy signals to depths of approximately 1500 m. The correlation coefficient between current meter and model time series varies from values close to 0.7 in the top 1500 m to values as low as 0.1-0.2 in the deep ocean.

  10. Estimating a Global Hydrological Carrying Capacity Using GRACE Observed Water Stress

    Science.gov (United States)

    An, K.; Reager, J. T.; Famiglietti, J. S.

    2013-12-01

    Global population is expected to reach 9 billion people by the year 2050, causing increased demands for water and potential threats to human security. This study attempts to frame the overpopulation problem through a hydrological resources lens by hypothesizing that observed groundwater trends should be directly attributed to human water consumption. This study analyzes the relationships between available blue water, population, and cropland area on a global scale. Using satellite data from NASA's Gravity Recovery and Climate Experiment (GRACE) along with land surface model data from the Global Land Data Assimilation System (GLDAS), a global groundwater depletion trend is isolated, the validity of which has been verified in many regional studies. By using the inherent distributions of these relationships, we estimate the regional populations that have exceeded their local hydrological carrying capacity. Globally, these populations sum to ~3.5 billion people that are living in presently water-stressed or potentially water-scarce regions, and we estimate total cropland is exceeding a sustainable threshold by about 80 million km^2. Key study areas such as the North China Plain, northwest India, and Mexico City were qualitatively chosen for further analysis of regional water resources and policies, based on our distributions of water stress. These case studies are used to verify the groundwater level changes seen in the GRACE trend . Tfor the many populous, arid regions of the world that have already begun to experience the strains of high water demand.he many populous, arid regions of the world have already begun to experience the strains of high water demand. It will take a global cooperative effort of improving domestic and agricultural use efficiency, and summoning a political will to prioritize environmental issues to adapt to a thirstier planet. Global Groundwater Depletion Trend (Mar 2003-Dec 2011)

  11. Paleo Data Assimilation of Pseudo-Tree-Ring-Width Chronologies in a Climate Model

    Science.gov (United States)

    Fallah Hassanabadi, B.; Acevedo, W.; Reich, S.; Cubasch, U.

    2016-12-01

    Using the Time-Averaged Ensemble Kalman Filter (EnKF) and a forward model, we assimilate the pseudo Tree-Ring-Width (TRW) chronologies into an Atmospheric Global Circulation model. This study investigates several aspects of Paleo-Data Assimilation (PDA) within a perfect-model set-up: (i) we test the performance of several forward operators in the framework of a PDA-based climate reconstruction, (ii) compare the PDA-based simulations' skill against the free ensemble runs and (iii) inverstigate the skill of the "online" (with cycling) DA and the "off-line" (no-cycling) DA. In our experiments, the "online" (with cycling) PDA approach did not outperform the "off-line" (no-cycling) one, despite its considerable additional implementation complexity. On the other hand, it was observed that the error reduction achieved by assimilating a particular pseudo-TRW chronology is modulated by the strength of the yearly internal variability of the model at the chronology site. This result might help the dendrochronology community to optimize their sampling efforts.

  12. Impact of assimilation window length on diurnal features in a Mars atmospheric analysis

    Directory of Open Access Journals (Sweden)

    Yongjing Zhao

    2015-05-01

    Full Text Available Effective simulation of diurnal variability is an important aspect of many geophysical data assimilation systems. For the Martian atmosphere, thermal tides are particularly prominent and contribute much to the Martian atmospheric circulation, dynamics and dust transport. To study the Mars diurnal variability and Mars thermal tides, the Geophysical Fluid Dynamics Laboratory Mars Global Climate Model with the 4D-local ensemble transform Kalman filter (4D-LETKF is used to perform an analysis assimilating spacecraft temperature retrievals. We find that the use of a ‘traditional’ 6-hr assimilation cycle induces spurious forcing of a resonantly enhanced semi-diurnal Kelvin waves represented in both surface pressure and mid-level temperature by forming a wave 4 pattern in the diurnal averaged analysis increment that acts as a ‘topographic’ stationary forcing. Different assimilation window lengths in the 4D-LETKF are introduced to remove the artificially induced resonance. It is found that short assimilation window lengths not only remove the spurious resonance, but also push the migrating semi-diurnal temperature variation at 50 Pa closer to the estimated ‘true’ tides even in the absence of a radiatively active water ice cloud parameterisation. In order to compare the performance of different assimilation window lengths, short-term to mid-range forecasts based on the hour 00 and 12 assimilation are evaluated and compared. Results show that during Northern Hemisphere summer, it is not the assimilation window length, but the radiatively active water ice clouds that influence the model prediction. A ‘diurnal bias correction’ that includes bias correction fields dependent on the local time is shown to effectively reduce the forecast root mean square differences between forecasts and observations, compensate for the absence of water ice cloud parameterisation and enhance Martian atmosphere prediction. The implications of these results for

  13. A comparative analysis of UV nadir-backscatter and infrared limb-emission ozone data assimilation

    Directory of Open Access Journals (Sweden)

    R. Dragani

    2016-07-01

    Full Text Available This paper presents a comparative assessment of ultraviolet nadir-backscatter and infrared limb-emission ozone profile assimilation. The Meteorological Operational Satellite A (MetOp-A Global Ozone Monitoring Experiment 2 (GOME-2 nadir and the ENVISAT Michelson Interferometer for Passive Atmospheric Sounding (MIPAS limb profiles, generated by the ozone consortium of the European Space Agency Climate Change Initiative (ESA O3-CCI, were individually added to a reference set of ozone observations and assimilated in the European Centre for Medium-Range Weather Forecasts (ECMWF data assimilation system (DAS. The two sets of resulting analyses were compared with that from a control experiment, only constrained by the reference dataset, and independent, unassimilated observations. Comparisons with independent observations show that both datasets improve the stratospheric ozone distribution. The changes inferred by the limb-based observations are more localized and, in places, more important than those implied by the nadir profiles, albeit they have a much lower number of observations. A small degradation (up to 0.25 mg kg−1 for GOME-2 and 0.5 mg kg−1 for MIPAS in the mass mixing ratio is found in the tropics between 20 and 30 hPa. In the lowermost troposphere below its vertical coverage, the limb data are found to be able to modify the ozone distribution with changes as large as 60 %. Comparisons of the ozone analyses with sonde data show that at those levels the assimilation of GOME-2 leads to about 1 Dobson Unit (DU smaller root mean square error (RMSE than that of MIPAS. However, the assimilation of MIPAS can still improve the quality of the ozone analyses and – with a reduction in the RMSE of up to about 2 DU – outperform the control experiment thanks to its synergistic assimilation with total-column ozone data within the DAS. High vertical resolution ozone profile observations are essential to accurately monitor and

  14. Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations

    Science.gov (United States)

    Kauker, F.; Kaminski, T.; Ricker, R.; Toudal-Pedersen, L.; Dybkjaer, G.; Melsheimer, C.; Eastwood, S.; Sumata, H.; Karcher, M.; Gerdes, R.

    2015-10-01

    The recent thinning and shrinking of the Arctic sea ice cover has increased the interest in seasonal sea ice forecasts. Typical tools for such forecasts are numerical models of the coupled ocean sea ice system such as the North Atlantic/Arctic Ocean Sea Ice Model (NAOSIM). The model uses as input the initial state of the system and the atmospheric boundary condition over the forecasting period. This study investigates the potential of remotely sensed ice thickness observations in constraining the initial model state. For this purpose it employs a variational assimilation system around NAOSIM and the Alfred Wegener Institute's CryoSat-2 ice thickness product in conjunction with the University of Bremen's snow depth product and the OSI SAF ice concentration and sea surface temperature products. We investigate the skill of predictions of the summer ice conditions starting in March for three different years. Straightforward assimilation of the above combination of data streams results in slight improvements over some regions (especially in the Beaufort Sea) but degrades the over-all fit to independent observations. A considerable enhancement of forecast skill is demonstrated for a bias correction scheme for the CryoSat-2 ice thickness product that uses a spatially varying scaling factor.

  15. Evaluation of a Soil Moisture Data Assimilation System Over the Conterminous United States

    Science.gov (United States)

    Bolten, J. D.; Crow, W. T.; Zhan, X.; Reynolds, C. A.; Jackson, T. J.

    2008-12-01

    A data assimilation system has been designed to integrate surface soil moisture estimates from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) with an online soil moisture model used by the USDA Foreign Agriculture Service for global crop estimation. USDA's International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) ingests global soil moisture within a Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS) to provide nowcasts of crop conditions and agricultural-drought. This information is primarily used to derive mid-season crop yield estimates for the improvement of foreign market access for U.S. agricultural products. The CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The integration of AMSR-E observations into the two-layer soil moisture model employed by IPAD can potentially enhance the reliability of the CADRE soil moisture estimates due to AMSR-E's improved repeat time and greater spatial coverage. Assimilation of the AMSR-E soil moisture estimates is accomplished using a 1-D Ensemble Kalman filter (EnKF) at daily time steps. A diagnostic calibration of the filter is performed using innovation statistics by accurately weighting the filter observation and modeling errors for three ranges of vegetation biomass density estimated using historical data from the Advanced Very High Resolution Radiometer (AVHRR). Assessment of the AMSR-E assimilation has been completed for a five year duration over the conterminous United States. To evaluate the ability of the filter to compensate for incorrect precipitation forcing into the model, a data denial approach is employed by comparing soil moisture results obtained from separate model simulations forced with precipitation products of varying uncertainty. An analysis of surface and root-zone anomalies is presented for each

  16. Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content

    Science.gov (United States)

    Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N.; Scholze, Marko

    2018-04-01

    The synthesis of model and observational information using data assimilation can improve our understanding of the terrestrial carbon cycle, a key component of the Earth's climate-carbon system. Here we provide a data assimilation framework for combining observations of solar-induced chlorophyll fluorescence (SIF) and a process-based model to improve estimates of terrestrial carbon uptake or gross primary production (GPP). We then quantify and assess the constraint SIF provides on the uncertainty in global GPP through model process parameters in an error propagation study. By incorporating 1 year of SIF observations from the GOSAT satellite, we find that the parametric uncertainty in global annual GPP is reduced by 73 % from ±19.0 to ±5.2 Pg C yr-1. This improvement is achieved through strong constraint of leaf growth processes and weak to moderate constraint of physiological parameters. We also find that the inclusion of uncertainty in shortwave down-radiation forcing has a net-zero effect on uncertainty in GPP when incorporated into the SIF assimilation framework. This study demonstrates the powerful capacity of SIF to reduce uncertainties in process-based model estimates of GPP and the potential for improving our predictive capability of this uncertain carbon flux.

  17. River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model

    Science.gov (United States)

    Oubanas, Hind; Gejadze, Igor; Malaterre, Pierre-Olivier; Mercier, Franck

    2018-04-01

    The upcoming Surface Water and Ocean Topography satellite mission, to be launched in 2021, will measure river water surface elevation, slope and width, with an unprecedented level of accuracy for a remote sensing tool. This work investigates the river discharge estimation from synthetic SWOT observations, in the presence of strong uncertainties in the model inputs, i.e. the river bathymetry and bed roughness. The estimation problem is solved by a novel variant of the standard variational data assimilation, the '4D-Var' method, involving the full Saint-Venant 1.5D-network hydraulic model SIC2. The assimilation scheme simultaneously estimates the discharge, bed elevation and bed roughness coefficient and is designed to assimilate both satellite and in situ measurements. The method is tested on a 50 km-long reach of the Garonne River during a five-month period of the year 2010, characterized by multiple flooding events. First, the impact of the sampling frequency on discharge estimation is investigated. Secondly, discharge as well as the spatially distributed bed elevation and bed roughness coefficient are determined simultaneously. Results demonstrate feasibility and efficiency of the chosen combination of the estimation method and of the hydraulic model. Assimilation of the SWOT data results into an accurate estimation of the discharge at observation times, and a local improvement in the bed level and bed roughness coefficient. However, the latter estimates are not generally usable for different independent experiments.

  18. Potential of an ensemble Kalman smoother for stratospheric chemical-dynamical data assimilation

    Directory of Open Access Journals (Sweden)

    Thomas Milewski

    2013-02-01

    Full Text Available A new stratospheric ensemble Kalman smoother (EnKS system is introduced, and the potential of assimilating posterior stratospheric observations to better constrain the whole model state at analysis time is investigated. A set of idealised perfect-model Observation System Simulation Experiments (OSSE assimilating synthetic limb-sounding temperature or ozone retrievals are performed with a chemistry–climate model. The impact during the analysis step is characterised in terms of the root mean square error reduction between the forecast state and the analysis state. The performances of (1 a fixed-lag EnKS assimilating observations spread over 48 hours and (2 an ensemble Kalman Filter (EnKF assimilating a denser network of observations are compared with a reference EnKF. The ozone assimilation with EnKS shows a significant additional reduction of analysis error of the order of 10% for dynamical and chemical variables in the extratropical upper troposphere lower stratosphere (UTLS and Polar Vortex regions when compared to the reference EnKF. This reduction has similar magnitude to the one achieved by the denser-network EnKF assimilation. Similarly, the temperature assimilation with EnKS significantly decreases the error in the UTLS for the wind variables like the denser-network EnKF assimilation. However, the temperature assimilation with EnKS has little or no significant impact on the temperature and ozone analyses, whereas the denser-network EnKF shows improvement with respect to the reference EnKF. The different analysis impacts from the assimilation of current and posterior ozone observations indicate the capacity of time-lagged background-error covariances to represent temporal interactions up to 48 hours between variables during the ensemble data assimilation analysis step, and the possibility to use posterior observations whenever additional current observations are unavailable. The possible application of the EnKS for reanalyses is

  19. A simple lightning assimilation technique for improving ...

    Science.gov (United States)

    Convective rainfall is often a large source of error in retrospective modeling applications. In particular, positive rainfall biases commonly exist during summer months due to overactive convective parameterizations. In this study, lightning assimilation was applied in the Kain-Fritsch (KF) convective scheme to improve retrospective simulations using the Weather Research and Forecasting (WRF) model. The assimilation method has a straightforward approach: force KF deep convection where lightning is observed and, optionally, suppress deep convection where lightning is absent. WRF simulations were made with and without lightning assimilation over the continental United States for July 2012, July 2013, and January 2013. The simulations were evaluated against NCEP stage-IV precipitation data and MADIS near-surface meteorological observations. In general, the use of lightning assimilation considerably improves the simulation of summertime rainfall. For example, the July 2012 monthly averaged bias of 6 h accumulated rainfall is reduced from 0.54 to 0.07 mm and the spatial correlation is increased from 0.21 to 0.43 when lightning assimilation is used. Statistical measures of near-surface meteorological variables also are improved. Consistent improvements also are seen for the July 2013 case. These results suggest that this lightning assimilation technique has the potential to substantially improve simulation of warm-season rainfall in retrospective WRF applications. The

  20. Impact of advanced technology microwave sounder data in the NCMRWF 4D-VAR data assimilation system

    Science.gov (United States)

    Rani, S. Indira; Srinivas, D.; Mallick, Swapan; George, John P.

    2016-05-01

    This study demonstrates the added benefits of assimilating the Advanced Technology Microwave Sounder (ATMS) radiances from the Suomi-NPP satellite in the NCMRWF Unified Model (NCUM). ATMS is a cross-track scanning microwave radiometer inherited the legacy of two very successful instrument namely, Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS). ATMS has 22 channels: 11 temperature sounding channels around 50-60 GHz oxygen band and 6 moisture sounding channels around the 183GHz water vapour band in addition to 5 channels sensitive to the surface in clear conditions, or to water vapour, rain, and cloud when conditions are not clear (at 23, 31, 50, 51 and 89 GHz). Before operational assimilation of any new observation by NWP centres it is standard practice to assess data quality with respect to NWP model background (short-forecast) fields. Quality of all channels is estimated against the model background and the biases are computed and compared against that from the similar observations. The impact of the ATMS data on global analyses and forecasts is tested by adding the ATMS data in the NCUM Observation Processing system (OPS) and 4D-Var variational assimilation (VAR) system. This paper also discusses the pre-operational numerical experiments conducted to assess the impact of ATMS radiances in the NCUM assimilation system. It is noted that the performance of ATMS is stable and it contributes to the performance of the model, complimenting observations from other instruments.

  1. Modeling ionospheric pre-reversal enhancement and plasma bubble growth rate using data assimilation

    Science.gov (United States)

    Rajesh, P. K.; Lin, C. C. H.; Chen, C. H.; Matsuo, T.

    2017-12-01

    We report that assimilating total electron content (TEC) into a coupled thermosphere-ionosphere model by using the ensemble Kalman filter results in improved specification and forecast of eastward pre-reversal enhancement (PRE) electric field (E-field). Through data assimilation, the ionospheric plasma density, thermospheric winds, temperature and compositions are adjusted simultaneously. The improvement of dusk-side PRE E-field over the prior state is achieved primarily by intensification of eastward neutral wind. The improved E-field promotes a stronger plasma fountain and deepens the equatorial trough. As a result, the horizontal gradients of Pedersen conductivity and eastward wind are increased due to greater zonal electron density gradient and smaller ion drag at dusk, respectively. Such modifications provide preferable conditions and obtain a strengthened PRE magnitude closer to the observation. The adjustment of PRE E-field is enabled through self-consistent thermosphere and ionosphere coupling processes captured in the model. The assimilative outputs are further utilized to calculate the flux tube integrated Rayleigh-Taylor instability growth rate during March 2015 for investigation of global plasma bubble occurrence. Significant improvements in the calculated growth rates could be achieved because of the improved update of zonal electric field in the data assimilation forecast. The results suggest that realistic estimate or prediction of plasma bubble occurrence could be feasible by taking advantage of the data assimilation approach adopted in this work.

  2. Climatic features of the Red Sea from a regional assimilative model

    KAUST Repository

    Viswanadhapalli, Yesubabu

    2016-08-16

    The Advanced Research version of Weather Research and Forecasting (WRF-ARW) model was used to generate a downscaled, 10-km resolution regional climate dataset over the Red Sea and adjacent region. The model simulations are performed based on two, two-way nested domains of 30- and 10-km resolutions assimilating all conventional observations using a cyclic three-dimensional variational approach over an initial 12-h period. The improved initial conditions are then used to generate regional climate products for the following 24 h. We combined the resulting daily 24-h datasets to construct a 15-year Red Sea atmospheric downscaled product from 2000 to 2014. This 15-year downscaled dataset is evaluated via comparisons with various in situ and gridded datasets. Our analysis indicates that the assimilated model successfully reproduced the spatial and temporal variability of temperature, wind, rainfall, relative humidity and sea level pressure over the Red Sea region. The model also efficiently simulated the seasonal and monthly variability of wind patterns, the Red Sea Convergence Zone and associated rainfall. Our results suggest that dynamical downscaling and assimilation of available observations improve the representation of regional atmospheric features over the Red Sea compared to global analysis data from the National Centers for Environmental Prediction. We use the dataset to describe the atmospheric climatic conditions over the Red Sea region. © 2016 Royal Meteorological Society.

  3. Three-dimensional data assimilation and reanalysis of radiation belt electrons: Observations over two solar cycles, and operational forecasting.

    Science.gov (United States)

    Kellerman, A. C.; Shprits, Y.; Kondrashov, D. A.; Podladchikova, T.; Drozdov, A.; Subbotin, D.; Makarevich, R. A.; Donovan, E.; Nagai, T.

    2015-12-01

    Understanding of the dynamics in Earth's radiation belts is critical to accurate modeling and forecasting of space weather conditions, both which are important for design, and protection of our space-borne assets. In the current study, we utilize the Versatile Electron Radiation Belt (VERB) code, multi-spacecraft measurements, and a split-operator Kalman filter to recontructe the global state of the radiation belt system in the CRRES era and the current era. The reanalysis has revealed a never before seen 4-belt structure in the radiation belts during the March 1991 superstorm, and highlights several important aspects in regards to the the competition between the source, acceleration, loss, and transport of particles. In addition to the above, performing reanalysis in adiabatic coordinates relies on specification of the Earth's magnetic field, and associated observational, and model errors. We determine the observational errors for the Kalman filter directly from cross-spacecraft phase-space density (PSD) conjunctions, and obtain the error in VERB by comparison with reanalysis over a long time period. Specification of errors associated with several magnetic field models provides an important insight into the applicability of such models for radiation belt research. The comparison of CRRES area reanalysis with Van Allen Probe era reanalysis allows us to perform a global comparison of the dynamics of the radiation belts during different parts of the solar cycle and during different solar cycles. The data assimilative model is presently used to perform operational forecasts of the radiation belts (http://rbm.epss.ucla.edu/realtime-forecast/).

  4. Kalman filters for assimilating near-surface observations in the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

    Science.gov (United States)

    Medina, H.; Romano, N.; Chirico, G. B.

    2012-12-01

    We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.

  5. Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction

    Directory of Open Access Journals (Sweden)

    C. L. Keppenne

    2005-01-01

    Full Text Available To compensate for a poorly known geoid, satellite altimeter data is usually analyzed in terms of anomalies from the time mean record. When such anomalies are assimilated into an ocean model, the bias between the climatologies of the model and data is problematic. An ensemble Kalman filter (EnKF is modified to account for the presence of a forecast-model bias and applied to the assimilation of TOPEX/Poseidon (T/P altimeter data. The online bias correction (OBC algorithm uses the same ensemble of model state vectors to estimate biased-error and unbiased-error covariance matrices. Covariance localization is used but the bias covariances have different localization scales from the unbiased-error covariances, thereby accounting for the fact that the bias in a global ocean model could have much larger spatial scales than the random error.The method is applied to a 27-layer version of the Poseidon global ocean general circulation model with about 30-million state variables. Experiments in which T/P altimeter anomalies are assimilated show that the OBC reduces the RMS observation minus forecast difference for sea-surface height (SSH over a similar EnKF run in which OBC is not used. Independent in situ temperature observations show that the temperature field is also improved. When the T/P data and in situ temperature data are assimilated in the same run and the configuration of the ensemble at the end of the run is used to initialize the ocean component of the GMAO coupled forecast model, seasonal SSH hindcasts made with the coupled model are generally better than those initialized with optimal interpolation of temperature observations without altimeter data. The analysis of the corresponding sea-surface temperature hindcasts is not as conclusive.

  6. Development of a data assimilation algorithm

    DEFF Research Database (Denmark)

    Thomsen, Per Grove; Zlatev, Zahari

    2008-01-01

    It is important to incorporate all available observations when large-scale mathematical models arising in different fields of science and engineering are used to study various physical and chemical processes. Variational data assimilation techniques can be used in the attempts to utilize efficien......It is important to incorporate all available observations when large-scale mathematical models arising in different fields of science and engineering are used to study various physical and chemical processes. Variational data assimilation techniques can be used in the attempts to utilize...... assimilation technique is applied. Therefore, it is important to study the interplay between the three components of the variational data assimilation techniques as well as to apply powerful parallel computers in the computations. Some results obtained in the search for a good combination of numerical methods...... computers, Mathematics and Computers in Simulation, 65 (2004) 557–577, Z. Zlatev, Computer Treatment of Large Air Pollution Models, Kluwer Academic Publishers, Dordrecht, Boston, London, 1995]. The ideas are rather general and can easily be applied in connection with other mathematical models....

  7. Preparing for the Future Nankai Trough Tsunami: A Data Assimilation and Inversion Analysis From Various Observational Systems

    Science.gov (United States)

    Mulia, Iyan E.; Inazu, Daisuke; Waseda, Takuji; Gusman, Aditya Riadi

    2017-10-01

    The future Nankai Trough tsunami is one of the imminent threats to the Japanese coastal communities that could potentially cause a catastrophic event. As a part of the countermeasure efforts for such an occurrence, this study analyzes the efficacy of combining tsunami data assimilation (DA) and waveform inversion (WI). The DA is used to continuously refine a wavefield model whereas the WI is used to estimate the tsunami source. We consider a future scenario of the Nankai Trough tsunami recorded at various observational systems, including ocean bottom pressure (OBP) gauges, global positioning system (GPS) buoys, and ship height positioning data. Since most of the OBP gauges are located inside the source region, the recorded tsunami signals exhibit significant offsets from surface measurements due to coseismic seafloor deformation effects. Such biased data are not applicable to the standard DA, but can be taken into account in the WI. On the other hand, the use of WI for the ship data may not be practical because a considerably large precomputed tsunami database is needed to cope with the spontaneous ship locations. The DA is more suitable for such an observational system as it can be executed sequentially in time and does not require precomputed scenarios. Therefore, the combined approach of DA and WI allows us to concurrently make use of all observational resources. Additionally, we introduce a bias correction scheme for the OBP data to improve the accuracy, and an adaptive thinning of observations to determine the efficient number of observations.

  8. A composite state method for ensemble data assimilation with multiple limited-area models

    Directory of Open Access Journals (Sweden)

    Matthew Kretschmer

    2015-04-01

    Full Text Available Limited-area models (LAMs allow high-resolution forecasts to be made for geographic regions of interest when resources are limited. Typically, boundary conditions for these models are provided through one-way boundary coupling from a coarser resolution global model. Here, data assimilation is considered in a situation in which a global model supplies boundary conditions to multiple LAMs. The data assimilation method presented combines information from all of the models to construct a single ‘composite state’, on which data assimilation is subsequently performed. The analysis composite state is then used to form the initial conditions of the global model and all of the LAMs for the next forecast cycle. The method is tested by using numerical experiments with simple, chaotic models. The results of the experiments show that there is a clear forecast benefit to allowing LAM states to influence one another during the analysis. In addition, adding LAM information at analysis time has a strong positive impact on global model forecast performance, even at points not covered by the LAMs.

  9. Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

    Science.gov (United States)

    Albergel, Clément; Munier, Simon; Leroux, Delphine Jennifer; Dewaele, Hélène; Fairbairn, David; Lavinia Barbu, Alina; Gelati, Emiliano; Dorigo, Wouter; Faroux, Stéphanie; Meurey, Catherine; Le Moigne, Patrick; Decharme, Bertrand; Mahfouf, Jean-Francois; Calvet, Jean-Christophe

    2017-10-01

    In this study, a global land data assimilation system (LDAS-Monde) is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the interactions between soil-biosphere-atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. SSM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 to 100 cm depth). A sensitivity test of the Jacobians over 2000-2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and SSM have an impact on the different control variables. From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM) from the top layers of soil, as model sensitivity to SSM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A comprehensive evaluation of

  10. Assimilation of passive and active CCI soil moisture products into hydrological modelling: an intercomparison study in Europe

    Science.gov (United States)

    Maggioni, V.; Massari, C.; Camici, S.; Brocca, L.; Marchesini, I.

    2017-12-01

    Soil moisture (SM) is a key variable in rainfall-runoff partitioning since it acts on the main hydrological processes taking part within a catchment. Modeling SM is often a difficult task due to its large variability at different temporal and spatial scales. Ground soil moisture measurements are a valuable tool for improving runoff prediction but are often limited and suffer from spatial representativeness issues. Remotely sensed observations offer a new source of data able to cope the latter issues thus opening new possibilities for improving flood simulations worldwide. Today, several different SM products are available at increased accuracy with respect to the past. Some interesting products are those derived from the Climate Change Initiative (CCI) which offer the most complete and most consistent global SM data record based on active and passive microwave sensors.Thanks to the combination of multiple sensors within an active, a passive and an active+passive products, the CCI SM is expected to provide a significant benefit for the improvement of rainfall-runoff simulations through data assimilation. However, previous studies have shown that the success of the assimilation is not only related to the accuracy of the observations but also to the specific climate and the catchment physical and hydrological characteristics as well as to many necessary choices related to the assimilation technique. These choices along with the type of SM observations (i.e. passive or active) might play an important role for the success or the failure of the assimilation exercise which is not still clear. In this study, based on a large dataset of catchments covering large part of the Europe, we assimilated satellite SM observations from the passive and the active CCI SM products into Modello Idrologico Semiditribuito in Continuo (MISDc, Brocca et al. 2011). Rainfall and temperature data were collected from the European Climate Assessment & Dataset (E-OBS) while discharge data were

  11. Joint Center for Satellite Data Assimilation Overview and Research Activities

    Science.gov (United States)

    Auligne, T.

    2017-12-01

    In 2001 NOAA/NESDIS, NOAA/NWS, NOAA/OAR, and NASA, subsequently joined by the US Navy and Air Force, came together to form the Joint Center for Satellite Data Assimilation (JCSDA) for the common purpose of accelerating the use of satellite data in environmental numerical prediction modeling by developing, using, and anticipating advances in numerical modeling, satellite-based remote sensing, and data assimilation methods. The primary focus was to bring these advances together to improve operational numerical model-based forecasting, under the premise that these partners have common technical and logistical challenges assimilating satellite observations into their modeling enterprises that could be better addressed through cooperative action and/or common solutions. Over the last 15 years, the JCSDA has made and continues to make major contributions to operational assimilation of satellite data. The JCSDA is a multi-agency U.S. government-owned-and-operated organization that was conceived as a venue for the several agencies NOAA, NASA, USAF and USN to collaborate on advancing the development and operational use of satellite observations into numerical model-based environmental analysis and forecasting. The primary mission of the JCSDA is to "accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction systems." This mission is fulfilled through directed research targeting the following key science objectives: Improved radiative transfer modeling; new instrument assimilation; assimilation of humidity, clouds, and precipitation observations; assimilation of land surface observations; assimilation of ocean surface observations; atmospheric composition; and chemistry and aerosols. The goal of this presentation is to briefly introduce the JCSDA's mission and vision, and to describe recent research activities across various JCSDA partners.

  12. Update on the NASA GEOS-5 Aerosol Forecasting and Data Assimilation System

    Science.gov (United States)

    Colarco, Peter; da Silva, Arlindo; Aquila, Valentina; Bian, Huisheng; Buchard, Virginie; Castellanos, Patricia; Darmenov, Anton; Follette-Cook, Melanie; Govindaraju, Ravi; Keller, Christoph; hide

    2017-01-01

    GEOS-5 is the Goddard Earth Observing System model. GEOS-5 is maintained by the NASA Global Modeling and Assimilation Office. Core development is within GMAO,Goddard Atmospheric Chemistry and Dynamics Laboratory, and with external partners. Primary GEOS-5 functions: Earth system model for studying climate variability and change, provide research quality reanalyses for supporting NASA instrument teams and scientific community, provide near-real time forecasts of meteorology,aerosols, and other atmospheric constituents to support NASA airborne campaigns.

  13. Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates

    Science.gov (United States)

    Lievens, H.; Reichle, R. H.; Liu, Q.; De Lannoy, G.; Dunbar, R. S.; Kim, S.; Das, N. N.; Cosh, M. H.; Walker, J. P.; Wagner, W.

    2017-12-01

    SMAP (Soil Moisture Active and Passive) radiometer observations at 40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model (CLSM) to generate the SMAP Level 4 Soil Moisture product. The use of C-band radar backscatter observations from Sentinel-1 has the potential to add value to the radiance assimilation by increasing the level of spatial detail. The specifications of Sentinel-1 are appealing, particularly its high spatial resolution (5 by 20 m in interferometric wide swath mode) and frequent revisit time (6 day repeat cycle for the Sentinel-1A and Sentinel-1B constellation). However, the shorter wavelength of Sentinel-1 observations implies less sensitivity to soil moisture. This study investigates the value of Sentinel-1 data for hydrologic simulations by assimilating the radar observations into CLSM, either separately from or simultaneously with SMAP radiometer observations. To facilitate the assimilation of the radar observations, CLSM is coupled to the water cloud model, simulating the radar backscatter as observed by Sentinel-1. The innovations, i.e. differences between observations and simulations, are converted into increments to the model soil moisture state through an Ensemble Kalman Filter. The assimilation impact is assessed by comparing 3-hourly, 9 km surface and root-zone soil moisture simulations with in situ measurements from 9 km SMAP core validation sites and sparse networks, from May 2015 to 2017. The Sentinel-1 assimilation consistently improves surface soil moisture, whereas root-zone impacts are mostly neutral. Relatively larger improvements are obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performs best, demonstrating the complementary value of radar and radiometer observations.

  14. A Global Rapid Integrated Monitoring System for Water Cycle and Water Resource Assessment (Global-RIMS)

    Science.gov (United States)

    Roads, John; Voeroesmarty, Charles

    2005-01-01

    The main focus of our work was to solidify underlying data sets, the data processing tools and the modeling environment needed to perform a series of long-term global and regional hydrological simulations leading eventually to routine hydrometeorological predictions. A water and energy budget synthesis was developed for the Mississippi River Basin (Roads et al. 2003), in order to understand better what kinds of errors exist in current hydrometeorological data sets. This study is now being extended globally with a larger number of observations and model based data sets under the new NASA NEWS program. A global comparison of a number of precipitation data sets was subsequently carried out (Fekete et al. 2004) in which it was further shown that reanalysis precipitation has substantial problems, which subsequently led us to the development of a precipitation assimilation effort (Nunes and Roads 2005). We believe that with current levels of model skill in predicting precipitation that precipitation assimilation is necessary to get the appropriate land surface forcing.

  15. Earth Observations for Global Water Security

    Science.gov (United States)

    Lawford, Richard; Strauch, Adrian; Toll, David; Fekete, Balazs; Cripe, Douglas

    2013-01-01

    The combined effects of population growth, increasing demands for water to support agriculture, energy security, and industrial expansion, and the challenges of climate change give rise to an urgent need to carefully monitor and assess trends and variations in water resources. Doing so will ensure that sustainable access to adequate quantities of safe and useable water will serve as a foundation for water security. Both satellite and in situ observations combined with data assimilation and models are needed for effective, integrated monitoring of the water cycle's trends and variability in terms of both quantity and quality. On the basis of a review of existing observational systems, we argue that a new integrated monitoring capability for water security purposes is urgently needed. Furthermore, the components for this capability exist and could be integrated through the cooperation of national observational programmes. The Group on Earth Observations should play a central role in the design, implementation, management and analysis of this system and its products.

  16. Ensemble Kalman Filter data assimilation and storm surge experiments of tropical cyclone Nargis

    Directory of Open Access Journals (Sweden)

    Le Duc

    2015-07-01

    Full Text Available Data assimilation experiments on Myanmar tropical cyclone (TC, Nargis, using the Local Ensemble Transform Kalman Filter (LETKF method and the Japan Meteorological Agency (JMA non-hydrostatic model (NHM were performed to examine the impact of LETKF on analysis performance in real cases. Although the LETKF control experiment using NHM as its driving model (NHM–LETKF produced a weak vortex, the subsequent 3-day forecast predicted Nargis’ track and intensity better than downscaling from JMA's global analysis. Some strategies to further improve the final analysis were considered. They were sea surface temperature (SST perturbations and assimilation of TC advisories. To address SST uncertainty, SST analyses issued by operational forecast centres were used in the assimilation window. The use of a fixed source of SST analysis for each ensemble member was more effective in practice. SST perturbations were found to have slightly positive impact on the track forecasts. Assimilation of TC advisories could have a positive impact with a reasonable choice of its free parameters. However, the TC track forecasts exhibited northward displacements, when the observation error of intensities was underestimated in assimilation of TC advisories. The use of assimilation of TC advisories was considered in the final NHM–LETKF by choosing an appropriate set of free parameters. The extended forecast based on the final analysis provided meteorological forcings for a storm surge simulation using the Princeton Ocean Model. Probabilistic forecasts of the water levels at Irrawaddy and Yangon significantly improved the results in the previous studies.

  17. SWOT data assimilation for operational reservoir management on the upper Niger River Basin

    Science.gov (United States)

    Munier, S.; Polebistki, A.; Brown, C.; Belaud, G.; Lettenmaier, D. P.

    2015-01-01

    The future Surface Water and Ocean Topography (SWOT) satellite mission will provide two-dimensional maps of water elevation for rivers with width greater than 100 m globally. We describe a modeling framework and an automatic control algorithm that prescribe optimal releases from the Selingue dam in the Upper Niger River Basin, with the objective of understanding how SWOT data might be used to the benefit of operational water management. The modeling framework was used in a twin experiment to simulate the "true" system state and an ensemble of corrupted model states. Virtual SWOT observations of reservoir and river levels were assimilated into the model with a repeat cycle of 21 days. The updated state was used to initialize a Model Predictive Control (MPC) algorithm that computed the optimal reservoir release that meets a minimum flow requirement 300 km downstream of the dam. The data assimilation results indicate that the model updates had a positive effect on estimates of both water level and discharge. The "persistence," which describes the duration of the assimilation effect, was clearly improved (greater than 21 days) by integrating a smoother into the assimilation procedure. We compared performances of the MPC with SWOT data assimilation to an open-loop MPC simulation. Results show that the data assimilation resulted in substantial improvements in the performances of the Selingue dam management with a greater ability to meet environmental requirements (the number of days the target is missed falls to zero) and a minimum volume of water released from the dam.

  18. Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

    Science.gov (United States)

    Fillion, Anthony; Bocquet, Marc; Gratton, Serge

    2018-04-01

    The analysis in nonlinear variational data assimilation is the solution of a non-quadratic minimization. Thus, the analysis efficiency relies on its ability to locate a global minimum of the cost function. If this minimization uses a Gauss-Newton (GN) method, it is critical for the starting point to be in the attraction basin of a global minimum. Otherwise the method may converge to a local extremum, which degrades the analysis. With chaotic models, the number of local extrema often increases with the temporal extent of the data assimilation window, making the former condition harder to satisfy. This is unfortunate because the assimilation performance also increases with this temporal extent. However, a quasi-static (QS) minimization may overcome these local extrema. It accomplishes this by gradually injecting the observations in the cost function. This method was introduced by Pires et al. (1996) in a 4D-Var context. We generalize this approach to four-dimensional strong-constraint nonlinear ensemble variational (EnVar) methods, which are based on both a nonlinear variational analysis and the propagation of dynamical error statistics via an ensemble. This forces one to consider the cost function minimizations in the broader context of cycled data assimilation algorithms. We adapt this QS approach to the iterative ensemble Kalman smoother (IEnKS), an exemplar of nonlinear deterministic four-dimensional EnVar methods. Using low-order models, we quantify the positive impact of the QS approach on the IEnKS, especially for long data assimilation windows. We also examine the computational cost of QS implementations and suggest cheaper algorithms.

  19. Assimilation of MODIS Dark Target and Deep Blue Observations in the Dust Aerosol Component of NMMB-MONARCH version 1.0

    Science.gov (United States)

    Di Tomaso, Enza; Schutgens, Nick A. J.; Jorba, Oriol; Perez Garcia-Pando, Carlos

    2017-01-01

    A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter - LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets.The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.

  20. Satellite Data Assimilation within KIAPS-LETKF system

    Science.gov (United States)

    Jo, Y.; Lee, S., Sr.; Cho, K.

    2016-12-01

    Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing an ensemble data assimilation system using four-dimensional local ensemble transform kalman filter (LETKF; Hunt et al., 2007) within KIAPS Integrated Model (KIM), referred to as "KIAPS-LETKF". KIAPS-LETKF system was successfully evaluated with various Observing System Simulation Experiments (OSSEs) with NCAR Community Atmospheric Model - Spectral Element (Kang et al., 2013), which has fully unstructured quadrilateral meshes based on the cubed-sphere grid as the same grid system of KIM. Recently, assimilation of real observations has been conducted within the KIAPS-LETKF system with four-dimensional covariance functions over the 6-hr assimilation window. Then, conventional (e.g., sonde, aircraft, and surface) and satellite (e.g., AMSU-A, IASI, GPS-RO, and AMV) observations have been provided by the KIAPS Package for Observation Processing (KPOP). Wind speed prediction was found most beneficial due to ingestion of AMV and for the temperature prediction the improvement in assimilation is mostly due to ingestion of AMSU-A and IASI. However, some degradation in the simulation of the GPS-RO is presented in the upper stratosphere, even though GPS-RO leads positive impacts on the analysis and forecasts. We plan to test the bias correction method and several vertical localization strategies for radiance observations to improve analysis and forecast impacts.

  1. Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

    Science.gov (United States)

    Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

    2017-12-01

    The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

  2. Statistical techniques to extract information during SMAP soil moisture assimilation

    Science.gov (United States)

    Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.

    2017-12-01

    Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

  3. Impact of Assimilating Surface Velocity Observations on the Model Sea Surface Height Using the NCOM-4DVAR

    Science.gov (United States)

    2016-09-26

    the ensemble Kalman filter and the ensemble Kalman smoother: A comparison study using a nonlinear reduced gravity ocean model.OceanModell., 12, 378...using local ensemble transform Kalman filter and optimum-interpolation assimilation schemes. Ocean Modell., 69, 22–38, doi:10.1016/j.ocemod.2013.05.002...observations are assimi- lated. This gives a sense of the added value from the inclusion of velocity observations with the standard set of temperature

  4. Recent Trends in Global Ocean Chlorophyll

    Science.gov (United States)

    Gregg, Watson; Casey, Nancy

    2004-01-01

    Recent analyses of SeaWiFS data have shown that global ocean chlorophyll has increased more than 5% since 1998. The North Pacific ocean basin has increased nearly 19%. To understand the causes of these trends we have applied the newly developed NASA Ocean Biogeochemical Assimilation Model (OBAM), which is driven in mechanistic fashion by surface winds, sea surface temperature, atmospheric iron deposition, sea ice, and surface irradiance. The mode1 utilizes chlorophyll from SeaWiFS in a daily assimilation. The model has in place many of the climatic variables that can be expected to produce the changes observed in SeaWiFS data. Ths enables us to diagnose the model performance, the assimilation performance, and possible causes for the increase in chlorophyll.

  5. Ensemble assimilation of JASON/ENVISAT and JASON/AltiKA altimetric observations with stochastic parameterization of the model dynamical uncertainties

    Science.gov (United States)

    Brasseur, Pierre; Candille, Guillem; Bouttier, Pierre-Antoine; Brankart, Jean-Michel; Verron, Jacques

    2015-04-01

    The objective of this study is to explicitly simulate and quantify the uncertainty related to sea-level anomalies diagnosed from eddy-resolving ocean circulation models, in order to develop advanced methods suitable for addressing along-track altimetric data assimilation into such models. This work is carried out jointly with the MyOcean and SANGOMA (Stochastic Assimilation for the Next Generation Ocean Model Applications) consortium, funded by EU under the GMES umbrella over the 2012-2015 period. In this framework, a realistic circulation model of the North Atlantic ocean at 1/4° resolution (NATL025 configuration) has been adapted to include effects of unresolved scales on the dynamics. This is achieved by introducing stochastic perturbations of the equation of state to represent the associated model uncertainty. Assimilation experiments are designed using altimetric data from past and on-going missions (Jason-2 and Saral/AltiKA experiments, and Cryosat-2 for fully independent altimetric validation) to better control the Gulf Stream circulation, especially the frontal regions which are predominantly affected by the non-resolved dynamical scales. An ensemble based on such stochastic perturbations is then produced and evaluated -through the probabilistic criteria: the reliability and the resolution- using the model equivalent of along-track altimetric observations. These three elements (stochastic parameterization, ensemble simulation and 4D observation operator) are used together to perform optimal 4D analysis of along-track altimetry over 10-day assimilation windows. In this presentation, the results show that the free ensemble -before starting the assimilation process- well reproduces the climatological variability over the Gulf Stream area: the system is then pretty reliable but no informative (null probabilistic resolution). Updating the free ensemble with altimetric data leads to a better reliability and to an improvement of the information (resolution

  6. Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

    Directory of Open Access Journals (Sweden)

    C. Albergel

    2017-10-01

    Full Text Available In this study, a global land data assimilation system (LDAS-Monde is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface soil moisture (SSM and leaf area index (LAI observations to constrain the interactions between soil–biosphere–atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere land surface model (LSM coupled with the CNRM (Centre National de Recherches Météorologiques version of the Total Runoff Integrating Pathways (ISBA-CTRIP continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. SSM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF, which uses finite differences from perturbed simulations to generate flow dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 to 100 cm depth. A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and SSM have an impact on the different control variables. From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM from the top layers of soil, as model sensitivity to SSM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 to 60 cm. The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A

  7. Continuous dynamic assimilation of the inner region data in hydrodynamics modelling: optimization approach

    Directory of Open Access Journals (Sweden)

    F. I. Pisnitchenko

    2008-11-01

    Full Text Available In meteorological and oceanological studies the classical approach for finding the numerical solution of the regional model consists in formulating and solving a Cauchy-Dirichlet problem. The boundary conditions are obtained by linear interpolation of coarse-grid data provided by a global model. Errors in boundary conditions due to interpolation may cause large deviations from the correct regional solution. The methods developed to reduce these errors deal with continuous dynamic assimilation of known global data available inside the regional domain. One of the approaches of this assimilation procedure performs a nudging of large-scale components of regional model solution to large-scale global data components by introducing relaxation forcing terms into the regional model equations. As a result, the obtained solution is not a valid numerical solution to the original regional model. Another approach is the use a four-dimensional variational data assimilation procedure which is free from the above-mentioned shortcoming. In this work we formulate the joint problem of finding the regional model solution and data assimilation as a PDE-constrained optimization problem. Three simple model examples (ODE Burgers equation, Rossby-Oboukhov equation, Korteweg-de Vries equation are considered in this paper. Numerical experiments indicate that the optimization approach can significantly improve the precision of the regional solution.

  8. Multi-parametric variational data assimilation for hydrological forecasting

    Science.gov (United States)

    Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

    2017-12-01

    Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

  9. Boundary Conditions, Data Assimilation, and Predictability in Coastal Ocean Models

    National Research Council Canada - National Science Library

    Samelson, Roger M; Allen, John S; Egbert, Gary D; Kindle, John C; Snyder, Chris

    2007-01-01

    ...: The specific objectives of this research are to determine the impact on coastal ocean circulation models of open ocean boundary conditions from Global Ocean Data Assimilation Experiment (GODAE...

  10. Skill Assessment in Ocean Biological Data Assimilation

    Science.gov (United States)

    Gregg, Watson W.; Friedrichs, Marjorie A. M.; Robinson, Allan R.; Rose, Kenneth A.; Schlitzer, Reiner; Thompson, Keith R.; Doney, Scott C.

    2008-01-01

    There is growing recognition that rigorous skill assessment is required to understand the ability of ocean biological models to represent ocean processes and distributions. Statistical analysis of model results with observations represents the most quantitative form of skill assessment, and this principle serves as well for data assimilation models. However, skill assessment for data assimilation requires special consideration. This is because there are three sets of information in the free-run model, data, and the assimilation model, which uses Data assimilation information from both the flee-run model and the data. Intercom parison of results among the three sets of information is important and useful for assessment, but is not conclusive since the three information sets are intertwined. An independent data set is necessary for an objective determination. Other useful measures of ocean biological data assimilation assessment include responses of unassimilated variables to the data assimilation, performance outside the prescribed region/time of interest, forecasting, and trend analysis. Examples of each approach from the literature are provided. A comprehensive list of ocean biological data assimilation and their applications of skill assessment, in both ecosystem/biogeochemical and fisheries efforts, is summarized.

  11. The Representation of Tropical Cyclones Within the Global William Putman Non-Hydrostatic Goddard Earth Observing System Model (GEOS-5) at Cloud-Permitting Resolutions

    Science.gov (United States)

    Putman, William M.

    2010-01-01

    The Goddard Earth Observing System Model (GEOS-S), an earth system model developed in the NASA Global Modeling and Assimilation Office (GMAO), has integrated the non-hydrostatic finite-volume dynamical core on the cubed-sphere grid. The extension to a non-hydrostatic dynamical framework and the quasi-uniform cubed-sphere geometry permits the efficient exploration of global weather and climate modeling at cloud permitting resolutions of 10- to 4-km on today's high performance computing platforms. We have explored a series of incremental increases in global resolution with GEOS-S from irs standard 72-level 27-km resolution (approx.5.5 million cells covering the globe from the surface to 0.1 hPa) down to 3.5-km (approx. 3.6 billion cells).

  12. Model Uncertainty Quantification Methods In Data Assimilation

    Science.gov (United States)

    Pathiraja, S. D.; Marshall, L. A.; Sharma, A.; Moradkhani, H.

    2017-12-01

    Data Assimilation involves utilising observations to improve model predictions in a seamless and statistically optimal fashion. Its applications are wide-ranging; from improving weather forecasts to tracking targets such as in the Apollo 11 mission. The use of Data Assimilation methods in high dimensional complex geophysical systems is an active area of research, where there exists many opportunities to enhance existing methodologies. One of the central challenges is in model uncertainty quantification; the outcome of any Data Assimilation study is strongly dependent on the uncertainties assigned to both observations and models. I focus on developing improved model uncertainty quantification methods that are applicable to challenging real world scenarios. These include developing methods for cases where the system states are only partially observed, where there is little prior knowledge of the model errors, and where the model error statistics are likely to be highly non-Gaussian.

  13. Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

    Science.gov (United States)

    Peters-Lidard, Christa D.

    2011-01-01

    The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling

  14. Insights on the impact of systematic model errors on data assimilation performance in changing catchments

    Science.gov (United States)

    Pathiraja, S.; Anghileri, D.; Burlando, P.; Sharma, A.; Marshall, L.; Moradkhani, H.

    2018-03-01

    The global prevalence of rapid and extensive land use change necessitates hydrologic modelling methodologies capable of handling non-stationarity. This is particularly true in the context of Hydrologic Forecasting using Data Assimilation. Data Assimilation has been shown to dramatically improve forecast skill in hydrologic and meteorological applications, although such improvements are conditional on using bias-free observations and model simulations. A hydrologic model calibrated to a particular set of land cover conditions has the potential to produce biased simulations when the catchment is disturbed. This paper sheds new light on the impacts of bias or systematic errors in hydrologic data assimilation, in the context of forecasting in catchments with changing land surface conditions and a model calibrated to pre-change conditions. We posit that in such cases, the impact of systematic model errors on assimilation or forecast quality is dependent on the inherent prediction uncertainty that persists even in pre-change conditions. Through experiments on a range of catchments, we develop a conceptual relationship between total prediction uncertainty and the impacts of land cover changes on the hydrologic regime to demonstrate how forecast quality is affected when using state estimation Data Assimilation with no modifications to account for land cover changes. This work shows that systematic model errors as a result of changing or changed catchment conditions do not always necessitate adjustments to the modelling or assimilation methodology, for instance through re-calibration of the hydrologic model, time varying model parameters or revised offline/online bias estimation.

  15. Towards a Comprehensive Dynamic-chemistry Assimilation for Eos-Chem: Plans and Status in NASA's Data Assimilation Office

    Science.gov (United States)

    Pawson, Steven; Lin, Shian-Jiann; Rood, Richard B.; Stajner, Ivanka; Nebuda, Sharon; Nielsen, J. Eric; Douglass, Anne R.

    2000-01-01

    In order to support the EOS-Chem project, a comprehensive assimilation package for the coupled chemical-dynamical system is being developed by the Data Assimilation Office at NASA GSFC. This involves development of a coupled chemistry/meteorology model and of data assimilation techniques for trace species and meteorology. The model is being developed using the flux-form semi-Lagrangian dynamical core of Lin and Rood, the physical parameterizations from the NCAR Community Climate Model, and atmospheric chemistry modules from the Atmospheric Chemistry and Dynamics branch at NASA GSFC. To date the following results have been obtained: (i) multi-annual simulations with the dynamics-radiation model show the credibility of the package for atmospheric simulations; (ii) initial simulations including a limited number of middle atmospheric trace gases reveal the realistic nature of transport mechanisms, although there is still a need for some improvements. Samples of these results will be shown. A meteorological assimilation system is currently being constructed using the model; this will form the basis for the proposed meteorological/chemical assimilation package. The latter part of the presentation will focus on areas targeted for development in the near and far terms, with the objective of Providing a comprehensive assimilation package for the EOS-Chem science experiment. The first stage will target ozone assimilation. The plans also encompass a reanalysis (ReSTS) for the 1991-1995 period, which includes the Mt. Pinatubo eruption and the time when a large number of UARS observations were available. One of the most challenging aspects of future developments will be to couple theoretical advances in tracer assimilation with the practical considerations of a real environment and eventually a near-real-time assimilation system.

  16. Assimilation of the Observational Data in the Marine Ecosystem Adaptive Model at the Known Mean Values of the Processes in the Marine Environment

    Directory of Open Access Journals (Sweden)

    I.Е. Тimchenko

    2017-10-01

    Full Text Available Assimilation of observational data in the marine ecosystem adaptive models constructed by the adaptive balance of causes method is considered. It is shown that the feedback balance between the ecosystem variables and the rates of their change used in the method equations, permits to introduce a stationary state of the ecosystem characterized by the observed mean values of the variables. The method for assessing the normalized coefficients of influences based on application of the Euler theorem on homogeneous functions to the functions representing material balances of biochemical reactions of the substance transformation is proposed. It is shown that the normalized ratios of the modeled process mean values can be used as the estimates of the reaction product derivatives obtained on the basis of their resources included in the equations of material balances. One-dimensional adaptive model of the sea upper layer ecosystem is constructed as an example; it is based on the scheme of cause-effect relations of the Fasham, Dacklow and McKelvie model of plankton dynamics and nitrogen cycle It is shown that in such a model, observational data is assimilated by automatic adaptation of the model variables to the assimilated information providing that the substance material balance are preserved in the transformation reactions. The data simulating both observations of the chlorophyll a concentrations and the marine environment dynamics are assimilated in the model. Time scenarios of the biochemical processes are constructed; they confirm applicability of the proposed method for assessing the effect coefficients based on the ratios of the simulated process mean values.

  17. Calibration of a rainfall-runoff hydrological model and flood simulation using data assimilation

    Science.gov (United States)

    Piacentini, A.; Ricci, S. M.; Thual, O.; Coustau, M.; Marchandise, A.

    2010-12-01

    velocity travel before the flood peak. These optimal values are used for a new simulation of the event in forecast mode (under the assumption of perfect rain-fall). On both catchments, it was shown over a significant number of flood events, that the data assimilation procedure improves the flood peak forecast. The improvement is globally more important for the Gardon d'Anduze catchment where the flood events are stronger. The peak can be forecasted up to 36 hours head of time assimilating very few observations (up to 4) during the rise of the water level. For multiple peaks events, the assimilation of the observations from the first peak leads to a significant improvement of the second peak simulation. It was also shown that the flood rise is often faster in reality than it is represented by the model. In this case and when the flood peak is under estimated in the simulation, the use of the first observations can be misleading for the data assimilation algorithm. The careful estimation of the observation and background error variances enabled the satisfying use of the data assimilation in these complex cases even though it does not allow the model error correction.

  18. Assimilation of SAPHIR radiance: impact on hyperspectral radiances in 4D-VAR

    Science.gov (United States)

    Indira Rani, S.; Doherty, Amy; Atkinson, Nigel; Bell, William; Newman, Stuart; Renshaw, Richard; George, John P.; Rajagopal, E. N.

    2016-04-01

    Assimilation of a new observation dataset in an NWP system may affect the quality of an existing observation data set against the model background (short forecast), which in-turn influence the use of an existing observation in the NWP system. Effect of the use of one data set on the use of another data set can be quantified as positive, negative or neutral. Impact of the addition of new dataset is defined as positive if the number of assimilated observations of an existing type of observation increases, and bias and standard deviation decreases compared to the control (without the new dataset) experiment. Recently a new dataset, Megha Tropiques SAPHIR radiances, which provides atmospheric humidity information, is added in the Unified Model 4D-VAR assimilation system. In this paper we discuss the impact of SAPHIR on the assimilation of hyper-spectral radiances like AIRS, IASI and CrIS. Though SAPHIR is a Microwave instrument, its impact can be clearly seen in the use of hyper-spectral radiances in the 4D-VAR data assimilation systems in addition to other Microwave and InfraRed observation. SAPHIR assimilation decreased the standard deviation of the spectral channels of wave number from 650 -1600 cm-1 in all the three hyperspectral radiances. Similar impact on the hyperspectral radiances can be seen due to the assimilation of other Microwave radiances like from AMSR2 and SSMIS Imager.

  19. DART: New Research Using Ensemble Data Assimilation in Geophysical Models

    Science.gov (United States)

    Hoar, T. J.; Raeder, K.

    2015-12-01

    The Data Assimilation Research Testbed (DART) is a community facilityfor ensemble data assimilation developed and supported by the NationalCenter for Atmospheric Research. DART provides a comprehensive suite of software, documentation, and tutorials that can be used for ensemble data assimilation research, operations, and education. Scientists and software engineers at NCAR are available to support DART users who want to use existing DART products or develop their own applications. Current DART users range from university professors teaching data assimilation, to individual graduate students working with simple models, through national laboratories doing operational prediction with large state-of-the-art models. DART runs efficiently on many computational platforms ranging from laptops through thousands of cores on the newest supercomputers.This poster focuses on several recent research activities using DART with geophysical models.Using CAM/DART to understand whether OCO-2 Total Precipitable Water observations can be useful in numerical weather prediction.Impacts of the synergistic use of Infra-red CO retrievals (MOPITT, IASI) in CAM-CHEM/DART assimilations.Assimilation and Analysis of Observations of Amazonian Biomass Burning Emissions by MOPITT (aerosol optical depth), MODIS (carbon monoxide) and MISR (plume height).Long term evaluation of the chemical response of MOPITT-CO assimilation in CAM-CHEM/DART OSSEs for satellite planning and emission inversion capabilities.Improved forward observation operators for land models that have multiple land use/land cover segments in a single grid cell,Simulating mesoscale convective systems (MCSs) using a variable resolution, unstructured grid in the Model for Prediction Across Scales (MPAS) and DART.The mesoscale WRF+DART system generated an ensemble of year-long, real-time initializations of a convection allowing model over the United States.Constraining WACCM with observations in the tropical band (30S-30N) using DART

  20. Data assimilation in the decision support system RODOS

    DEFF Research Database (Denmark)

    Rojas-Palma, C.; Madsen, H.; Gering, F.

    2003-01-01

    . The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman...

  1. Discharge estimation in ungauged basins through variational data assimilation : The potential of the SWOT mission.

    Science.gov (United States)

    Oubanas, H.; Gejadze, I.; Malaterre, P. O.; Durand, M. T.; Wei, R.; Frasson, R. P. M.; Domeneghetti, A.

    2017-12-01

    This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) mission, to be launched in 2021, using a variant of the standard variational data assimilation method `4D-Var'. The hydrology SWOT simulator, developed at the Jet Propulsion Laboratory (JPL) has been used to simulate the expected performance of the KaRIn instrument onboard the satellite, producing synthetic SWOT observations of height and width, at each satellite overpass. SWOT data products were synthesized at the spatial scale of 200 m along the river centerline. Using a 1.5D full Saint-Venant hydraulic model, variational data assimilation simultaneously estimates the inflow discharge, river bathymetry and bed roughness. The proposed method has been designed for an application to fully ungauged basins; therefore, the prior information is derived from the SWOT observations only and the globally available ancillary information. Two reaches of the Po and Sacramento Rivers of about 130 km and 150 km, respectively, have been considered in this study. Discharge was successfully recovered at the overpass time with a relative-root-mean-square error of 16% and 12.3% for the Po and Sacramento Rivers, respectively. The estimates of the bed level and the roughness coefficient demonstrate a local improvement; however they may not provide reliable global information of the river bathymetry and roughness.

  2. Testbed model and data assimilation for ARM

    International Nuclear Information System (INIS)

    Louis, J.F.

    1992-01-01

    The objectives of this contract are to further develop and test the ALFA (AER Local Forecast and Assimilation) model originally designed at AER for local weather prediction and apply it to three distinct but related purposes in connection with the Atmospheric Radiation Measurement (ARM) program: (a) to provide a testbed that simulates a global climate model in order to facilitate the development and testing of new cloud parametrizations and radiation models; (b) to assimilate the ARM data continuously at the scale of a climate model, using the adjoint method, thus providing the initial conditions and verification data for testing parameumtions; (c) to study the sensitivity of a radiation scheme to cloud parameters, again using the adjoint method, thus demonstrating the usefulness of the testbed model. The data assimilation will use a variational technique that minimizes the difference between the model results and the observation during the analysis period. The adjoint model is used to compute the gradient of a measure of the model errors with respect to nudging terms that are added to the equations to force the model output closer to the data. The radiation scheme that will be included in the basic ALFA model makes use of a gen two-stream approximation, and is designed for vertically inhonogeneous, multiple-scattering atmospheres. The sensitivity of this model to the definition of cloud parameters will be studied. The adjoint technique will also be used to compute the sensitivities. This project is designed to provide the Science Team members with the appropriate tools and modeling environment for proper testing and tuning of new radiation models and cloud parametrization schemes

  3. Evaluating the performance of the Electron Density Assimilative Model (EDAM) in the Western European sector using modified Taylor diagrams

    Science.gov (United States)

    Jackson-Booth, N.; Parker, J.; Pryse, S. E.; Buckland, R.

    2017-12-01

    The Electron Density Assimilative Model (EDAM) is an ionospheric model that assimilates data sources into a background model, currently provided by IRI2007, to generate a global, or regional, 3D representation of the ionospheric electron density. In this study, slant total electron content (sTEC) between GPS satellites and 43 ground receivers in Europe were assimilated into EDAM to model the ionospheric electron density over western Europe. For the evaluation of the model an additional ground receiver (the truth station) was considered, which was not used in the assimilation process. Slant total electron contents for this station were calculated through the EDAM model along satellite-to-receiver paths corresponding to those of the observations made by the receiver. The modelled and observed sTEC were compared for each satellite and every day, between September 2002 and August 2003. For the comparison standard deviations of the modelled and observed sTEC were determined. These were used in modified Taylor Diagrams to display the mean-removed rms difference between the model and observations, the correlation between the two data sets and the bias of the modelled data. Taylor diagrams were obtained for the entire year, and each season and month. Results of the comparisons are presented and discussed, with a specific interest in times that show increased rms differences and decreased correlations between the data sets. The effect of the satellite calibration biases on the results are also considered.

  4. Data assimilation in integrated hydrological modelling

    DEFF Research Database (Denmark)

    Rasmussen, Jørn

    Integrated hydrological models are useful tools for water resource management and research, and advances in computational power and the advent of new observation types has resulted in the models generally becoming more complex and distributed. However, the models are often characterized by a high...... degree of parameterization which results in significant model uncertainty which cannot be reduced much due to observations often being scarce and often taking the form of point measurements. Data assimilation shows great promise for use in integrated hydrological models , as it allows for observations...... to be efficiently combined with models to improve model predictions, reduce uncertainty and estimate model parameters. In this thesis, a framework for assimilating multiple observation types and updating multiple components and parameters of a catchment scale integrated hydrological model is developed and tested...

  5. Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

    Science.gov (United States)

    Prive, N. C.; Errico, Ronald M.

    2015-01-01

    The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

  6. Naming game with biased assimilation over adaptive networks

    Science.gov (United States)

    Fu, Guiyuan; Zhang, Weidong

    2018-01-01

    The dynamics of two-word naming game incorporating the influence of biased assimilation over adaptive network is investigated in this paper. Firstly an extended naming game with biased assimilation (NGBA) is proposed. The hearer in NGBA accepts the received information in a biased manner, where he may refuse to accept the conveyed word from the speaker with a predefined probability, if the conveyed word is different from his current memory. Secondly, the adaptive network is formulated by rewiring the links. Theoretical analysis is developed to show that the population in NGBA will eventually reach global consensus on either A or B. Numerical simulation results show that the larger strength of biased assimilation on both words, the slower convergence speed, while larger strength of biased assimilation on only one word can slightly accelerate the convergence; larger population size can make the rate of convergence slower to a large extent when it increases from a relatively small size, while such effect becomes minor when the population size is large; the behavior of adaptively reconnecting the existing links can greatly accelerate the rate of convergence especially on the sparse connected network.

  7. IASI Radiance Data Assimilation in Local Ensemble Transform Kalman Filter

    Science.gov (United States)

    Cho, K.; Hyoung-Wook, C.; Jo, Y.

    2016-12-01

    Korea institute of Atmospheric Prediction Systems (KIAPS) is developing NWP model with data assimilation systems. Local Ensemble Transform Kalman Filter (LETKF) system, one of the data assimilation systems, has been developed for KIAPS Integrated Model (KIM) based on cubed-sphere grid and has successfully assimilated real data. LETKF data assimilation system has been extended to 4D- LETKF which considers time-evolving error covariance within assimilation window and IASI radiance data assimilation using KPOP (KIAPS package for observation processing) with RTTOV (Radiative Transfer for TOVS). The LETKF system is implementing semi operational prediction including conventional (sonde, aircraft) observation and AMSU-A (Advanced Microwave Sounding Unit-A) radiance data from April. Recently, the semi operational prediction system updated radiance observations including GPS-RO, AMV, IASI (Infrared Atmospheric Sounding Interferometer) data at July. A set of simulation of KIM with ne30np4 and 50 vertical levels (of top 0.3hPa) were carried out for short range forecast (10days) within semi operation prediction LETKF system with ensemble forecast 50 members. In order to only IASI impact, our experiments used only conventional and IAIS radiance data to same semi operational prediction set. We carried out sensitivity test for IAIS thinning method (3D and 4D). IASI observation number was increased by temporal (4D) thinning and the improvement of IASI radiance data impact on the forecast skill of model will expect.

  8. Temperature Data Assimilation with Salinity Corrections: Validation for the NSIPP Ocean Data Assimilation System in the Tropical Pacific Ocean, 1993-1998

    Science.gov (United States)

    Troccoli, Alberto; Rienecker, Michele M.; Keppenne, Christian L.; Johnson, Gregory C.

    2003-01-01

    The NASA Seasonal-to-Interannual Prediction Project (NSIPP) has developed an Ocean data assimilation system to initialize the quasi-isopycnal ocean model used in our experimental coupled-model forecast system. Initial tests of the system have focused on the assimilation of temperature profiles in an optimal interpolation framework. It is now recognized that correction of temperature only often introduces spurious water masses. The resulting density distribution can be statically unstable and also have a detrimental impact on the velocity distribution. Several simple schemes have been developed to try to correct these deficiencies. Here the salinity field is corrected by using a scheme which assumes that the temperature-salinity relationship of the model background is preserved during the assimilation. The scheme was first introduced for a zlevel model by Troccoli and Haines (1999). A large set of subsurface observations of salinity and temperature is used to cross-validate two data assimilation experiments run for the 6-year period 1993-1998. In these two experiments only subsurface temperature observations are used, but in one case the salinity field is also updated whenever temperature observations are available.

  9. Insights about data assimilation frameworks for integrating GRACE with hydrological models

    Science.gov (United States)

    Schumacher, Maike; Kusche, Jürgen; Van Dijk, Albert I. J. M.; Döll, Petra; Schuh, Wolf-Dieter

    2016-04-01

    Improving the understanding of changes in the water cycle represents a challenging objective that requires merging information from various disciplines. Debates exist on selecting an appropriate assimilation technique to integrate GRACE-derived terrestrial water storage changes (TWSC) into hydrological models in order to downscale and disaggregate GRACE TWSC, overcome model limitations, and improve monitoring and forecast skills. Yet, the effect of the specific data assimilation technique in conjunction with ill-conditioning, colored noise, resolution mismatch between GRACE and model, and other complications is still unclear. Due to its simplicity, ensemble Kalman filters or smoothers (EnKF/S) are often applied. In this study, we show that modification of the filter approach might open new avenues to improve the integration process. Particularly, we discuss an improved calibration and data assimilation (C/DA) framework (Schumacher et al., 2016), which is based on the EnKF and was extended by the square root analysis scheme (SQRA) and the singular evolutive interpolated Kalman (SEIK) filter. In addition, we discuss an off-line data blending approach (Van Dijk et al., 2014) that offers the chance to merge multi-model ensembles with GRACE observations. The investigations include: (i) a theoretical comparison, focusing on similarities and differences of the conceptual formulation of the filter algorithms, (ii) a practical comparison, for which the approaches were applied to an ensemble of runs of the WaterGAP Global Hydrology Model (WGHM), as well as (iii) an impact assessment of the GRACE error structure on C/DA results. First, a synthetic experiment over the Mississippi River Basin (USA) was used to gain insights about the C/DA set-up before applying it to real data. The results indicated promising performances when considering alternative methods, e.g. applying the SEIK algorithm improved the correlation coefficient and root mean square error (RMSE) of TWSC by 0

  10. Downscaling, 2-way Nesting, and Data Assimilative Modeling in Coastal and Shelf Waters of the U.S. Mid-Atlantic Bight and Gulf of Maine

    Science.gov (United States)

    Wilkin, J.; Levin, J.; Lopez, A.; Arango, H.

    2016-02-01

    Coastal ocean models that downscale output from basin and global scale models are widely used to study regional circulation at enhanced resolution and locally important ecosystem, biogeochemical, and geomorphologic processes. When operated as now-cast or forecast systems, these models offer predictions that assist decision-making for numerous maritime applications. We describe such a system for shelf waters of the Mid-Atlantic Bight (MAB) and Gulf of Maine (GoM) where the MARACOOS and NERACOOS associations of U.S. IOOS operate coastal ocean observing systems that deliver a dense observation set using CODAR HF-radar, autonomous underwater glider vehicles (AUGV), telemetering moorings, and drifting buoys. Other U.S. national and global observing systems deliver further sustained observations from moorings, ships, profiling floats, and a constellation of satellites. Our MAB and GoM re-analysis and forecast system uses the Regional Ocean Modeling System (ROMS; myroms.org) with 4-dimensional Variational (4D-Var) data assimilation to adjust initial conditions, boundary conditions, and surface forcing in each analysis cycle. Data routinely assimilated include CODAR velocities, altimeter satellite sea surface height (with coastal corrections), satellite temperature, in situ CTD data from AUGV and ships (NMFS Ecosystem Monitoring voyages), and all in situ data reported via the WMO GTS network. A climatological data assimilative analysis of hydrographic and long-term mean velocity observations specifies the regional Mean Dynamic Topography that augments altimeter sea level anomaly data and is also used to adjust boundary condition biases that would otherwise be introduced in the process of downscaling from global models. System performance is described with respect to the impact of satellite, CODAR and in situ observations on analysis skill. Results from a 2-way nested modeling system that adds enhanced resolution over the NSF OOI Pioneer Array in the central MAB are also

  11. Assimilating irregularly spaced sparsely observed turbulent signals with hierarchical Bayesian reduced stochastic filters

    International Nuclear Information System (INIS)

    Brown, Kristen A.; Harlim, John

    2013-01-01

    In this paper, we consider a practical filtering approach for assimilating irregularly spaced, sparsely observed turbulent signals through a hierarchical Bayesian reduced stochastic filtering framework. The proposed hierarchical Bayesian approach consists of two steps, blending a data-driven interpolation scheme and the Mean Stochastic Model (MSM) filter. We examine the potential of using the deterministic piecewise linear interpolation scheme and the ordinary kriging scheme in interpolating irregularly spaced raw data to regularly spaced processed data and the importance of dynamical constraint (through MSM) in filtering the processed data on a numerically stiff state estimation problem. In particular, we test this approach on a two-layer quasi-geostrophic model in a two-dimensional domain with a small radius of deformation to mimic ocean turbulence. Our numerical results suggest that the dynamical constraint becomes important when the observation noise variance is large. Second, we find that the filtered estimates with ordinary kriging are superior to those with linear interpolation when observation networks are not too sparse; such robust results are found from numerical simulations with many randomly simulated irregularly spaced observation networks, various observation time intervals, and observation error variances. Third, when the observation network is very sparse, we find that both the kriging and linear interpolations are comparable

  12. Three-dimensional data assimilation and reanalysis of radiation belt electrons: Observations of a four-zone structure using five spacecraft and the VERB code

    Science.gov (United States)

    Kellerman, A. C.; Shprits, Y. Y.; Kondrashov, D.; Subbotin, D.; Makarevich, R. A.; Donovan, E.; Nagai, T.

    2014-11-01

    Obtaining the global state of radiation belt electrons through reanalysis is an important step toward validating our current understanding of radiation belt dynamics and for identification of new physical processes. In the current study, reanalysis of radiation belt electrons is achieved through data assimilation of five spacecraft with the 3-D Versatile Electron Radiation Belt (VERB) code using a split-operator Kalman filter technique. The spacecraft data are cleaned for noise, saturation effects, and then intercalibrated on an individual energy channel basis, by considering phase space density conjunctions in the T96 field model. Reanalysis during the CRRES era reveals a never-before-reported four-zone structure in the Earth's radiation belts during the 24 March 1991 shock-induced injection superstorm: (1) an inner belt, (2) the high-energy shock-injection belt, (3) a remnant outer radiation belt, and (4) a second outer radiation belt. The third belt formed near the same time as the second belt and was later enhanced across keV to MeV energies by a second particle injection observed by CRRES and the Northern Solar Terrestrial Array riometer network. During the recovery phase of the storm, the fourth belt was created near L*=4RE, lasting for several days. Evidence is provided that the fourth belt was likely created by a dominant local heating process. This study outlines the necessity to consider all diffusive processes acting simultaneously and the advantage of supporting ground-based data in quantifying the observed radiation belt dynamics. It is demonstrated that 3-D data assimilation can resolve various nondiffusive processes and provides a comprehensive picture of the electron radiation belts.

  13. Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

    Science.gov (United States)

    Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

    2011-01-01

    The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

  14. Multivariate and multiscale data assimilation in terrestrial systems: a review.

    Science.gov (United States)

    Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

    2012-11-26

    More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a

  15. Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

    Directory of Open Access Journals (Sweden)

    Harry Vereecken

    2012-11-01

    Full Text Available More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF, Particle Filter (PF and variational methods (3/4D-VAR. In this review, we distinguish between four major DA approaches: (1 univariate single-scale DA (UVSS, which is the approach used in the majority of published DA applications, (2 univariate multiscale DA (UVMS referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3 multivariate single-scale DA (MVSS dealing with the assimilation of at least two different data types, and (4 combined multivariate multiscale DA (MVMS. Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a

  16. The dynamic radiation environment assimilation model (DREAM)

    International Nuclear Information System (INIS)

    Reeves, Geoffrey D.; Koller, Josef; Tokar, Robert L.; Chen, Yue; Henderson, Michael G.; Friedel, Reiner H.

    2010-01-01

    The Dynamic Radiation Environment Assimilation Model (DREAM) is a 3-year effort sponsored by the US Department of Energy to provide global, retrospective, or real-time specification of the natural and potential nuclear radiation environments. The DREAM model uses Kalman filtering techniques that combine the strengths of new physical models of the radiation belts with electron observations from long-term satellite systems such as GPS and geosynchronous systems. DREAM includes a physics model for the production and long-term evolution of artificial radiation belts from high altitude nuclear explosions. DREAM has been validated against satellites in arbitrary orbits and consistently produces more accurate results than existing models. Tools for user-specific applications and graphical displays are in beta testing and a real-time version of DREAM has been in continuous operation since November 2009.

  17. Correcting surface solar radiation of two data assimilation systems against FLUXNET observations in North America

    Science.gov (United States)

    Zhao, Lei; Lee, Xuhui; Liu, Shoudong

    2013-09-01

    Solar radiation at the Earth's surface is an important driver of meteorological and ecological processes. The objective of this study is to evaluate the accuracy of the reanalysis solar radiation produced by NARR (North American Regional Reanalysis) and MERRA (Modern-Era Retrospective Analysis for Research and Applications) against the FLUXNET measurements in North America. We found that both assimilation systems systematically overestimated the surface solar radiation flux on the monthly and annual scale, with an average bias error of +37.2 Wm-2 for NARR and of +20.2 Wm-2 for MERRA. The bias errors were larger under cloudy skies than under clear skies. A postreanalysis algorithm consisting of empirical relationships between model bias, a clearness index, and site elevation was proposed to correct the model errors. Results show that the algorithm can remove the systematic bias errors for both FLUXNET calibration sites (sites used to establish the algorithm) and independent validation sites. After correction, the average annual mean bias errors were reduced to +1.3 Wm-2 for NARR and +2.7 Wm-2 for MERRA. Applying the correction algorithm to the global domain of MERRA brought the global mean surface incoming shortwave radiation down by 17.3 W m-2 to 175.5 W m-2. Under the constraint of the energy balance, other radiation and energy balance terms at the Earth's surface, estimated from independent global data products, also support the need for a downward adjustment of the MERRA surface solar radiation.

  18. Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

    Science.gov (United States)

    Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

    2015-04-01

    The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

  19. A Study on the Impact of Observation Assimilation on the Numerical Simulation of Tropical Cyclones JAL and THANE Using 3DVAR

    KAUST Repository

    Viswanadhapalli, Yesubabu; Srinivas, C. V.; Hariprasad, K. B R R; Baskaran, R.

    2013-01-01

    In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region

  20. Lidar data assimilation for improved analyses of volcanic aerosol events

    Science.gov (United States)

    Lange, Anne Caroline; Elbern, Hendrik

    2014-05-01

    Observations of hazardous events with release of aerosols are hardly analyzable by today's data assimilation algorithms, without producing an attenuating bias. Skillful forecasts of unexpected aerosol events are essential for human health and to prevent an exposure of infirm persons and aircraft with possibly catastrophic outcome. Typical cases include mineral dust outbreaks, mostly from large desert regions, wild fires, and sea salt uplifts, while the focus aims for volcanic eruptions. In general, numerical chemistry and aerosol transport models cannot simulate such events without manual adjustments. The concept of data assimilation is able to correct the analysis, as long it is operationally implemented in the model system. Though, the tangent-linear approximation, which describes a substantial precondition for today's cutting edge data assimilation algorithms, is not valid during unexpected aerosol events. As part of the European COPERNICUS (earth observation) project MACC II and the national ESKP (Earth System Knowledge Platform) initiative, we developed a module that enables the assimilation of aerosol lidar observations, even during unforeseeable incidences of extreme emissions of particulate matter. Thereby, the influence of the background information has to be reduced adequately. Advanced lidar instruments comprise on the one hand the aspect of radiative transfer within the atmosphere and on the other hand they can deliver a detailed quantification of the detected aerosols. For the assimilation of maximal exploited lidar data, an appropriate lidar observation operator is constructed, compatible with the EURAD-IM (European Air Pollution and Dispersion - Inverse Model) system. The observation operator is able to map the modeled chemical and physical state on lidar attenuated backscatter, transmission, aerosol optical depth, as well as on the extinction and backscatter coefficients. Further, it has the ability to process the observed discrepancies with lidar

  1. Space Observations for Global Change

    Science.gov (United States)

    Rasool, S. I.

    1991-01-01

    There is now compelling evidence that man's activities are changing both the composition of the atmospheric and the global landscape quite drastically. The consequences of these changes on the global climate of the 21st century is currently a hotly debated subject. Global models of a coupled Earth-ocean-atmosphere system are still very primitive and progress in this area appears largely data limited, specially over the global biosphere. A concerted effort on monitoring biospheric functions on scales from pixels to global and days to decades needs to be coordinated on an international scale in order to address the questions related to global change. An international program of space observations and ground research was described.

  2. Data assimilation in the decision support system RODOS

    International Nuclear Information System (INIS)

    Rojas-Palma, C.; Madsen, H.; Gering, F.; Puch, R.; Turcanu, C.; Astrup, P.; Mueller, H.; Richter, K.; Zheleznyak, M.; Treebushny, D.; Kolomeev, M.; Kamaev, D.; Wynn, H.

    2003-01-01

    Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters,are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data. (author)

  3. Improving Forecast Skill by Assimilation of AIRS Cloud Cleared Radiances RiCC

    Science.gov (United States)

    Susskind, Joel; Rosenberg, Robert I.; Iredell, Lena

    2015-01-01

    ECMWF, NCEP, and GMAO routinely assimilate radiosonde and other in-situ observations along with satellite IR and MW Sounder radiance observations. NCEP and GMAO use the NCEP GSI Data Assimilation System (DAS).GSI DAS assimilates AIRS, CrIS, IASI channel radiances Ri on a channel-by-channel, case-by-case basis, only for those channels i thought to be unaffected by cloud cover. This test excludes Ri for most tropospheric sounding channels under partial cloud cover conditions. AIRS Version-6 RiCC is a derived quantity representative of what AIRS channel i would have seen if the AIRS FOR were cloud free. All values of RiCC have case-by-case error estimates RiCC associated with them. Our experiments present to the GSI QCd values of AIRS RiCC in place of AIRS Ri observations. GSI DAS assimilates only those values of RiCC it thinks are cloud free. This potentially allows for better coverage of assimilated QCd values of RiCC as compared to Ri.

  4. Scalable and balanced dynamic hybrid data assimilation

    Science.gov (United States)

    Kauranne, Tuomo; Amour, Idrissa; Gunia, Martin; Kallio, Kari; Lepistö, Ahti; Koponen, Sampsa

    2017-04-01

    Scalability of complex weather forecasting suites is dependent on the technical tools available for implementing highly parallel computational kernels, but to an equally large extent also on the dependence patterns between various components of the suite, such as observation processing, data assimilation and the forecast model. Scalability is a particular challenge for 4D variational assimilation methods that necessarily couple the forecast model into the assimilation process and subject this combination to an inherently serial quasi-Newton minimization process. Ensemble based assimilation methods are naturally more parallel, but large models force ensemble sizes to be small and that results in poor assimilation accuracy, somewhat akin to shooting with a shotgun in a million-dimensional space. The Variational Ensemble Kalman Filter (VEnKF) is an ensemble method that can attain the accuracy of 4D variational data assimilation with a small ensemble size. It achieves this by processing a Gaussian approximation of the current error covariance distribution, instead of a set of ensemble members, analogously to the Extended Kalman Filter EKF. Ensemble members are re-sampled every time a new set of observations is processed from a new approximation of that Gaussian distribution which makes VEnKF a dynamic assimilation method. After this a smoothing step is applied that turns VEnKF into a dynamic Variational Ensemble Kalman Smoother VEnKS. In this smoothing step, the same process is iterated with frequent re-sampling of the ensemble but now using past iterations as surrogate observations until the end result is a smooth and balanced model trajectory. In principle, VEnKF could suffer from similar scalability issues as 4D-Var. However, this can be avoided by isolating the forecast model completely from the minimization process by implementing the latter as a wrapper code whose only link to the model is calling for many parallel and totally independent model runs, all of them

  5. Displacement data assimilation

    Energy Technology Data Exchange (ETDEWEB)

    Rosenthal, W. Steven [Pacific Northwest Laboratory, Richland, WA 99354 (United States); Venkataramani, Shankar [Department of Mathematics and Program in Applied Mathematics, University of Arizona, Tucson, AZ 85721 (United States); Mariano, Arthur J. [Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149 (United States); Restrepo, Juan M., E-mail: restrepo@math.oregonstate.edu [Department of Mathematics, Oregon State University, Corvallis, OR 97331 (United States)

    2017-02-01

    We show that modifying a Bayesian data assimilation scheme by incorporating kinematically-consistent displacement corrections produces a scheme that is demonstrably better at estimating partially observed state vectors in a setting where feature information is important. While the displacement transformation is generic, here we implement it within an ensemble Kalman Filter framework and demonstrate its effectiveness in tracking stochastically perturbed vortices.

  6. A data assimilation system combining CryoSat-2 data and hydrodynamic river models

    Science.gov (United States)

    Schneider, Raphael; Ridler, Marc-Etienne; Godiksen, Peter Nygaard; Madsen, Henrik; Bauer-Gottwein, Peter

    2018-02-01

    There are numerous hydrologic studies using satellite altimetry data from repeat-orbit missions such as Envisat or Jason over rivers. This study is one of the first examples for the combination of altimetry from drifting-ground track satellite missions, namely CryoSat-2, with a river model. CryoSat-2 SARIn Level 2 data is used to improve a 1D hydrodynamic model of the Brahmaputra River in South Asia, which is based on the Saint-Venant equations for unsteady flow and set up in the MIKE HYDRO River software. After calibration of discharge and water level the hydrodynamic model can accurately and bias-free represent the spatio-temporal variations of water levels. A data assimilation framework has been developed and linked with the model. It is a flexible framework that can assimilate water level data which are arbitrarily distributed in time and space. The setup has been used to assimilate CryoSat-2 water level observations over the Assam valley for the years 2010-2015, using an Ensemble Transform Kalman Filter (ETKF). Performance improvement in terms of discharge forecasting skill was then evaluated. For experiments with synthetic CryoSat-2 data the continuous ranked probability score (CRPS) was improved by up to 32%, whilst for experiments assimilating real data it could be improved by up to 10%. The developed methods are expected to be transferable to other rivers and altimeter missions. The model setup and calibration is based almost entirely on globally available remote sensing data.

  7. The Seasonal Cycle of Water Vapour on Mars from Assimilation of Thermal Emission Spectrometer Data

    Science.gov (United States)

    Steele, Liam J.; Lewis, Stephen R.; Patel, Manish R.; Montmessin, Franck; Forget, Francois; Smith, Michael D.

    2014-01-01

    We present for the first time an assimilation of Thermal Emission Spectrometer (TES) water vapour column data into a Mars global climate model (MGCM). We discuss the seasonal cycle of water vapour, the processes responsible for the observed water vapour distribution, and the cross-hemispheric water transport. The assimilation scheme is shown to be robust in producing consistent reanalyses, and the global water vapour column error is reduced to around 2-4 pr micron depending on season. Wave activity is shown to play an important role in the water vapour distribution, with topographically steered flows around the Hellas and Argyre basins acting to increase transport in these regions in all seasons. At high northern latitudes, zonal wavenumber 1 and 2 stationary waves during northern summer are responsible for spreading the sublimed water vapour away from the pole. Transport by the zonal wavenumber 2 waves occurs primarily to the west of Tharsis and Arabia Terra and, combined with the effects of western boundary currents, this leads to peak water vapour column abundances here as observed by numerous spacecraft. A net transport of water to the northern hemisphere over the course of one Mars year is calculated, primarily because of the large northwards flux of water vapour which occurs during the local dust storm around L(sub S) = 240-260deg. Finally, outlying frost deposits that surround the north polar cap are shown to be important in creating the peak water vapour column abundances observed during northern summer.

  8. Transport pathways of CO in the African upper troposphere during the monsoon season: a study based upon the assimilation of spaceborne observations

    Directory of Open Access Journals (Sweden)

    B. Barret

    2008-06-01

    Full Text Available The transport pathways of carbon monoxide (CO in the African Upper Troposphere (UT during the West African Monsoon (WAM is investigated through the assimilation of CO observations by the Aura Microwave Limb Sounder (MLS in the MOCAGE Chemistry Transport Model (CTM. The assimilation setup, based on a 3-D First Guess at Assimilation Time (3-D-FGAT variational method is described. Comparisons between the assimilated CO fields and in situ airborne observations from the MOZAIC program between Europe and both Southern Africa and Southeast Asia show an overall good agreement around the lowermost pressure level sampled by MLS (~215 hPa. The 4-D assimilated fields averaged over the month of July 2006 have been used to determine the main dynamical processes responsible for the transport of CO in the African UT. The studied period corresponds to the second AMMA (African Monsoon Multidisciplinary Analyses aircraft campaign. At 220 hPa, the CO distribution is characterized by a latitudinal maximum around 5° N mostly driven by convective uplift of air masses impacted by biomass burning from Southern Africa, uplifted within the WAM region and vented predominantly southward by the upper branch of the winter hemisphere Hadley cell. Above 150 hPa, the African CO distribution is characterized by a broad maximum over northern Africa. This maximum is mostly controlled by the large scale UT circulation driven by the Asian Summer Monsoon (ASM and characterized by the Asian Monsoon Anticyclone (AMA centered at 30° N and the Tropical Easterly Jet (TEJ on the southern flank of the anticyclone. Asian pollution uplifted to the UT over large region of Southeast Asia is trapped within the AMA and transported by the anticyclonic circulation over Northeast Africa. South of the AMA, the TEJ is responsible for the tranport of CO-enriched air masses from India and Southeast Asia over Africa. Using the high time resolution provided by the 4-D assimilated fields, we give evidence

  9. A virtual reality catchment for data assimilation experiments

    Science.gov (United States)

    Schalge, Bernd; Rihani, Jehan; Haese, Barbara; Baroni, Gabriele; Erdal, Daniel; Neuweiler, Insa; Hendricks-Franssen, Harrie-Jan; Geppert, Gernot; Ament, Felix; Kollet, Stefan; Cirpka, Olaf; Saavedra, Pablo; Han, Xujun; Attinger, Sabine; Kunstmann, Harald; Vereecken, Harry; Simmer, Clemens

    2016-04-01

    Current data assimilation (DA) systems often lack the possibility to assimilate measurements across compartments to accurately estimate states and fluxes in subsurface-land surface-atmosphere systems (SLAS). In order to develop a new DA framework that is able to realize this cross-compartmental assimilation a comprehensive testing environment is needed. Therefore a virtual reality (VR) catchment is constructed with the Terrestrial System Modeling Platform (TerrSysMP). This catchment mimics the Neckar catchment in Germany. TerrSysMP employs the atmospheric model COSMO, the land surface model CLM and the hydrological model ParFlow coupled with the external coupler OASIS. We will show statistical tests to prove the plausibility of the VR. The VR is running in a fully-coupled mode (subsurface - land surface - atmosphere) which includes the interactions of subsurface dynamics with the atmosphere, such as the effects of soil moisture, which can influence near-surface temperatures, convection patterns or the surface heat fluxes. A reference high resolution run serves as the "truth" from which virtual observations are extracted with observation operators like virtual rain gauges, synoptic stations and satellite observations (amongst others). This effectively solves the otherwise often encountered data scarcity issues with respect to DA. Furthermore an ensemble of model runs at a reduced resolution is performed. This ensemble serves also for open loop runs to be compared with data assimilation experiments. The model runs with this ensemble served to identify sets of parameters that are especially sensitive to changes and have the largest impact on the system. These parameters were the focus of subsequent ensemble simulations and DA experiments. We will show to what extend the VR states can be re-constructed using data assimilation methods with only a limited number of virtual observations available.

  10. Ecohydrological drought monitoring and prediction using a land data assimilation system

    Science.gov (United States)

    Sawada, Y.; Koike, T.

    2017-12-01

    Despite the importance of the ecological and agricultural aspects of severe droughts, few drought monitor and prediction systems can forecast the deficit of vegetation growth. To address this issue, we have developed a land data assimilation system (LDAS) which can simultaneously simulate soil moisture and vegetation dynamics. By assimilating satellite-observed passive microwave brightness temperature, which is sensitive to both surface soil moisture and vegetation water content, we can significantly improve the skill of a land surface model to simulate surface soil moisture, root zone soil moisture, and leaf area index (LAI). We run this LDAS to generate a global ecohydrological land surface reanalysis product. In this presentation, we will demonstrate how useful this new reanalysis product is to monitor and analyze the historical mega-droughts. In addition, using the analyses of soil moistures and LAI as initial conditions, we can forecast the ecological and hydrological conditions in the middle of droughts. We will present our recent effort to develop a near real time ecohydrological drought monitoring and prediction system in Africa by combining the LDAS and the atmospheric seasonal prediction.

  11. Total kinetic energy in four global eddying ocean circulation models and over 5000 current meter records

    KAUST Repository

    Scott, Robert B.

    2010-01-01

    We compare the total kinetic energy (TKE) in four global eddying ocean circulation simulations with a global dataset of over 5000, quality controlled, moored current meter records. At individual mooring sites, there was considerable scatter between models and observations that was greater than estimated statistical uncertainty. Averaging over all current meter records in various depth ranges, all four models had mean TKE within a factor of two of observations above 3500. m, and within a factor of three below 3500. m. With the exception of observations between 20 and 100. m, the models tended to straddle the observations. However, individual models had clear biases. The free running (no data assimilation) model biases were largest below 2000. m. Idealized simulations revealed that the parameterized bottom boundary layer tidal currents were not likely the source of the problem, but that reducing quadratic bottom drag coefficient may improve the fit with deep observations. Data assimilation clearly improved the model-observation comparison, especially below 2000. m, despite assimilated data existing mostly above this depth and only south of 47°N. Different diagnostics revealed different aspects of the comparison, though in general the models appeared to be in an eddying-regime with TKE that compared reasonably well with observations. © 2010 Elsevier Ltd.

  12. a Thtee-Dimensional Variational Assimilation Scheme for Satellite Aod

    Science.gov (United States)

    Liang, Y.; Zang, Z.; You, W.

    2018-04-01

    A three-dimensional variational data assimilation scheme is designed for satellite AOD based on the IMPROVE (Interagency Monitoring of Protected Visual Environments) equation. The observation operator that simulates AOD from the control variables is established by the IMPROVE equation. All of the 16 control variables in the assimilation scheme are the mass concentrations of aerosol species from the Model for Simulation Aerosol Interactions and Chemistry scheme, so as to take advantage of this scheme in providing comprehensive analyses of species concentrations and size distributions as well as be calculating efficiently. The assimilation scheme can save computational resources as the IMPROVE equation is a quadratic equation. A single-point observation experiment shows that the information from the single-point AOD is effectively spread horizontally and vertically.

  13. Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

    Science.gov (United States)

    Kim, Min-Jeong; Jin, Jianjun; McCarty, Will; El Akkraoui, Amal; Todling, Ricardo; Gelaro, Ron

    2018-01-01

    Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit

  14. Air Quality Modeling Using the NASA GEOS-5 Multispecies Data Assimilation System

    Science.gov (United States)

    Keller, Christoph A.; Pawson, Steven; Wargan, Krzysztof; Weir, Brad

    2018-01-01

    The NASA Goddard Earth Observing System (GEOS) data assimilation system (DAS) has been expanded to include chemically reactive tropospheric trace gases including ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). This system combines model analyses from the GEOS-5 model with detailed atmospheric chemistry and observations from MLS (O3), OMI (O3 and NO2), and MOPITT (CO). We show results from a variety of assimilation test experiments, highlighting the improvements in the representation of model species concentrations by up to 50% compared to an assimilation-free control experiment. Taking into account the rapid chemical cycling of NO2 when applying the assimilation increments greatly improves assimilation skills for NO2 and provides large benefits for model concentrations near the surface. Analysis of the geospatial distribution of the assimilation increments suggest that the free-running model overestimates biomass burning emissions but underestimates lightning NOx emissions by 5-20%. We discuss the capability of the chemical data assimilation system to improve atmospheric composition forecasts through improved initial value and boundary condition inputs, particularly during air pollution events. We find that the current assimilation system meaningfully improves short-term forecasts (1-3 day). For longer-term forecasts more emphasis on updating the emissions instead of initial concentration fields is needed.

  15. Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

    Science.gov (United States)

    Peters-Lidard, Christa D.; Kumar, Sujay V.; Santanello, Joseph A., Jr.; Reichle, Rolf H.

    2009-01-01

    The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters- Lidard et al.,2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected ase co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations. In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by

  16. Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy)

    Science.gov (United States)

    Mazzoleni, Maurizio; Cortes Arevalo, Vivian Juliette; Wehn, Uta; Alfonso, Leonardo; Norbiato, Daniele; Monego, Martina; Ferri, Michele; Solomatine, Dimitri P.

    2018-01-01

    To improve hydrological predictions, real-time measurements derived from traditional physical sensors are integrated within mathematic models. Recently, traditional sensors are being complemented with crowdsourced data (social sensors). Although measurements from social sensors can be low cost and more spatially distributed, other factors like spatial variability of citizen involvement, decreasing involvement over time, variable observations accuracy and feasibility for model assimilation play an important role in accurate flood predictions. Only a few studies have investigated the benefit of assimilating uncertain crowdsourced data in hydrological and hydraulic models. In this study, we investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess improvements in the model prediction performance for different spatial-temporal scenarios of citizen involvement levels. To that end, we simulate an extreme flood event that occurred in the Bacchiglione catchment (Italy) in May 2013 using a semi-distributed hydrological model with the station at Ponte degli Angeli (Vicenza) as the prediction-validation point. A conceptual hydrological model is implemented by the Alto Adriatico Water Authority and it is used to estimate runoff from the different sub-catchments, while a hydraulic model is implemented to propagate the flow along the river reach. In both models, a Kalman filter is implemented to assimilate the crowdsourced observations. Synthetic crowdsourced observations are generated for either static social or dynamic social sensors because these measures were not available at the time of the study. We consider two sets of experiments: (i) assuming random probability of receiving crowdsourced observations and (ii) using theoretical scenarios of citizen motivations, and consequent involvement levels, based on population distribution. The results demonstrate the

  17. Assimilative Modeling of Ionospheric Disturbances with FORMOSAT-3/COSMIC and Ground-Based GPS Measurements

    Directory of Open Access Journals (Sweden)

    Xiaoqing Pi

    2009-01-01

    Full Text Available The four-dimensional Global Assimilative Ionospheric Model (GAIM is applied to a study of ionospheric disturbances. The investigation is focused on disturbance features, particularly in the altitude and latitude dimensions, at low latitudes during a geomagnetic storm on 7 August 2006, under solar minimum conditions. The modeling of storm-time ionospheric state (electron density is conducted by assimilating an unprecedented volume of line-of-sight TEC data collected by the Global Positioning System (GPS occultation receivers on board six FORMOSAT-3/COSMIC satellites and geodetic-quality GPS receivers at two hundred globally-distributed ground tracking stations.With a band-limited Kalman filter technique to update the ionospheric state, the assimilative modeling reveals a pronounced enhancement in the equatorial anomaly in the East Asia sector during dusk and evening hours. The disturbance characteristics, obtained by comparing with the quiet conditions prior to the storm also modeled in this study through data assimilation, include lifted F layer and reduced electron density in the equatorial region, enhanced density at the magnetically conjugate anomaly latitudes, and tilted feature of density increase towards higher altitudes at lower latitudes. The characteristics are attributed to the enhanced plasma fountain effect driven by an enhanced eastward zonal electric field. These results enable us to distinguish the storm-time electric field perturbations clearly from other sources during the storm. The possible origins of electric field perturbations are also discussed, including penetration of the magnetospheric electric field and wind dynamo disturbances.

  18. A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

    Science.gov (United States)

    Hoffman, Ross N.

    1993-01-01

    A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

  19. A simple nudging scheme to assimilate ASCAT soil moisture data in the WRF model

    Science.gov (United States)

    Capecchi, V.; Gozzini, B.

    2012-04-01

    The present work shows results obtained in a numerical experiment using the WRF (Weather and Research Forecasting, www.wrf-model.org) model. A control run where soil moisture is constrained by GFS global analysis is compared with a test run where soil moisture analysis is obtained via a simple nudging scheme using ASCAT data. The basic idea of the assimilation scheme is to "nudge" the first level (0-10 cm below ground in NOAH model) of volumetric soil moisture of the first-guess (say θ(b,1) derived from global model) towards the ASCAT derived value (say ^θ A). The soil moisture analysis θ(a,1) is given by: { θ + K (^θA - θ ) l = 1 θ(a,1) = θ(b,l) (b,l) l > 1 (b,l) (1) where l is the model soil level. K is a constant scalar value that is user specified and in this study it is equal to 0.2 (same value as in similar studies). Soil moisture is critical for estimating latent and sensible heat fluxes as well as boundary layer structure. This parameter is, however, poorly assimilated in current global and regional numerical models since no extensive soil moisture observation network exists. Remote sensing technologies offer a synoptic view of the dynamics and spatial distribution of soil moisture with a frequent temporal coverage and with a horizontal resolution similar to mesoscale NWP model. Several studies have shown that measurements of normalized backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) operating at microwave frequencies and boarded on the meteorological operational (Metop) satellite, offer quality information about surface soil moisture. Recently several studies deal with the implementation of simple assimilation procedures (nudging, Extended Kalman Filter, etc...) to integrate ASCAT data in NWP models. They found improvements in screen temperature predictions, particularly in areas such as North-America and in the Tropics, where it is strong the land-atmosphere coupling. The ECMWF (Newsletter No. 127) is currently

  20. Lessons Learned from Assimilating Altimeter Data into a Coupled General Circulation Model with the GMAO Augmented Ensemble Kalman Filter

    Science.gov (United States)

    Keppenne, Christian; Vernieres, Guillaume; Rienecker, Michele; Jacob, Jossy; Kovach, Robin

    2011-01-01

    Satellite altimetry measurements have provided global, evenly distributed observations of the ocean surface since 1993. However, the difficulties introduced by the presence of model biases and the requirement that data assimilation systems extrapolate the sea surface height (SSH) information to the subsurface in order to estimate the temperature, salinity and currents make it difficult to optimally exploit these measurements. This talk investigates the potential of the altimetry data assimilation once the biases are accounted for with an ad hoc bias estimation scheme. Either steady-state or state-dependent multivariate background-error covariances from an ensemble of model integrations are used to address the problem of extrapolating the information to the sub-surface. The GMAO ocean data assimilation system applied to an ensemble of coupled model instances using the GEOS-5 AGCM coupled to MOM4 is used in the investigation. To model the background error covariances, the system relies on a hybrid ensemble approach in which a small number of dynamically evolved model trajectories is augmented on the one hand with past instances of the state vector along each trajectory and, on the other, with a steady state ensemble of error estimates from a time series of short-term model forecasts. A state-dependent adaptive error-covariance localization and inflation algorithm controls how the SSH information is extrapolated to the sub-surface. A two-step predictor corrector approach is used to assimilate future information. Independent (not-assimilated) temperature and salinity observations from Argo floats are used to validate the assimilation. A two-step projection method in which the system first calculates a SSH increment and then projects this increment vertically onto the temperature, salt and current fields is found to be most effective in reconstructing the sub-surface information. The performance of the system in reconstructing the sub-surface fields is particularly

  1. Information-Based Analysis of Data Assimilation (Invited)

    Science.gov (United States)

    Nearing, G. S.; Gupta, H. V.; Crow, W. T.; Gong, W.

    2013-12-01

    Data assimilation is defined as the Bayesian conditioning of uncertain model simulations on observations for the purpose of reducing uncertainty about model states. Practical data assimilation methods make the application of Bayes' law tractable either by employing assumptions about the prior, posterior and likelihood distributions (e.g., the Kalman family of filters) or by using resampling methods (e.g., bootstrap filter). We propose to quantify the efficiency of these approximations in an OSSE setting using information theory and, in an OSSE or real-world validation setting, to measure the amount - and more importantly, the quality - of information extracted from observations during data assimilation. To analyze DA assumptions, uncertainty is quantified as the Shannon-type entropy of a discretized probability distribution. The maximum amount of information that can be extracted from observations about model states is the mutual information between states and observations, which is equal to the reduction in entropy in our estimate of the state due to Bayesian filtering. The difference between this potential and the actual reduction in entropy due to Kalman (or other type of) filtering measures the inefficiency of the filter assumptions. Residual uncertainty in DA posterior state estimates can be attributed to three sources: (i) non-injectivity of the observation operator, (ii) noise in the observations, and (iii) filter approximations. The contribution of each of these sources is measurable in an OSSE setting. The amount of information extracted from observations by data assimilation (or system identification, including parameter estimation) can also be measured by Shannon's theory. Since practical filters are approximations of Bayes' law, it is important to know whether the information that is extracted form observations by a filter is reliable. We define information as either good or bad, and propose to measure these two types of information using partial

  2. Global Mercury Observation System (GMOS) surface observation data.

    Data.gov (United States)

    U.S. Environmental Protection Agency — GMOS global surface elemental mercury (Hg0) observations from 2013 & 2014. This dataset is associated with the following publication: Sprovieri, F., N. Pirrone,...

  3. The Impact of AMSU-A Radiance Assimilation in the U.S. Navy's Operational Global Atmospheric Prediction System (NOGAPS)

    National Research Council Canada - National Science Library

    Baker, Nancy L; Hogan, T. F; Campbell, W. F; Pauley, R. L; Swadley, S. D

    2005-01-01

    ...) sensor suite onboard NOAA 15 and 16 for NOGAPS. The direct assimilation of AMSU-A radiances replaced the assimilation of ATOVS temperature retrievals produced by NOAA's National Environmental Satellite, Data and Information Service (NESDIS...

  4. Using data assimilation to study extratropical Northern Hemisphere climate over the last millennium

    Directory of Open Access Journals (Sweden)

    M. Widmann

    2010-09-01

    Full Text Available Climate proxy data provide noisy, and spatially incomplete information on some aspects of past climate states, whereas palaeosimulations with climate models provide global, multi-variable states, which may however differ from the true states due to unpredictable internal variability not related to climate forcings, as well as due to model deficiencies. Using data assimilation for combining the empirical information from proxy data with the physical understanding of the climate system represented by the equations in a climate model is in principle a promising way to obtain better estimates for the climate of the past.

    Data assimilation has been used for a long time in weather forecasting and atmospheric analyses to control the states in atmospheric General Circulation Models such that they are in agreement with observation from surface, upper air, and satellite measurements. Here we discuss the similarities and the differences between the data assimilation problem in palaeoclimatology and in weather forecasting, and present and conceptually compare three data assimilation methods that have been developed in recent years for applications in palaeoclimatology. All three methods (selection of ensemble members, Forcing Singular Vectors, and Pattern Nudging are illustrated by examples that are related to climate variability over the extratropical Northern Hemisphere during the last millennium. In particular it is shown that all three methods suggest that the cold period over Scandinavia during 1790–1820 is linked to anomalous northerly or easterly atmospheric flow, which in turn is related to a pressure anomaly that resembles a negative state of the Northern Annular Mode.

  5. Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

    Science.gov (United States)

    Blankenship, Clay; Case, Jonathan; Zavodsky, Bradley; Jedlovec, Gary

    2014-01-01

    Soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) instrument are assimilated into the Noah land surface model (LSM) within the NASA Land Information System (LIS). Before assimilation, SMOS retrievals are bias-corrected to match the model climatological distribution using a Cumulative Distribution Function (CDF) matching approach. Data assimilation is done via the Ensemble Kalman Filter. The goal is to improve the representation of soil moisture within the LSM, and ultimately to improve numerical weather forecasts through better land surface initialization. We present a case study showing a large area of irrigation in the lower Mississippi River Valley, in an area with extensive rice agriculture. High soil moisture value in this region are observed by SMOS, but not captured in the forcing data. After assimilation, the model fields reflect the observed geographic patterns of soil moisture. Plans for a modeling experiment and operational use of the data are given. This work helps prepare for the assimilation of Soil Moisture Active/Passive (SMAP) retrievals in the near future.

  6. Variational Assimilation of Sparse and Uncertain Satellite Data For 1D Saint-Venant River Models

    Science.gov (United States)

    Garambois, P. A.; Brisset, P.; Monnier, J.; Roux, H.

    2016-12-01

    Profusion of satellites are providing increasingly accurate measurements of continental water cyle, and water bodies variations while in situ observability is declining. The future Surface Water and Ocean Topography (SWOT) mission will provide maps of river surface elevations widths and slopes with an almost global coverage and temporal revisits. This will offer the possibility to address a larger variety of inverse problems in surface hydrology. Data assimilation techniques, that are broadly used in several scientific fields, aim to optimally combine models, system observations and prior information. Variational assimilation consists in iterative minimization of a discrepency measure between model outputs and observations, here for retrieving boundary conditions and parameters of a 1D Saint Venant model. Nevertheless, inferring river discharge and hydraulic parameters thanks to the observation of river surface is not straightforward. This is particularly true in the case of sparse and uncertain observations of flow state variables since they are governed by nonlinear physical processes. This paper investigates the identifiability of hydraulic controls given sparse and uncertain satellite observations of a river. The identifiability of river discharge alone and with roughness is tested for several spatio temporal patterns of river observations, including SWOT like observations. A new 1D Shallow water model with variational data assimilation, within the DassFlow chain is presented as well as postprocessing and observation operator dedicated to the future SWOT and SWOT simulator data. In view to decrease inverse problem dimensionality discharge is represented in a reduced basis. Moreover we introduce an original and reduced parametrization of the flow resistance that can account for various flow regimes along with a cross section design dedicated to remote sensing. We show which discharge temporal frequencies can be identified w.r.t observation ones and at which

  7. Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction

    Science.gov (United States)

    Li, Zhijin; Chao, Yi; Li, P. Peggy

    2012-01-01

    A multi-scale three-dimensional variational data assimilation system (MS-3DVAR) has been formulated and the associated software system has been developed for improving high-resolution coastal ocean prediction. This system helps improve coastal ocean prediction skill, and has been used in support of operational coastal ocean forecasting systems and field experiments. The system has been developed to improve the capability of data assimilation for assimilating, simultaneously and effectively, sparse vertical profiles and high-resolution remote sensing surface measurements into coastal ocean models, as well as constraining model biases. In this system, the cost function is decomposed into two separate units for the large- and small-scale components, respectively. As such, data assimilation is implemented sequentially from large to small scales, the background error covariance is constructed to be scale-dependent, and a scale-dependent dynamic balance is incorporated. This scheme then allows effective constraining large scales and model bias through assimilating sparse vertical profiles, and small scales through assimilating high-resolution surface measurements. This MS-3DVAR enhances the capability of the traditional 3DVAR for assimilating highly heterogeneously distributed observations, such as along-track satellite altimetry data, and particularly maximizing the extraction of information from limited numbers of vertical profile observations.

  8. Assimilation of SMOS Soil Moisture Retrievals in the Land Information System

    Science.gov (United States)

    Blakenship, Clay; Zavodsky, Bradley; Cae, Jonathan

    2014-01-01

    Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.

  9. Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

    Science.gov (United States)

    De Lannoy, Gabrielle J. M.; Reichle, Rolf H.

    2016-01-01

    Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degree incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval

  10. Assimilation of NUCAPS Retrieved Profiles in GSI for Unique Forecasting Applications

    Science.gov (United States)

    Berndt, Emily Beth; Zavodsky, Bradley; Srikishen, Jayanthi; Blankenship, Clay

    2015-01-01

    Hyperspectral IR profiles can be assimilated in GSI as a separate observation other than radiosondes with only changes to tables in the fix directory. Assimilation of profiles does produce changes to analysis fields and evidenced by: Innovations larger than +/-2.0 K are present and represent where individual profiles impact the final temperature analysis.The updated temperature analysis is colder behind the cold front and warmer in the warm sector. The updated moisture analysis is modified more in the low levels and tends to be drier than the original model background Analysis of model output shows: Differences relative to 13-km RAP analyses are smaller when profiles are assimilated with NUCAPS errors. CAPE is under-forecasted when assimilating NUCAPS profiles, which could be problematic for severe weather forecasting Refining the assimilation technique to incorporate an error covariance matrix and creating a separate GSI module to assimilate satellite profiles may improve results.

  11. Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

    Science.gov (United States)

    Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.

    2016-01-01

    Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

  12. UNIFICATION AND APPLICATIONS OF MODERN OCEANIC/ATMOSPHERIC DATA ASSIMILATION ALGORITHMS

    Institute of Scientific and Technical Information of China (English)

    QIAO Fang-li; ZHANG Shao-qing; YUAN Ye-li

    2004-01-01

    The key mathematics and applications of various modern atmospheric/oceanic data assimilation methods including Optimal Interpolation(OI),4-dimensional variational approach(4D-Var)and filters were systematically reviewed and classified.Based on the data assimilation philosophy,I.e.,using model dynamics to extract the observational information,the common character of the problem,such as the probabilistic nature of the evolution of the atmospheric/oceanic system,noisy and irregularly spaced observations,and the advantages and disadvantages of these data assimilation algorithms,were discussed.In the filtering framework,all modern data assimilation algorithms were unified: OI/3D-Var is a stationary filter,4D-Var is a linear(Kalman)filter and an ensemble of Kalman filters is able to construct a nonlinear filter.The nonlinear filter such as the Ensemble Kalman Filter(ENKF),Ensemble Adjustment Kalman Filter(EAKF)and Ensemble Transformation Kalman Filter(ETKF)can,to some extent,account for the non-Gaussian information of the prior distribution from the model.The flow-dependent covariance estimated by an ensemble filter may be introduced to OI and 4D-Var to improve these traditional algorithms.In practice,the performance of algorithms may depend on the specific numerical model and the choice of algorithm may depend on the specific problem.However,the unification of algorithms allows us to establish a unified test system to evaluate these algorithms,which provides more insights into data assimilation philosophies and helps improve data assimilation techniques.

  13. Volcanic Ash Data Assimilation System for Atmospheric Transport Model

    Science.gov (United States)

    Ishii, K.; Shimbori, T.; Sato, E.; Tokumoto, T.; Hayashi, Y.; Hashimoto, A.

    2017-12-01

    The Japan Meteorological Agency (JMA) has two operations for volcanic ash forecasts, which are Volcanic Ash Fall Forecast (VAFF) and Volcanic Ash Advisory (VAA). In these operations, the forecasts are calculated by atmospheric transport models including the advection process, the turbulent diffusion process, the gravitational fall process and the deposition process (wet/dry). The initial distribution of volcanic ash in the models is the most important but uncertain factor. In operations, the model of Suzuki (1983) with many empirical assumptions is adopted to the initial distribution. This adversely affects the reconstruction of actual eruption plumes.We are developing a volcanic ash data assimilation system using weather radars and meteorological satellite observation, in order to improve the initial distribution of the atmospheric transport models. Our data assimilation system is based on the three-dimensional variational data assimilation method (3D-Var). Analysis variables are ash concentration and size distribution parameters which are mutually independent. The radar observation is expected to provide three-dimensional parameters such as ash concentration and parameters of ash particle size distribution. On the other hand, the satellite observation is anticipated to provide two-dimensional parameters of ash clouds such as mass loading, top height and particle effective radius. In this study, we estimate the thickness of ash clouds using vertical wind shear of JMA numerical weather prediction, and apply for the volcanic ash data assimilation system.

  14. Retrieval Assimilation and Modeling of Atmospheric Water Vapor from Ground- and Space-Based GPS Networks: Investigation of the Global and Regional Hydrological Cycles

    Science.gov (United States)

    Dickey, Jean O.

    1999-01-01

    Uncertainty over the response of the atmospheric hydrological cycle (particularly the distribution of water vapor and cloudiness) to anthropogenic forcing is a primary source of doubt in current estimates of global climate sensitivity, which raises severe difficulties in evaluating its likely societal impact. Fortunately, a variety of advanced techniques and sensors are beginning to shed new light on the atmospheric hydrological cycle. One of the most promising makes use of the sensitivity of the Global Positioning System (GPS) to the thermodynamic state, and in particular the water vapor content, of the atmosphere through which the radio signals propagate. Our strategy to derive the maximum benefit for hydrological studies from the rapidly increasing GPS data stream will proceed in three stages: (1) systematically analyze and archive quality-controlled retrievals using state-of-the-art techniques; (2) employ both currently available and innovative assimilation procedures to incorporate these determinations into advanced regional and global atmospheric models and assess their effects; and (3) apply the results to investigate selected scientific issues of relevance to regional and global hydrological studies. An archive of GPS-based estimation of total zenith delay (TZD) data and water vapor where applicable has been established with expanded automated quality control. The accuracy of the GPS estimates is being monitored; the investigation of systematic errors is ongoing using comparisons with water vapor radiometers. Meteorological packages have been implemented. The accuracy and utilization of the TZD estimates has been improved by implementing a troposphere gradient model. GPS-based gradients have been validated as real atmospheric moisture gradients, establishing a link between the estimated gradients and the passage of weather fronts. We have developed a generalized ray tracing inversion scheme that can be used to analyze occultation data acquired from space

  15. Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

    Directory of Open Access Journals (Sweden)

    Akhilesh S. Nair

    2016-11-01

    Full Text Available In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM and the Soil Moisture Operational Products System (SMOPS observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS using the Land Parameter Retrieval Model (LPRM AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD of 0.0303 m3/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb at an incidence angle of 55° using the Community Microwave Emission Model (CMEM Radiative transfer Model (RTM. This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.

  16. AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system

    Directory of Open Access Journals (Sweden)

    Chun Yang

    2016-06-01

    Full Text Available A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2 was developed within the Weather Research and Forecasting (WRF model's data assimilation (WRFDA system. The four essential elements are: (1 extending the community radiative transform model's (CRTM interface to include hydrometeor profiles; (2 using total water Qt as the moisture control variable; (3 using a warm-rain physics scheme for partitioning the Qt increment into individual increments of water vapour, cloud liquid water and rain; and (4 adopting a symmetric observation error model for all-sky radiance assimilation.Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or all-sky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012 was assessed through analysis/forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP, warm-core structure and cloud distribution. Substantial (>20 % error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the all-sky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment.

  17. Modeling whole-tree carbon assimilation rate using observed transpiration rates and needle sugar carbon isotope ratios.

    Science.gov (United States)

    Hu, Jia; Moore, David J P; Riveros-Iregui, Diego A; Burns, Sean P; Monson, Russell K

    2010-03-01

    *Understanding controls over plant-atmosphere CO(2) exchange is important for quantifying carbon budgets across a range of spatial and temporal scales. In this study, we used a simple approach to estimate whole-tree CO(2) assimilation rate (A(Tree)) in a subalpine forest ecosystem. *We analysed the carbon isotope ratio (delta(13)C) of extracted needle sugars and combined it with the daytime leaf-to-air vapor pressure deficit to estimate tree water-use efficiency (WUE). The estimated WUE was then combined with observations of tree transpiration rate (E) using sap flow techniques to estimate A(Tree). Estimates of A(Tree) for the three dominant tree species in the forest were combined with species distribution and tree size to estimate and gross primary productivity (GPP) using an ecosystem process model. *A sensitivity analysis showed that estimates of A(Tree) were more sensitive to dynamics in E than delta(13)C. At the ecosystem scale, the abundance of lodgepole pine trees influenced seasonal dynamics in GPP considerably more than Engelmann spruce and subalpine fir because of its greater sensitivity of E to seasonal climate variation. *The results provide the framework for a nondestructive method for estimating whole-tree carbon assimilation rate and ecosystem GPP over daily-to weekly time scales.

  18. Leaf nitrogen assimilation and partitioning differ among subtropical forest plants in response to canopy addition of nitrogen treatments

    Science.gov (United States)

    Nan Liu; Shuhua Wu; Qinfeng Guo; Jiaxin Wang; Ce Cao; Jun Wang

    2018-01-01

    Global increases in nitrogen deposition may alter forest structure and function by interferingwith plant nitrogen metabolism (e.g., assimilation and partitioning) and subsequent carbon assimilation, but it is unclear how these responses to nitrogen deposition differ among species. In this study, we conducted a 2-year experiment to investigate the effects of canopy...

  19. Error Covariance Estimation of Mesoscale Data Assimilation

    National Research Council Canada - National Science Library

    Xu, Qin

    2005-01-01

    The goal of this project is to explore and develop new methods of error covariance estimation that will provide necessary statistical descriptions of prediction and observation errors for mesoscale data assimilation...

  20. Impact of streamflow data assimilation and length of the verification period on the quality of short-term ensemble hydrologic forecasts

    Science.gov (United States)

    Randrianasolo, A.; Thirel, G.; Ramos, M. H.; Martin, E.

    2014-11-01

    Data assimilation has gained wide recognition in hydrologic forecasting due mainly to its capacity to improve the quality of short-term forecasts. In this study, a comparative analysis is conducted to assess the impact of discharge data assimilation on the quality of streamflow forecasts issued by two different modeling conceptualizations of catchment response. The sensitivity of the performance metrics to the length of the verification period is also investigated. The hydrological modeling approaches are: the coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU, a distributed model with a data assimilation procedure that uses streamflow measurements to assess the initial state of soil water content that optimizes discharge simulations, and the lumped soil moisture-accounting type rainfall-runoff model GRP, which assimilates directly the last observed discharge to update the state of the routing store. The models are driven by the weather ensemble prediction system PEARP of Météo-France, which is based on the global spectral ARPEGE model zoomed over France. It runs 11 perturbed members for a forecast range of 60 h. Forecast and observed data are available for 86 catchments over a 17-month period (March 2005-July 2006) for both models and for 82 catchments over a 52-month period (April 2005-July 2009) for the GRP model. The first dataset is used to investigate the impact of streamflow data assimilation on forecast quality, while the second is used to evaluate the impact of the length of the verification period on the assessment of forecast quality. Forecasts are compared to daily observed discharges and scores are computed for lead times 24 h and 48 h. Results indicate an overall good performance of both hydrological models forced by the PEARP ensemble predictions when the models are run with their data assimilation procedures. In general, when data assimilation is performed, the quality of the forecasts increases: median differences between

  1. Advances in the development of an integrated data assimilation and sounding system

    International Nuclear Information System (INIS)

    Dabberdt, W.F.; Parsons, D.; Kuo, Y.H.; Dudhia, J.; Guo, Y.R.; Van Baelen, J.; Martin, C.; Oncley, S.

    1994-01-01

    The Integrated Data Assimilation and Sounding System (IDASS) provides continuous high-resolution tropospheric profiles. The measurement system (Integrated Sounding System, or ISS) is developed around a suite of in situ and active and passive remote sensors. Observations from ISS networks provide a high-resolution description of atmospheric structure on the mesoscale. Measurements are coupled with a state-of-the-art mesoscale modeling system. The mesoscale data assimilation scheme is the Newtonian nudging technique. In the mesoscale data assimilation process, observations of wind, temperature, and humidity are used to nudge or relax the time-dependent model variables to the observed values. The end product is a highly resolved four-dimensional meteorological data set (including three components of wind, temperature, humidity, cloud water, and integrated moisture)

  2. Nitrogen assimilation in denitrifier Bacillus azotoformans LMG 9581T.

    Science.gov (United States)

    Sun, Yihua; De Vos, Paul; Willems, Anne

    2017-12-01

    Until recently, it has not been generally known that some bacteria can contain the gene inventory for both denitrification and dissimilatory nitrate (NO 3 - )/nitrite (NO 2 - ) reduction to ammonium (NH 4 + ) (DNRA). Detailed studies of these microorganisms could shed light on the differentiating environmental drivers of both processes without interference of organism-specific variation. Genome analysis of Bacillus azotoformans LMG 9581 T shows a remarkable redundancy of dissimilatory nitrogen reduction, with multiple copies of each denitrification gene as well as DNRA genes nrfAH, but a reduced capacity for nitrogen assimilation, with no nas operon nor amtB gene. Here, we explored nitrogen assimilation in detail using growth experiments in media with different organic and inorganic nitrogen sources at different concentrations. Monitoring of growth, NO 3 - NO 2 - , NH 4 + concentration and N 2 O production revealed that B. azotoformans LMG 9581 T could not grow with NH 4 + as sole nitrogen source and confirmed the hypothesis of reduced nitrogen assimilation pathways. However, NH 4 + could be assimilated and contributed up to 50% of biomass if yeast extract was also provided. NH 4 + also had a significant but concentration-dependent influence on growth rate. The mechanisms behind these observations remain to be resolved but hypotheses for this deficiency in nitrogen assimilation are discussed. In addition, in all growth conditions tested a denitrification phenotype was observed, with all supplied NO 3 - converted to nitrous oxide (N 2 O).

  3. Assimilating Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Observations and the Relative Value of Other Observation Types

    Science.gov (United States)

    2014-08-01

    US Army Research Laboratory ATTN: RDRL- CIE -M 2800 Powder Mill Road Adelphi MD 20783-1197 8. PERFORMING ORGANIZATION REPORT NUMBER ARL-TR...into account both the improvements and the degradations caused by the data assimilation. In general, the PCI decreases with increasing nudging strength...This is designed to account for the smaller-scale features that are resolvable on finer-resolution model forecasts that may result in smaller error

  4. Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

    Science.gov (United States)

    Gruber, A.; Crow, W. T.; Dorigo, W. A.

    2018-02-01

    Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

  5. Data Synthesis and Data Assimilation at Global Change Experiments and Fluxnet Toward Improving Land Process Models

    Energy Technology Data Exchange (ETDEWEB)

    Luo, Yiqi [Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology

    2017-09-12

    The project was conducted during the period from 7/1/2012 to 6/30/2017 with three major tasks: (1) data synthesis and development of data assimilation (DA) techniques to constrain modeled ecosystem feedback to climate change; (2) applications of DA techniques to improve process models at different scales from ecosystem to regions and the globe; and 3) improvements of modeling soil carbon (C) dynamics by land surface models. During this period, we have synthesized published data from soil incubation experiments (e.g., Chen et al., 2016; Xu et al., 2016; Feng et al., 2016), global change experiments (e.g., Li et al., 2013; Shi et al., 2015, 2016; Liang et al., 2016) and fluxnet (e.g., Niu et al., 2012., Xia et al., 2015; Li et al., 2016). These data have been organized into multiple data products and have been used to identify general mechanisms and estimate parameters for model improvement. We used the data sets that we collected and the DA techniques to improve model performance of both ecosystem models and global land models. The objectives are: 1) to improve model simulations of litter and soil carbon storage (e.g., Schädel et al., 2013; Hararuk and Luo, 2014; Hararuk et al., 2014; Liang et al., 2015); 2) to explore the effects of CO2, warming and precipitation on ecosystem processes (e.g., van Groenigen et al., 2014; Shi et al., 2015, 2016; Feng et al., 2017); and 3) to estimate parameters variability in different ecosystems (e.g., Li et al., 2016). We developed a traceability framework, which was based on matrix approaches and decomposed the modeled steady-state terrestrial ecosystem carbon storage capacity into four can trace the difference in ecosystem carbon storage capacity among different biomes to four traceable components: net primary productivity (NPP), baseline C residence times, environmental scalars and climate forcing (Xia et al., 2013). With this framework, we can diagnose the differences in modeled carbon storage across ecosystems

  6. Evaluation of a Soil Moisture Data Assimilation System Over West Africa

    Science.gov (United States)

    Bolten, J. D.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.

    2009-05-01

    A crucial requirement of global crop yield forecasts by the U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting soil moisture, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates global soil moisture using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer soil moisture model which calculates the daily amount of soil moisture withdrawn by evapotranspiration and replenished by precipitation. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9°N- 20°N; 20°W-20°E). This region contains many key agricultural areas and has a high agro- meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model

  7. Impact of AIRS radiance in the NCUM 4D-VAR assimilation system

    Science.gov (United States)

    Srinivas, Desamsetti; Indira Rani, S.; Mallick, Swapan; George, John P.; Sharma, Priti

    2016-04-01

    The hyperspectral radiances from Atmospheric InfraRed Sounder (AIRS), on board NASA-AQUA satellite, have been processed through the Observation Processing System (OPS) and assimilated in the Variational Assimilation (VAR) System of NCMRWF Unified Model (NCUM). Numerical experiments are conducted in order to study the impact of the AIRS radiance in the NCUM analysis and forecast system. NCMRWF receives AIRS radiance from EUMETCAST through MOSDAC. AIRS is a grating spectrometer having 2378 channels covering the thermal infrared spectrum between 3 and 15 μm. Out of 2378 channels, 324 channels are selected for assimilation according to the peaking of weighting function and meteorological importance. According to the surface type and day-night conditions, some of the channels are not assimilated in the VAR. Observation Simulation Experiments (OSEs) are conducted for a period of 15 days to see the impact of AIRS radiances in NCUM. Statistical parameters like bias and RMSE are calculated to see the real impact of AIRS radiances in the assimilation system. Assimilation of AIRS in the NCUM system reduced the bias and RMSE in the radiances from instruments onboard other satellites. The impact of AIRS is clearly seen in the hyperspectral radiances like IASI and CrIS and also in infrared (HIRS) and microwave (AMSU, ATMS, etc.) sensors.

  8. Ethnicity, assimilation and harassment in the labor market

    OpenAIRE

    Epstein, Gil S.; Gang, Ira N.

    2008-01-01

    We often observe minority ethnic groups at a disadvantage relative to the majority. Why is this and what can be done about it? Efforts made to assimilate, and time, are two elements working to bring the minority into line with the majority. A third element, the degree to which the majority welcomes the minority, also plays a role. We develop a simple theoretical model useful for examining the consequences for assimilation and harassment of growth in the minority population, time, and the role...

  9. Assimilation of Wave Imaging Radar Observations for Real-time Wave-by-Wave Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Simpson, Alexandra [Oregon State Univ., Corvallis, OR (United States); Haller, Merrick [Oregon State Univ., Corvallis, OR (United States). School of Civil & Construction Engineering; Walker, David [SRI International, Menlo Park, CA (United States); Lynett, Pat [Univ. of Southern California, Los Angeles, CA (United States)

    2017-08-29

    This project addressed Topic 3: “Wave Measurement Instrumentation for Feed Forward Controls” under the FOA number DE-FOA-0000971. The overall goal of the program was to develop a phase-resolving wave forecasting technique for application to the active control of Wave Energy Conversion (WEC) devices. We have developed an approach that couples a wave imaging marine radar with a phase-resolving linear wave model for real-time wave field reconstruction and forward propagation of the wave field in space and time. The scope of the project was to develop and assess the performance of this novel forecasting system. Specific project goals were as follows: Develop and verify a fast, GPU-based (Graphical Processing Unit) wave propagation model suitable for phase-resolved computation of nearshore wave transformation over variable bathymetry; Compare the accuracy and speed of performance of the wave model against a deep water model in their ability to predict wave field transformation in the intermediate water depths (50 to 70 m) typical of planned WEC sites; Develop and implement a variational assimilation algorithm that can ingest wave imaging radar observations and estimate the time-varying wave conditions offshore of the domain of interest such that the observed wave field is best reconstructed throughout the domain and then use this to produce model forecasts for a given WEC location; Collect wave-resolving marine radar data, along with relevant in situ wave data, at a suitable wave energy test site, apply the algorithm to the field data, assess performance, and identify any necessary improvements; and Develop a production cost estimate that addresses the affordability of the wave forecasting technology and include in the Final Report. The developed forecasting algorithm (“Wavecast”) was evaluated for both speed and accuracy against a substantial synthetic dataset. Early in the project, performance tests definitively demonstrated that the system was capable of

  10. Overview of NASA's Observations for Global Air Quality

    Science.gov (United States)

    Kaye, J. A.

    2015-12-01

    Observations of pollutants are central to the study of air quality. Much focus has been placed on local-scale observations that can help specific geographic areas document their air quality issues, plan abatement strategies, and understand potential impacts. In addition, long-range atmospheric transport of pollutants can cause downwind regions to not meet attainment standards. Satellite observations have shed significant light on air quality from local to regional to global scales, especially for pollutants such as ozone, aerosols, carbon monoxide, sulfur dioxide, and nitrogen dioxide. These observations have made use of multiple techniques and in some cases multiple satellite sensors. The satellite observations are complemented by surface observations, as well as atmospheric (in situ) observations typically made as part of focused airborne field campaigns. The synergy between satellite observations and field campaigns has been an important theme for recent and upcoming activities and plans. In this talk, a review of NASA's investments in observations relevant to global air quality will be presented, with examples given for a range of pollutants and measurement approaches covering the last twenty-five years. These investments have helped build national and international collaborations such that the global satellite community is now preparing to deploy a constellation of satellites that together will provide fundamental advances in global observations for air quality.

  11. Evaluation of Assimilated SMOS Soil Moisture Data for US Cropland Soil Moisture Monitoring

    Science.gov (United States)

    Yang, Zhengwei; Sherstha, Ranjay; Crow, Wade; Bolten, John; Mladenova, Iva; Yu, Genong; Di, Liping

    2016-01-01

    Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS)Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA)National Agricultural Statistics Service (NASS) survey based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASSs survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.

  12. Description and verification of a U.S. Naval Research Lab's loosely coupled data assimilation system for the Navy's Earth System Model

    Science.gov (United States)

    Barton, N. P.; Metzger, E. J.; Smedstad, O. M.; Ruston, B. C.; Wallcraft, A. J.; Whitcomb, T.; Ridout, J. A.; Zamudio, L.; Posey, P.; Reynolds, C. A.; Richman, J. G.; Phelps, M.

    2017-12-01

    The Naval Research Laboratory is developing an Earth System Model (NESM) to provide global environmental information to meet Navy and Department of Defense (DoD) operations and planning needs from the upper atmosphere to under the sea. This system consists of a global atmosphere, ocean, ice, wave, and land prediction models and the individual models include: atmosphere - NAVy Global Environmental Model (NAVGEM); ocean - HYbrid Coordinate Ocean Model (HYCOM); sea ice - Community Ice CodE (CICE); WAVEWATCH III™; and land - NAVGEM Land Surface Model (LSM). Data assimilation is currently loosely coupled between the atmosphere component using a 6-hour update cycle in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR) and the ocean/ice components using a 24-hour update cycle in the Navy Coupled Ocean Data Assimilation (NCODA) with 3 hours of incremental updating. This presentation will describe the US Navy's coupled forecast model, the loosely coupled data assimilation, and compare results against stand-alone atmosphere and ocean/ice models. In particular, we will focus on the unique aspects of this modeling system, which includes an eddy resolving ocean model and challenges associated with different update-windows and solvers for the data assimilation in the atmosphere and ocean. Results will focus on typical operational diagnostics for atmosphere, ocean, and ice analyses including 500 hPa atmospheric height anomalies, low-level winds, temperature/salinity ocean depth profiles, ocean acoustical proxies, sea ice edge, and sea ice drift. Overall, the global coupled system is performing with comparable skill to the stand-alone systems.

  13. Snow multivariable data assimilation for hydrological predictions in mountain areas

    Science.gov (United States)

    Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

    2016-04-01

    The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground

  14. Assimilation of ASCAT near-surface soil moisture into the French SIM hydrological model

    Science.gov (United States)

    Draper, C.; Mahfouf, J.-F.; Calvet, J.-C.; Martin, E.; Wagner, W.

    2011-06-01

    The impact of assimilating near-surface soil moisture into the SAFRAN-ISBA-MODCOU (SIM) hydrological model over France is examined. Specifically, the root-zone soil moisture in the ISBA land surface model is constrained over three and a half years, by assimilating the ASCAT-derived surface degree of saturation product, using a Simplified Extended Kalman Filter. In this experiment ISBA is forced with the near-real time SAFRAN analysis, which analyses the variables required to force ISBA from relevant observations available before the real time data cut-off. The assimilation results are tested against ISBA forecasts generated with a higher quality delayed cut-off SAFRAN analysis. Ideally, assimilating the ASCAT data will constrain the ISBA surface state to correct for errors in the near-real time SAFRAN forcing, the most significant of which was a substantial dry bias caused by a dry precipitation bias. The assimilation successfully reduced the mean root-zone soil moisture bias, relative to the delayed cut-off forecasts, by close to 50 % of the open-loop value. The improved soil moisture in the model then led to significant improvements in the forecast hydrological cycle, reducing the drainage, runoff, and evapotranspiration biases (by 17 %, 11 %, and 70 %, respectively). When coupled to the MODCOU hydrogeological model, the ASCAT assimilation also led to improved streamflow forecasts, increasing the mean discharge ratio, relative to the delayed cut off forecasts, from 0.68 to 0.76. These results demonstrate that assimilating near-surface soil moisture observations can effectively constrain the SIM model hydrology, while also confirming the accuracy of the ASCAT surface degree of saturation product. This latter point highlights how assimilation experiments can contribute towards the difficult issue of validating remotely sensed land surface observations over large spatial scales.

  15. An OSSE Study for Deep Argo Array using the GFDL Ensemble Coupled Data Assimilation System

    Science.gov (United States)

    Chang, You-Soon; Zhang, Shaoqing; Rosati, Anthony; Vecchi, Gabriel A.; Yang, Xiaosong

    2018-03-01

    An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, "observations" drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the "identical" twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.

  16. Ten years of multiple data stream assimilation with the ORCHIDEE land surface model to improve regional to global simulated carbon budgets: synthesis and perspectives on directions for the future

    Science.gov (United States)

    Peylin, P. P.; Bacour, C.; MacBean, N.; Maignan, F.; Bastrikov, V.; Chevallier, F.

    2017-12-01

    Predicting the fate of carbon stocks and their sensitivity to climate change and land use/management strongly relies on our ability to accurately model net and gross carbon fluxes. However, simulated carbon and water fluxes remain subject to large uncertainties, partly because of unknown or poorly calibrated parameters. Over the past ten years, the carbon cycle data assimilation system at the Laboratoire des Sciences du Climat et de l'Environnement has investigated the benefit of assimilating multiple carbon cycle data streams into the ORCHIDEE LSM, the land surface component of the Institut Pierre Simon Laplace Earth System Model. These datasets have included FLUXNET eddy covariance data (net CO2 flux and latent heat flux) to constrain hourly to seasonal time-scale carbon cycle processes, remote sensing of the vegetation activity (MODIS NDVI) to constrain the leaf phenology, biomass data to constrain "slow" (yearly to decadal) processes of carbon allocation, and atmospheric CO2 concentrations to provide overall large scale constraints on the land carbon sink. Furthermore, we have investigated technical issues related to multiple data stream assimilation and choice of optimization algorithm. This has provided a wide-ranging perspective on the challenges we face in constraining model parameters and thus better quantifying, and reducing, model uncertainty in projections of the future global carbon sink. We review our past studies in terms of the impact of the optimization on key characteristics of the carbon cycle, e.g. the partition of the northern latitudes vs tropical land carbon sink, and compare to the classic atmospheric flux inversion approach. Throughout, we discuss our work in context of the abovementioned challenges, and propose solutions for the community going forward, including the potential of new observations such as atmospheric COS concentrations and satellite-derived Solar Induced Fluorescence to constrain the gross carbon fluxes of the ORCHIDEE

  17. Experiments with data assimilation in comprehensive air quality models: Impacts on model predictions and observation requirements (Invited)

    Science.gov (United States)

    Mathur, R.

    2009-12-01

    Emerging regional scale atmospheric simulation models must address the increasing complexity arising from new model applications that treat multi-pollutant interactions. Sophisticated air quality modeling systems are needed to develop effective abatement strategies that focus on simultaneously controlling multiple criteria pollutants as well as use in providing short term air quality forecasts. In recent years the applications of such models is continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physical and chemical atmospheric processes occurring at these disparate spatial and temporal scales requires the use of observation data beyond traditional in-situ networks so that the model simulations can be reasonably constrained. Preliminary applications of assimilation of remote sensing and aloft observations within a comprehensive regional scale atmospheric chemistry-transport modeling system will be presented: (1) A methodology is developed to assimilate MODIS aerosol optical depths in the model to represent the impacts long-range transport associated with the summer 2004 Alaskan fires on surface-level regional fine particulate matter (PM2.5) concentrations across the Eastern U.S. The episodic impact of this pollution transport event on PM2.5 concentrations over the eastern U.S. during mid-July 2004, is quantified through the complementary use of the model with remotely-sensed, aloft, and surface measurements; (2) Simple nudging experiments with limited aloft measurements are performed to identify uncertainties in model representations of physical processes and assess the potential use of such measurements in improving the predictive capability of atmospheric chemistry-transport models. The results from these early applications will be discussed in context of uncertainties in the model and in the remote sensing

  18. Assimilation of SMOS Retrievals in the Land Information System

    Science.gov (United States)

    Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.; Crosson, William L.

    2016-01-01

    The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm(sub 3 cm(sub -3). These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve.

  19. The Use of the Data Assimilation Research Testbed for Initializing and Evaluating IPCC Decadal Forecasts

    Science.gov (United States)

    Raeder, K.; Anderson, J. L.; Lauritzen, P. H.; Hoar, T. J.; Collins, N.

    2010-12-01

    DART (www.image.ucar.edu/DAReS/DART) is a general purpose, freely available, ensemble Kalman filter, data assimilation system, which is being used to generate state-of-the-art, partially coupled, ocean-atmosphere re-analyses in support of the decadal predictions planned for the next IPCC report. The resulting gridded product is directly comparable to the state variables output by POP and CAM (oceanic and atmospheric components of NCAR's Community Earth System Model climate model) because those are the assimilating models. Other models could also benefit from comparison against these reanalyses, since the ocean analyses are at the leading edge of ocean state estimation, and the atmospheric analyses are competitive with operational centers'. Such comparisons can reveal model biases and predictability characteristics, and do so in a quantitative way, since the ensemble nature of the analyses provides an objective estimate of the analysis error. The analyses will also be used as initial conditions for the decadal forecasts because they are the most realistic available. The generation of such analyses has revealed errors in model formulation for several versions of the finite volume core CAM, which has led to model improvements in each case. New models can be incorporated into DART in a matter of weeks, allowing them to be compared directly against available observations. The observations currently used in the assimilations include, for the ocean; temperature and salinity from the World Ocean Database (floats, drifters, moorings, autonomous pinipeds, and others), and for the atmosphere; temperature and winds from radiosondes, satellite drift winds, ACARS and aircraft. Observations of ocean currents and atmospheric moisture and pressure are also available. Global Positioning System profiles of atmospheric temperature and moisture are available for recent years. All that is required to add new observations to the suite is the forward operator, which generates an estimate

  20. Assimilation of total lightning data using the three-dimensional variational method at convection-allowing resolution

    Science.gov (United States)

    Zhang, Rong; Zhang, Yijun; Xu, Liangtao; Zheng, Dong; Yao, Wen

    2017-08-01

    A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small-scale information can be incorporated to improve the quality of the initial condition and the subsequent forecasts. In this study, the empirical relationship between flash rate, water vapor mixing ratio, and graupel mixing ratio was used to adjust the model relative humidity, which was then assimilated by using the three-dimensional variational data assimilation system of the Weather Research and Forecasting model in cycling mode at 10-min intervals. To find the appropriate assimilation time-window length that yielded significant improvement in both the initial conditions and subsequent forecasts, four experiments with different assimilation time-window lengths were conducted for a squall line case that occurred on 10 July 2007 in North China. It was found that 60 min was the appropriate assimilation time-window length for this case, and longer assimilation window length was unnecessary since no further improvement was present. Forecasts of 1-h accumulated precipitation during the assimilation period and the subsequent 3-h accumulated precipitation were significantly improved compared with the control experiment without lightning data assimilation. The simulated reflectivity was optimal after 30 min of the forecast, it remained optimal during the following 42 min, and the positive effect from lightning data assimilation began to diminish after 72 min of the forecast. Overall, the improvement from lightning data assimilation can be maintained for about 3 h.

  1. Reply [to: Atlantic Tropical Cyclogenetic Processes during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System

    Science.gov (United States)

    Reale, Oreste; Lau, William K.

    2010-01-01

    This article is a Reply to a Comment by Scott Braun on a previously published article by O. Reale, K.-M. Lau, and E. Brin: "Atlantic tropical cyclogenetic processes during SOP-3 NAMMA in the GEOS-5 global data assimilation and forecast system", by Reale, Lau and Brin, hereafter referred to as RA09. RA09 investigated the role of the Saharan Air Layer (SAL) in tropical cyclogenetic processes associated with a non-developing easterly wave observed during the Special Observation Period (SOP-3) phase of the 2006 NASA African Monsoon Multidisciplinary Analyses (MAMMA). The wave was chosen because both interact heavily with Saharan air. Results showed: a) very steep moisture gradients are associated with the SAL in forecasts and analyses even at great distance from the Sahara; b) a thermal dipole (warm above, cool below) in the non-developing case. RA09A suggested that radiative effect of dust may play some role in producing a thermal structure less favorable to cyclogenesis, and also indicated that only global horizontal resolutions on the order of 20-30 kilometers can capture the large-scale transport and the fine thermal structure of the SAL Braun (2010) questions those results attributing the wave dissipation to midlatitude air. The core discussion is on a dry filament preceding the wave, on the presence of dust, and on the origin of the air contained in this dry filament. In the 'Reply', higher resolution analyses than the ones used by Braun, taken at almost coincident times with Aqua and Terra passes, are shown, to emphasize how the channel of dry air associated with W1 is indeed rich in dust. Backtrajectories on a higher resolution grid are also performed, leading to results drastically different from Braun (2010), and in particularly showing that there is a clear contribution of Saharan air. Finally, the 'Reply' presents evidence on that analyses at a horizontal resolution of one degree are inadequate to investigate such feature.

  2. Reconstruction of Historical Weather by Assimilating Old Weather Diary Data

    Science.gov (United States)

    Neluwala, P.; Yoshimura, K.; Toride, K.; Hirano, J.; Ichino, M.; Okazaki, A.

    2017-12-01

    Climate can control not only human life style but also other living beings. It is important to investigate historical climate to understand the current and future climates. Information about daily weather can give a better understanding of past life on earth. Long-term weather influences crop calendar as well as the development of civilizations. Unfortunately, existing reconstructed daily weather data are limited to 1850s due to the availability of instrumental data. The climate data prior to that are derived from proxy materials (e.g., tree-ring width, ice core isotopes, etc.) which are either in annual or decadal scale. However, there are many historical documents which contain information about weather such as personal diaries. In Japan, around 20 diaries in average during the 16th - 19th centuries have been collected and converted into a digitized form. As such, diary data exist in many other countries. This study aims to reconstruct historical daily weather during the 18th and 19th centuries using personal daily diaries which have analogue weather descriptions such as `cloudy' or `sunny'. A recent study has shown the possibility of assimilating coarse weather data using idealized experiments. We further extend this study by assimilating modern weather descriptions similar to diary data in recent periods. The Global Spectral model (GSM) of National Centers for Environmental Prediction (NCEP) is used to reconstruct weather with the Local Ensemble Kalman filter (LETKF). Descriptive data are first converted to model variables such as total cloud cover (TCC), solar radiation and precipitation using empirical relationships. Those variables are then assimilated on a daily basis after adding random errors to consider the uncertainty of actual diary data. The assimilation of downward short wave solar radiation using weather descriptions improves RMSE from 64.3 w/m2 to 33.0 w/m2 and correlation coefficient (R) from 0.5 to 0.8 compared with the case without any

  3. Data Assimilation by delay-coordinate nudging

    Science.gov (United States)

    Pazo, Diego; Lopez, Juan Manuel; Carrassi, Alberto

    2016-04-01

    A new nudging method for data assimilation, delay-coordinate nudging, is presented. Delay-coordinate nudging makes explicit use of present and past observations in the formulation of the forcing driving the model evolution at each time-step. Numerical experiments with a low order chaotic system show that the new method systematically outperforms standard nudging in different model and observational scenarios, also when using an un-optimized formulation of the delay-nudging coefficients. A connection between the optimal delay and the dominant Lyapunov exponent of the dynamics is found based on heuristic arguments and is confirmed by the numerical results, providing a guideline for the practical implementation of the algorithm. Delay-coordinate nudging preserves the easiness of implementation, the intuitive functioning and the reduced computational cost of the standard nudging, making it a potential alternative especially in the field of seasonal-to-decadal predictions with large Earth system models that limit the use of more sophisticated data assimilation procedures.

  4. Data assimilation strategies for volcano geodesy

    Science.gov (United States)

    Zhan, Yan; Gregg, Patricia M.

    2017-09-01

    Ground deformation observed using near-real time geodetic methods, such as InSAR and GPS, can provide critical information about the evolution of a magma chamber prior to volcanic eruption. Rapid advancement in numerical modeling capabilities has resulted in a number of finite element models targeted at better understanding the connection between surface uplift associated with magma chamber pressurization and the potential for volcanic eruption. Robust model-data fusion techniques are necessary to take full advantage of the numerical models and the volcano monitoring observations currently available. In this study, we develop a 3D data assimilation framework using the Ensemble Kalman Filter (EnKF) approach in order to combine geodetic observations of surface deformation with geodynamic models to investigate volcanic unrest. The EnKF sequential assimilation method utilizes disparate data sets as they become available to update geodynamic models of magma reservoir evolution. While the EnKF has been widely applied in hydrologic and climate modeling, the adaptation for volcano monitoring is in its initial stages. As such, our investigation focuses on conducting a series of sensitivity tests to optimize the EnKF for volcano applications and on developing specific strategies for assimilation of geodetic data. Our numerical experiments illustrate that the EnKF is able to adapt well to the spatial limitations posed by GPS data and the temporal limitations of InSAR, and that specific strategies can be adopted to enhance EnKF performance to improve model forecasts. Specifically, our numerical experiments indicate that: (1) incorporating additional iterations of the EnKF analysis step is more efficient than increasing the number of ensemble members; (2) the accuracy of the EnKF results are not affected by initial parameter assumptions; (3) GPS observations near the center of uplift improve the quality of model forecasts; (4) occasionally shifting continuous GPS stations to

  5. NOAA HRD's HEDAS Data Assimilation System's performance for the 2010 Atlantic Hurricane Season

    Science.gov (United States)

    Sellwood, K.; Aksoy, A.; Vukicevic, T.; Lorsolo, S.

    2010-12-01

    The Hurricane Ensemble Data Assimilation System (HEDAS) was developed at the Hurricane Research Division (HRD) of NOAA, in conjunction with an experimental version of the Hurricane Weather and Research Forecast model (HWRFx), in an effort to improve the initial representation of the hurricane vortex by utilizing high resolution in-situ data collected during NOAA’s Hurricane Field Program. HEDAS implements the “ensemble square root “ filter of Whitaker and Hamill (2002) using a 30 member ensemble obtained from NOAA/ESRL’s ensemble Kalman filter (EnKF) system and the assimilation is performed on a 3-km nest centered on the hurricane vortex. As part of NOAA’s Hurricane Forecast Improvement Program (HFIP), HEDAS will be run in a semi-operational mode for the first time during the 2010 Atlantic hurricane season and will assimilate airborne Doppler radar winds, dropwindsonde and flight level wind, temperature, pressure and relative humidity, and Stepped Frequency Microwave Radiometer surface wind observations as they become available. HEDAS has been implemented in an experimental mode for the cases of Hurricane Bill, 2009 and Paloma, 2008 to confirm functionality and determine the optimal configuration of the system. This test case demonstrates the importance of assimilating thermodynamic data in addition to wind observations and the benefit of increasing the quantity and distribution of observations. Applying HEDAS to a larger sample of storm forecasts would provide further insight into the behavior of the model when inner core aircraft observations are assimilated. The main focus of this talk will be to present a summary of HEDAS performance in the HWRFx model for the inaugural season. The HEDAS analyses and the resulting HWRFx forecasts will be compared with HWRFx analyses and forecasts produced concurrently using the HRD modeling group’s vortex initialization which does not employ data assimilation. The initial vortex and subsequent forecasts will be

  6. Assimilate partitioning during reproductive growth

    International Nuclear Information System (INIS)

    Finazzo, S.F.; Davenport, T.L.

    1987-01-01

    Leaves having various phyllotactic relationships to fruitlets were labeled for 1 hour with 10/sub r/Ci of 14 CO 2 . Fruitlets were also labeled. Fruitlets did fix 14 CO 2 . Translocation of radioactivity from the peel into the fruit occurred slowly and to a limited extent. No evidence of translocation out of the fruitlets was observed. Assimilate partitioning in avocado was strongly influenced by phyllotaxy. If a fruit and the labeled leaf had the same phyllotaxy then greater than 95% of the radiolabel was present in this fruit. When the fruit did not have the same phyllotaxy as the labeled leaf, the radiolabel distribution was skewed with 70% of the label going to a single adjacent position. Avocado fruitlets exhibit uniform labeling throughout a particular tissue. In avocado, assimilates preferentially move from leaves to fruits with the same phyllotaxy

  7. Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System

    Directory of Open Access Journals (Sweden)

    Thamiris B. Campos

    2018-03-01

    Full Text Available Due to the high spatial and temporal variability of atmospheric water vapor associated with the deficient methodologies used in its quantification and the imperfect physics parameterizations incorporated in the models, there are significant uncertainties in characterizing the moisture field. The process responsible for incorporating the information provided by observation into the numerical weather prediction is denominated data assimilation. The best result in atmospheric moisture depend on the correct choice of the moisture control variable. Normalized relative humidity and pseudo-relative humidity are the variables usually used by the main weather prediction centers. The objective of this study is to assess the sensibility of the Center for Weather Forecast and Climate Studies to choose moisture control variable in the data assimilation scheme. Experiments using these variables are carried out. The results show that the pseudo-relative humidity improves the variables that depend on temperature values but damage the moisture field. The opposite results show when the simulation used the normalized relative humidity. These experiments suggest that the pseudo-relative humidity should be used in the cyclical process of data assimilation and the normalized relative humidity should be used in non-cyclic process (e.g., nowcasting application in high resolution.

  8. Assimilation of Ocean-Color Plankton Functional Types to Improve Marine Ecosystem Simulations

    Science.gov (United States)

    Ciavatta, S.; Brewin, R. J. W.; Skákala, J.; Polimene, L.; de Mora, L.; Artioli, Y.; Allen, J. I.

    2018-02-01

    We assimilated phytoplankton functional types (PFTs) derived from ocean color into a marine ecosystem model, to improve the simulation of biogeochemical indicators and emerging properties in a shelf sea. Error-characterized chlorophyll concentrations of four PFTs (diatoms, dinoflagellates, nanoplankton, and picoplankton), as well as total chlorophyll for comparison, were assimilated into a physical-biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The reanalysis simulations spanned the years 1998-2003. The skill of the reference and reanalysis simulations in estimating ocean color and in situ biogeochemical data were compared by using robust statistics. The reanalysis outperformed both the reference and the assimilation of total chlorophyll in estimating the ocean-color PFTs (except nanoplankton), as well as the not-assimilated total chlorophyll, leading the model to simulate better the plankton community structure. Crucially, the reanalysis improved the estimates of not-assimilated in situ data of PFTs, as well as of phosphate and pCO2, impacting the simulation of the air-sea carbon flux. However, the reanalysis increased further the model overestimation of nitrate, in spite of increases in plankton nitrate uptake. The method proposed here is easily adaptable for use with other ecosystem models that simulate PFTs, for, e.g., reanalysis of carbon fluxes in the global ocean and for operational forecasts of biogeochemical indicators in shelf-sea ecosystems.

  9. Evaluation of global monitoring and forecasting systems at Mercator Océan

    Directory of Open Access Journals (Sweden)

    J.-M. Lellouche

    2013-01-01

    Full Text Available Since December 2010, the MyOcean global analysis and forecasting system has consisted of the Mercator Océan NEMO global 1/4° configuration with a 1/12° nested model over the Atlantic and the Mediterranean. The open boundary data for the nested configuration come from the global 1/4° configuration at 20° S and 80° N.

    The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3-D-Var scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite sea surface temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model.

    This paper describes the recent systems used by Mercator Océan and the validation procedure applied to current MyOcean systems as well as systems under development. The paper shows how refinements or adjustments to the system during the validation procedure affect its quality. Additionally, we show that quality checks (in situ, drifters and data sources (satellite sea surface temperature have as great an impact as the system design (model physics and assimilation parameters. The results of the scientific assessment are illustrated with diagnostics over the year 2010 mainly, assorted with time series over the 2007–2011 period. The validation procedure demonstrates the accuracy of MyOcean global products, whose quality is stable over time. All monitoring systems are close to altimetric observations with a forecast RMS difference of 7 cm. The update of the mean

  10. Evaluation of the Tropical Pacific Observing System from the Data Assimilation Perspective

    Science.gov (United States)

    2014-01-01

    hereafter, SIDA systems) have the capacity to assimilate salinity profiles imposing a multivariate (mainly T-S) balance relationship (summarized in...Fujii et al., 2011). Current SIDA systems in operational centers generally use Ocean General Circulation Models (OGCM) with resolution typically 1...long-term (typically 20-30 years) ocean DA runs are often performed with SIDA systems in operational centers for validation and calibration of SI

  11. Performance and Quality Assessment of the Forthcoming Copernicus Marine Service Global Ocean Monitoring and Forecasting Real-Time System

    Science.gov (United States)

    Lellouche, J. M.; Le Galloudec, O.; Greiner, E.; Garric, G.; Regnier, C.; Drillet, Y.

    2016-02-01

    Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.Since May 2015, Mercator Ocean opened the Copernicus Marine Service (CMS) and is in charge of the global ocean analyses and forecast, at eddy resolving resolution. In this context, R&D activities have been conducted at Mercator Ocean these last years in order to improve the real-time 1/12° global system for the next CMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefit among others from the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting …This presentation doesn't focus on the impact of each update, but rather on the overall behavior of the system integrating all updates. This assessment reports on the products quality improvements, highlighting the level of performance and the reliability of the new system.

  12. Assimilation of zenith total delays in the AROME France convective scale model: a recent assessment

    Directory of Open Access Journals (Sweden)

    Jean-Francois Mahfouf

    2015-02-01

    Full Text Available The impact of assimilating GPS zenith total delays (ZTD in the convective scale model AROME is assessed over a 1-month period in summer 2013. The experimental set-up is similar to the current operational usage at Météo-France where the observing system has been expanded in July 2013 in a three-dimensional variational (3D-Var data assimilation scheme with a 3-hour cycling. Three experiments are performed. In a baseline experiment the GPS ZTD provided through the E-GVAP programme are withdrawn from the observing system (NOGPS. In a second experiment, GPS ZTD from E-GVAP are included in the observing system, representing the operational configuration at Météo-France (EGVAP. The last experiment is similar to EGVAP but new ZTD observations processed by the University of Luxembourg are also assimilated on top of all other observations (UL01. In the first stage, it has been verified through a systematic comparison with model counterparts that the quality of ZTD data processed by the University of Luxembourg is similar to the one provided by other analysis centres from the E-GVAP programme. After a number of quality controls, it has been possible to assimilate around 90 additional observations on top of around 600 stations from E-GVAP every 3 hours. Despite the small fraction of observations assimilated in AROME that ZTD represent (<2%, it is shown that they systematically improve the atmospheric humidity short-range forecasts by a comparison with other observing systems informative about water vapour (radiosoundings, satellite radiances, surface networks even though it is by small amounts. When examining objective precipitation scores over France, the improvement brought by the UL01 stations on top of E-GVAP is systematic for all daily precipitation thresholds. Examination of several case studies reveals the ability of the ZTD observations to modify the intensity and location of precipitating areas in accordance with previous studies. The addition

  13. Assimilation of Feng-Yun-3B satellite microwave humidity sounder data over land

    Science.gov (United States)

    Chen, Keyi; Bormann, Niels; English, Stephen; Zhu, Jiang

    2018-03-01

    The ECMWF has been assimilating Feng-Yun-3B (FY-3B) satellite microwave humidity sounder (MWHS) data over ocean in an operational forecasting system since 24 September 2014. It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel 1 [150 GHz (vertical polarization)] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS (Advanced Technology Microwave Sounder) humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.

  14. Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal adjustment scale

    Science.gov (United States)

    Lee, H.; Seo, D.-J.; Liu, Y.; Koren, V.; McKee, P.; Corby, R.

    2012-01-01

    State updating of distributed rainfall-runoff models via streamflow assimilation is subject to overfitting because large dimensionality of the state space of the model may render the assimilation problem seriously under-determined. To examine the issue in the context of operational hydrology, we carry out a set of real-world experiments in which streamflow data is assimilated into gridded Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic-wave routing models of the US National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) with the variational data assimilation technique. Study basins include four basins in Oklahoma and five basins in Texas. To assess the sensitivity of data assimilation performance to dimensionality reduction in the control vector, we used nine different spatiotemporal adjustment scales, where state variables are adjusted in a lumped, semi-distributed, or distributed fashion and biases in precipitation and potential evaporation (PE) are adjusted hourly, 6-hourly, or kept time-invariant. For each adjustment scale, three different streamflow assimilation scenarios are explored, where streamflow observations at basin interior points, at the basin outlet, or at both interior points and the outlet are assimilated. The streamflow assimilation experiments with nine different basins show that the optimum spatiotemporal adjustment scale varies from one basin to another and may be different for streamflow analysis and prediction in all of the three streamflow assimilation scenarios. The most preferred adjustment scale for seven out of nine basins is found to be the distributed, hourly scale, despite the fact that several independent validation results at this adjustment scale indicated the occurrence of overfitting. Basins with highly correlated interior and outlet flows tend to be less sensitive to the adjustment scale and could benefit more from streamflow assimilation. In comparison to outlet flow assimilation, interior flow

  15. Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

    Science.gov (United States)

    Draper, C.; Mahfouf, J.-F.; Calvet, J.-C.; Martin, E.; Wagner, W.

    2011-12-01

    This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

  16. Applications of Data Assimilation to Analysis of the Ocean on Large Scales

    Science.gov (United States)

    Miller, Robert N.; Busalacchi, Antonio J.; Hackert, Eric C.

    1997-01-01

    It is commonplace to begin talks on this topic by noting that oceanographic data are too scarce and sparse to provide complete initial and boundary conditions for large-scale ocean models. Even considering the availability of remotely-sensed data such as radar altimetry from the TOPEX and ERS-1 satellites, a glance at a map of available subsurface data should convince most observers that this is still the case. Data are still too sparse for comprehensive treatment of interannual to interdecadal climate change through the use of models, since the new data sets have not been around for very long. In view of the dearth of data, we must note that the overall picture is changing rapidly. Recently, there have been a number of large scale ocean analysis and prediction efforts, some of which now run on an operational or at least quasi-operational basis, most notably the model based analyses of the tropical oceans. These programs are modeled on numerical weather prediction. Aside from the success of the global tide models, assimilation of data in the tropics, in support of prediction and analysis of seasonal to interannual climate change, is probably the area of large scale ocean modeling and data assimilation in which the most progress has been made. Climate change is a problem which is particularly suited to advanced data assimilation methods. Linear models are useful, and the linear theory can be exploited. For the most part, the data are sufficiently sparse that implementation of advanced methods is worthwhile. As an example of a large scale data assimilation experiment with a recent extensive data set, we present results of a tropical ocean experiment in which the Kalman filter was used to assimilate three years of altimetric data from Geosat into a coarsely resolved linearized long wave shallow water model. Since nonlinear processes dominate the local dynamic signal outside the tropics, subsurface dynamical quantities cannot be reliably inferred from surface height

  17. Improving the extreme rainfall forecast of Typhoon Morakot (2009) by assimilating radar data from Taiwan Island and mainland China

    Science.gov (United States)

    Bao, Xuwei; Wu, Dan; Lei, Xiaotu; Ma, Leiming; Wang, Dongliang; Zhao, Kun; Jou, Ben Jong-Dao

    2017-08-01

    This study examined the impact of an improved initial field through assimilating ground-based radar data from mainland China and Taiwan Island to simulate the long-lasting and extreme rainfall caused by Morakot (2009). The vortex location and the subsequent track analyzed through the radial velocity data assimilation (VDA) are generally consistent with the best track. The initial humidity within the radar detecting region and Morakot's northward translation speed can be significantly improved by the radar reflectivity data assimilation (ZDA). As a result, the heavy rainfall on both sides of Taiwan Strait can be reproduced with the joint application of VDA and ZDA. Based on sensitivity experiments, it was found that, without ZDA, the simulated storm underwent an unrealistic inward contraction after 12-h integration, due to underestimation of humidity in the global reanalysis, leading to underestimation of rainfall amount and coverage. Without the vortex relocation via VDA, the moister (drier) initial field with (without) ZDA will produce a more southward (northward) track, so that the rainfall location on both sides of Taiwan Strait will be affected. It was further found that the improvement in the humidity field of Morakot is mainly due to assimilation of high-value reflectivity (strong convection) observed by the radars in Taiwan Island, especially at Kenting station. By analysis of parcel trajectories and calculation of water vapor flux divergence, it was also found that the improved typhoon circulation through assimilating radar data can draw more water vapor from the environment during the subsequent simulation, eventually contributing to the extreme rainfall on both sides of Taiwan Strait.

  18. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model

    Directory of Open Access Journals (Sweden)

    François Counillon

    2016-12-01

    Full Text Available We document a pilot stochastic re-analysis computed by assimilating sea surface temperature (SST anomalies into the ocean component of the coupled Norwegian Climate Prediction Model (NorCPM for the period 1950–2010 (doi: 10.11582/2016.00002. NorCPM is based on the Norwegian Earth System Model and uses the ensemble Kalman filter for data assimilation (DA. Here, we assimilate SST from the stochastic HadISST2 historical reconstruction. The accuracy, reliability and drift are investigated using both assimilated and independent observations. NorCPM is slightly overdispersive against assimilated observations but shows stable performance through the analysis period. It demonstrates skills against independent measurements: sea surface height, heat and salt content, in particular in the Equatorial and North Pacific, the North Atlantic Subpolar Gyre (SPG region and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the vertical temperature variability there, in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using a flow-dependent assimilation method and constructing the covariance in isopycnal coordinates are investigated in the SPG region. Isopycnal coordinates discretisation is found to better capture the vertical structure than standard depth-coordinate discretisation, because it leads to a deeper influence of the assimilated surface observations. The vertical covariance shows a pronounced seasonal and decadal variability that highlights the benefit of flow-dependent DA method. This study demonstrates the potential of NorCPM to compute an ocean re-analysis for the 19th and 20th centuries when SST observations are available.

  19. Assimilation of Polder aerosol optical thickness into LMD2-Inca model in order to study aerosol-climate interactions; Etude des interactions entre aerosols et climat: assimilation des observations spatiales de Polder dans LMDz-Inca

    Energy Technology Data Exchange (ETDEWEB)

    Generoso, S.

    2004-12-15

    Aerosols influence the Earth radiative budget both through their direct (scattering and absorption of solar radiation) and indirect (impacts on cloud microphysics) effects. The anthropogenic perturbation due to aerosol emissions is of the same order of magnitude than the one due to greenhouse gases, but less well known. To improve our knowledge, we need to better know aerosol spatial and temporal distributions. Indeed, aerosol modeling still suffers from large uncertainties in sources and transport, while satellite observations are incomplete (no detection in the presence of clouds, no information on the vertical distribution or on the chemical nature). Moreover, field campaigns are localized in space and time. This study aims to reduce uncertainties in aerosol distributions, developing assimilation of satellite data into a chemical transport model. The basic idea is to combine information obtained from spatial observation (optical thickness) and modeling studies (aerosol types, vertical distribution). In this study, we assimilate data from the POLDER space-borne instrument into the LMDz-INCA model. The results show the advantage of merging information from different sources. In many regions, the method reduces uncertainties on aerosol distribution (reduction of RMS error). An application of the method to the study of aerosol impact on cloud microphysics is shown. (author)

  20. Continuous data assimilation for downscaling large-footprint soil moisture retrievals

    KAUST Repository

    Altaf, Muhammad

    2016-01-01

    Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model\\'s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

  1. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

    Science.gov (United States)

    Quinn Thomas, R.; Brooks, Evan B.; Jersild, Annika L.; Ward, Eric J.; Wynne, Randolph H.; Albaugh, Timothy J.; Dinon-Aldridge, Heather; Burkhart, Harold E.; Domec, Jean-Christophe; Fox, Thomas R.; Gonzalez-Benecke, Carlos A.; Martin, Timothy A.; Noormets, Asko; Sampson, David A.; Teskey, Robert O.

    2017-07-01

    Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present

  2. Assessing the Global Extent of Rivers Observable by SWOT

    Science.gov (United States)

    Pavelsky, T.; Durand, M. T.; Andreadis, K.; Beighley, E.; Allen, G. H.; Miller, Z.

    2013-12-01

    Flow of water through rivers is among the key fluxes in the global hydrologic cycle and its knowledge would advance the understanding of flood hazards, water resources management, ecology, and climate. However, gauges providing publicly accessible measurements of river stage or discharge remain sparse in many regions. The Surface Water and Ocean Topography (SWOT) satellite mission is a joint project of NASA and the French Centre National d'Etudes Spatiales (CNES) that would provide the first high-resolution images of simultaneous terrestrial water surface height, inundation extent, and ocean surface elevation. Among SWOT's primary goals is the direct observation of variations in river water surface elevation and, where possible, estimation of river discharge from SWOT measurements. The mission science requirements specify that rivers wider than 100 m would be observed globally, with a goal of observing rivers wider than 50m. However, the extent of anticipated SWOT river observations remains fundamentally unknown because no high-resolution, global dataset of river widths exists. Here, we estimate the global extent of rivers wider than 50 m-100 m thresholds using established relationships among river width, discharge, and drainage area. We combine a global digital elevation model with in situ river discharge data to estimate the global extent of SWOT-observable rivers, and validate these estimates against satellite-derived measurements of river width in two large river basins (the Yukon and the Ohio). We then compare the extent of SWOT-observed rivers with the current publicly-available, global gauge network included in the Global Runoff Data Centre (GRDC) database to examine the impact of SWOT on the availability of river observation over continental and global scales. Results suggest that if SWOT observes 100 m wide rivers, river basins with areas greater than 50,000 km2 will commonly be measured. If SWOT could observe 50 m wide rivers, then most 10,000 km2 basins

  3. Discharge data assimilation in a distributed hydrologic model for flood forecasting purposes

    Science.gov (United States)

    Ercolani, G.; Castelli, F.

    2017-12-01

    Flood early warning systems benefit from accurate river flow forecasts, and data assimilation may improve their reliability. However, the actual enhancement that can be obtained in the operational practice should be investigated in detail and quantified. In this work we assess the benefits that the simultaneous assimilation of discharge observations at multiple locations can bring to flow forecasting through a distributed hydrologic model. The distributed model, MOBIDIC, is part of the operational flood forecasting chain of Tuscany Region in Central Italy. The assimilation system adopts a mixed variational-Monte Carlo approach to update efficiently initial river flow, soil moisture, and a parameter related to runoff production. The evaluation of the system is based on numerous hindcast experiments of real events. The events are characterized by significant rainfall that resulted in both high and relatively low flow in the river network. The area of study is the main basin of Tuscany Region, i.e. Arno river basin, which extends over about 8300 km2 and whose mean annual precipitation is around 800 mm. Arno's mainstream, with its nearly 240 km length, passes through major Tuscan cities, as Florence and Pisa, that are vulnerable to floods (e.g. flood of November 1966). The assimilation tests follow the usage of the model in the forecasting chain, employing the operational resolution in both space and time (500 m and 15 minutes respectively) and releasing new flow forecasts every 6 hours. The assimilation strategy is evaluated in respect to open loop simulations, i.e. runs that do not exploit discharge observations through data assimilation. We compare hydrographs in their entirety, as well as classical performance indexes, as error on peak flow and Nash-Sutcliffe efficiency. The dependence of performances on lead time and location is assessed. Results indicate that the operational forecasting chain can benefit from the developed assimilation system, although with a

  4. Benchmarking the mesoscale variability in global ocean eddy-permitting numerical systems

    Science.gov (United States)

    Cipollone, Andrea; Masina, Simona; Storto, Andrea; Iovino, Doroteaciro

    2017-10-01

    The role of data assimilation procedures on representing ocean mesoscale variability is assessed by applying eddy statistics to a state-of-the-art global ocean reanalysis (C-GLORS), a free global ocean simulation (performed with the NEMO system) and an observation-based dataset (ARMOR3D) used as an independent benchmark. Numerical results are computed on a 1/4 ∘ horizontal grid (ORCA025) and share the same resolution with ARMOR3D dataset. This "eddy-permitting" resolution is sufficient to allow ocean eddies to form. Further to assessing the eddy statistics from three different datasets, a global three-dimensional eddy detection system is implemented in order to bypass the need of regional-dependent definition of thresholds, typical of commonly adopted eddy detection algorithms. It thus provides full three-dimensional eddy statistics segmenting vertical profiles from local rotational velocities. This criterion is crucial for discerning real eddies from transient surface noise that inevitably affects any two-dimensional algorithm. Data assimilation enhances and corrects mesoscale variability on a wide range of features that cannot be well reproduced otherwise. The free simulation fairly reproduces eddies emerging from western boundary currents and deep baroclinic instabilities, while underestimates shallower vortexes that populate the full basin. The ocean reanalysis recovers most of the missing turbulence, shown by satellite products , that is not generated by the model itself and consistently projects surface variability deep into the water column. The comparison with the statistically reconstructed vertical profiles from ARMOR3D show that ocean data assimilation is able to embed variability into the model dynamics, constraining eddies with in situ and altimetry observation and generating them consistently with local environment.

  5. Storm surge model based on variational data assimilation method

    Directory of Open Access Journals (Sweden)

    Shi-li Huang

    2010-06-01

    Full Text Available By combining computation and observation information, the variational data assimilation method has the ability to eliminate errors caused by the uncertainty of parameters in practical forecasting. It was applied to a storm surge model based on unstructured grids with high spatial resolution meant for improving the forecasting accuracy of the storm surge. By controlling the wind stress drag coefficient, the variation-based model was developed and validated through data assimilation tests in an actual storm surge induced by a typhoon. In the data assimilation tests, the model accurately identified the wind stress drag coefficient and obtained results close to the true state. Then, the actual storm surge induced by Typhoon 0515 was forecast by the developed model, and the results demonstrate its efficiency in practical application.

  6. Data assimilation and model evaluation experiment datasets

    Science.gov (United States)

    Lai, Chung-Cheng A.; Qian, Wen; Glenn, Scott M.

    1994-01-01

    The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

  7. Variational data assimilative modeling of the Gulf of Maine in spring and summer 2010

    Science.gov (United States)

    Li, Yizhen; He, Ruoying; Chen, Ke; McGillicuddy, Dennis J.

    2015-05-01

    A data assimilative ocean circulation model is used to hindcast the Gulf of Maine [GOM) circulation in spring and summer 2010. Using the recently developed incremental strong constraint 4D Variational data assimilation algorithm, the model assimilates satellite sea surface temperature and in situ temperature and salinity profiles measured by expendable bathythermograph, Argo floats, and shipboard CTD casts. Validation against independent observations shows that the model skill is significantly improved after data assimilation. The data-assimilative model hindcast reproduces the temporal and spatial evolution of the ocean state, showing that a sea level depression southwest of the Scotian Shelf played a critical role in shaping the gulf-wide circulation. Heat budget analysis further demonstrates that both advection and surface heat flux contribute to temperature variability. The estimated time scale for coastal water to travel from the Scotian Shelf to the Jordan Basin is around 60 days, which is consistent with previous estimates based on in situ observations. Our study highlights the importance of resolving upstream and offshore forcing conditions in predicting the coastal circulation in the GOM.

  8. Assimilating satellite soil moisture into rainfall-runoff modelling: towards a systematic study

    Science.gov (United States)

    Massari, Christian; Tarpanelli, Angelica; Brocca, Luca; Moramarco, Tommaso

    2015-04-01

    Soil moisture is the main factor for the repartition of the mass and energy fluxes between the land surface and the atmosphere thus playing a fundamental role in the hydrological cycle. Indeed, soil moisture represents the initial condition of rainfall-runoff modelling that determines the flood response of a catchment. Different initial soil moisture conditions can discriminate between catastrophic and minor effects of a given rainfall event. Therefore, improving the estimation of initial soil moisture conditions will reduce uncertainties in early warning flood forecasting models addressing the mitigation of flood hazard. In recent years, satellite soil moisture products have become available with fine spatial-temporal resolution and a good accuracy. Therefore, a number of studies have been published in which the impact of the assimilation of satellite soil moisture data into rainfall-runoff modelling is investigated. Unfortunately, data assimilation involves a series of assumptions and choices that significantly affect the final result. Given a satellite soil moisture observation, a rainfall-runoff model and a data assimilation technique, an improvement or a deterioration of discharge predictions can be obtained depending on the choices made in the data assimilation procedure. Consequently, large discrepancies have been obtained in the studies published so far likely due to the differences in the implementation of the data assimilation technique. On this basis, a comprehensive and robust procedure for the assimilation of satellite soil moisture data into rainfall-runoff modelling is developed here and applied to six subcatchment of the Upper Tiber River Basin for which high-quality hydrometeorological hourly observations are available in the period 1989-2013. The satellite soil moisture product used in this study is obtained from the Advanced SCATterometer (ASCAT) onboard Metop-A satellite and it is available since 2007. The MISDc ("Modello Idrologico Semi

  9. Radiance Assimilation Shows Promise for Snowpack Characterization: A 1-D Case Study

    Science.gov (United States)

    Durand, Michael; Kim, Edward; Margulis, Steve

    2008-01-01

    We demonstrate an ensemble-based radiometric data assimilation (DA) methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) observations at 18.7 and 36.5 GHz collected during the NASA CLPX-1, March 2003, Colorado, USA. A land surface model was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-DA-based retrieval algorithm using the same PM data had a bias of -18.3 cm. The sensitivity of the assimilation scheme to the grain size uncertainty was evaluated; over the range of grain size uncertainty tested, the posterior snow depth estimate bias ranges from -2.99 cm to -9.85 cm, which is uniformly better than both the prior and retrieval estimates. This study demonstrates the potential applicability of radiometric DA at larger scales.

  10. Refinements in the use of equivalent latitude for assimilating sporadic inhomogeneous stratospheric tracer observations, 1: Detecting transport of Pinatubo aerosol across a strong vortex edge

    Directory of Open Access Journals (Sweden)

    P. Good

    2004-01-01

    Full Text Available The use of PV equivalent latitude for assimilating stratospheric tracer observations is discussed - with particular regard to the errors in the equivalent latitude coordinate, and to the assimilation of sparse data. Some example measurements are assimilated: they sample the stratosphere sporadically and inhomogeneously. The aim was to obtain precise information about the isentropic tracer distribution and evolution as a function of equivalent latitude. Precision is important, if transport across barriers like the vortex edge are to be detected directly. The main challenges addressed are the errors in modelled equivalent latitude, and the non-ideal observational sampling. The methods presented allow first some assessment of equivalent latitude errors and a picture of how good or poor the observational coverage is. This information determines choices in the approach for estimating as precisely as possible the true equivalent latitude distribution of the tracer, in periods of good and poor observational coverage. This is in practice an optimisation process, since better understanding of the equivalent latitude distribution of the tracer feeds back into a clearer picture of the errors in the modelled equivalent latitude coordinate. Error estimates constrain the reliability of using equivalent latitude to make statements like 'this observation samples air poleward of the vortex edge' or that of more general model-measurement comparisons. The approach is demonstrated for ground-based lidar soundings of the Mount Pinatubo aerosol cloud, focusing on the 1991-92 arctic vortex edge between 475-520K. Equivalent latitude is estimated at the observation times and locations from Eulerian model tracers initialised with PV and forced by UK Meteorological Office analyses. With the model formulation chosen, it is shown that tracer transport of a few days resulted in an error distribution that was much closer to Gaussian form, although the mean error was not

  11. CATS Near Real Time Data Products: Applications for Assimilation Into the NASA GEOS-5 AGCM

    Science.gov (United States)

    Hlavka, D. L.; Nowottnick, E. P.; Yorks, J. E.; Da Silva, A.; McGill, M. J.; Palm, S. P.; Selmer, P. A.; Pauly, R. M.; Ozog, S.

    2017-01-01

    From February 2015 through October 2017, the NASA Cloud-Aerosol Transport System (CATS) backscatter lidar operated on the International Space Station (ISS) as a technology demonstration for future Earth Science Missions, providing vertical measurements of cloud and aerosols properties. Owing to its location on the ISS, a cornerstone technology demonstration of CATS was the capability to acquire, process, and disseminate near-real time (NRT) data within 6 hours of observation time. CATS NRT data has several applications, including providing notification of hazardous events for air traffic control and air quality advisories, field campaign flight planning, as well as for constraining cloud and aerosol distributions in via data assimilation in aerosol transport models.   Recent developments in aerosol data assimilation techniques have permitted the assimilation of aerosol optical thickness (AOT), a 2-dimensional column integrated quantity that is reflective of the simulated aerosol loading in aerosol transport models. While this capability has greatly improved simulated AOT forecasts, the vertical position, a key control on aerosol transport, is often not impacted when 2-D AOT is assimilated. Here, we present preliminary efforts to assimilate CATS aerosol observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) atmospheric general circulation model and assimilation system using a 1-D Variational (1-D VAR) ensemble approach, demonstrating the utility of CATS for future Earth Science Missions.

  12. Evaluation and Improvement of Polar WRF simulations using the observed atmospheric profiles in the Arctic seasonal ice zone

    Science.gov (United States)

    Liu, Z.; Schweiger, A. J. B.

    2016-12-01

    We use the Polar Weather Research and Forecasting (WRF) model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) over the Beaufort Sea in the summer since 2013. With the 119 SIZRS dropsondes in the18 cross sections along the 150W and 140W longitude lines, we evaluate the performance of WRF simulations and two forcing data sets, the ERA-Interim reanalysis and the Global Forecast System (GFS) analysis, and explore the improvement of the Polar WRF performance when the dropsonde data are assimilated using observation nudging. Polar WRF, ERA-Interim, and GFS can reproduce the general features of the observed mean atmospheric profiles, such as low-level temperature inversion, low-level jet (LLJ) and specific humidity inversion. The Polar WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing, which is likely related to the lower values of the boundary layer diffusion in WRF than in the global models such as ECMWF and GFS. The Polar WRF simulated relative humidity closely resembles the forcing datasets while having large biases compared to observations. This suggests that the performance of Polar WRF and its forecasts in this region are limited by the quality of the forcing dataset and that the assimilation of more and better-calibrated observations, such as humidity data, is critical for their improvement. We investigate the potential of assimilating the SIZRS dropsonde dataset in improving the weather forecast over the Beaufort Sea. A simple local nudging approach is adopted. Along SIZRS flight cross sections, a set of Polar WRF simulations are performed with varying number of variables and dropsonde profiles assimilated. Different model physics are tested to examine the sensitivity of different aspects of model physics, such as boundary layer schemes, cloud microphysics, and radiation parameterization, to data assimilation. The comparison of the Polar WRF runs with

  13. Improving Forecast Skill by Assimilation of Quality Controlled AIRS Version 5 Temperature Soundings

    Science.gov (United States)

    Susskind, Joel; Reale, Oreste

    2009-01-01

    The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of cloud cleared radiances R(sub i). This approach allows for the generation of accurate values of R(sub i) and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for R(sub i). These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

  14. Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

    Science.gov (United States)

    Kawa, S. R.; Collatz, G. J.; Erickson, D. J.; Denning, A. S.; Wofsy, S. C.; Andrews, A. E.

    2007-01-01

    As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its

  15. Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

    Science.gov (United States)

    Mateus, Pedro; Miranda, Pedro M. A.; Nico, Giovanni; Catalão, João.; Pinto, Paulo; Tomé, Ricardo

    2018-04-01

    Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three-dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.

  16. CATS Version 2 Aerosol Feature Detection and Applications for Data Assimilation

    Science.gov (United States)

    Nowottnick, Ed; Yorks, John; McGill, Matt; Scott, Stan; Palm, Stephen; Hlavka, Dennis; Hart, William; Selmer, Patrick; Kupchock, Andrew; Pauly, Rebecca

    2017-01-01

    Using GEOS-5, we are developing a 1D ENS approach for assimilating CATS near real time observations of total attenuated backscatter at 1064 nm: a) After performing a 1-ENS assimilation of a cloud-free profile, the GEOS-5 analysis closely followed observed total attenuated backscatter. b) Vertical localization length scales were varied for the well-mixed PBL and the free troposphere After assimilating a cloud free segment of a CATS granule, the fine detail of a dust event was obtained in the GEOS-5 analysis for both total attenuated backscatter and extinction. Future Work: a) Explore horizontal localization and test within a cloudy aerosol layer. b) Address noisy analysis increments in the free troposphere where both CATS and GEOS-5 aerosol loadings are low. c) Develop a technique to screen CATS ground return from profiles. d) "Dynamic" lidar ratio that will evolve in conjunction with simulated aerosol mixtures.

  17. A coherent structure approach for parameter estimation in Lagrangian Data Assimilation

    Science.gov (United States)

    Maclean, John; Santitissadeekorn, Naratip; Jones, Christopher K. R. T.

    2017-12-01

    We introduce a data assimilation method to estimate model parameters with observations of passive tracers by directly assimilating Lagrangian Coherent Structures. Our approach differs from the usual Lagrangian Data Assimilation approach, where parameters are estimated based on tracer trajectories. We employ the Approximate Bayesian Computation (ABC) framework to avoid computing the likelihood function of the coherent structure, which is usually unavailable. We solve the ABC by a Sequential Monte Carlo (SMC) method, and use Principal Component Analysis (PCA) to identify the coherent patterns from tracer trajectory data. Our new method shows remarkably improved results compared to the bootstrap particle filter when the physical model exhibits chaotic advection.

  18. Assimilation of LAI time-series in crop production models

    Science.gov (United States)

    Kooistra, Lammert; Rijk, Bert; Nannes, Louis

    2014-05-01

    Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor

  19. A Study on the Relationships among Surface Variables to Adjust the Height of Surface Temperature for Data Assimilation.

    Science.gov (United States)

    Kang, J. H.; Song, H. J.; Han, H. J.; Ha, J. H.

    2016-12-01

    The observation processing system, KPOP (KIAPS - Korea Institute of Atmospheric Prediction Systems - Package for Observation Processing) have developed to provide optimal observations to the data assimilation system for the KIAPS Integrated Model (KIM). Currently, the KPOP has capable of processing almost all of observations for the KMA (Korea Meteorological Administration) operational global data assimilation system. The height adjustment of SURFACE observations are essential for the quality control due to the difference in height between observation station and model topography. For the SURFACE observation, it is usual to adjust the height using lapse rate or hypsometric equation, which decides values mainly depending on the difference of height. We have a question of whether the height can be properly adjusted following to the linear or exponential relationship solely with regard to the difference of height, with disregard the atmospheric conditions. In this study, firstly we analyse the change of surface variables such as temperature (T2m), pressure (Psfc), humidity (RH2m and Q2m), and wind components (U and V) according to the height difference. Additionally, we look further into the relationships among surface variables . The difference of pressure shows a strong linear relationship with difference of height. But the difference of temperature according to the height shows a significant correlation with difference of relative humidity than with the height difference. A development of reliable model for the height-adjustment of surface temperature is being undertaken based on the preliminary results.

  20. Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST

    Directory of Open Access Journals (Sweden)

    Weijing Chen

    2017-03-01

    Full Text Available Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperature (TB and MODIS (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (LST products, which also corrects model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter (DEnKS. Common Land Model (CoLM and a Radiative Transfer Model (RTM are adopted as model and observation operator, respectively. The assimilation experiment was conducted in Naqu on the Tibet Plateau from 31 May to 27 September 2011. The updated soil temperature at surface obtained by assimilating MODIS LST serving as inputs of RTM is to reduce the differences between the simulated and observed TB, then AMSR-E TB is assimilated to update soil moisture and model parameters. Compared with in situ measurements, the accuracy of soil moisture estimation derived from the assimilation experiment has been tremendously improved at a variety of scales. The updated parameters effectively reduce the states bias of CoLM. The results demonstrate the potential of assimilating AMSR-E TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study indicates that the developed scheme is an effective way to retrieve downscaled soil moisture when assimilating the coarse-scale microwave TB.

  1. Ensemble streamflow assimilation with the National Water Model.

    Science.gov (United States)

    Rafieeinasab, A.; McCreight, J. L.; Noh, S.; Seo, D. J.; Gochis, D.

    2017-12-01

    Through case studies of flooding across the US, we compare the performance of the National Water Model (NWM) data assimilation (DA) scheme to that of a newly implemented ensemble Kalman filter approach. The NOAA National Water Model (NWM) is an operational implementation of the community WRF-Hydro modeling system. As of August 2016, the NWM forecasts of distributed hydrologic states and fluxes (including soil moisture, snowpack, ET, and ponded water) over the contiguous United States have been publicly disseminated by the National Center for Environmental Prediction (NCEP) . It also provides streamflow forecasts at more than 2.7 million river reaches up to 30 days in advance. The NWM employs a nudging scheme to assimilate more than 6,000 USGS streamflow observations and provide initial conditions for its forecasts. A problem with nudging is how the forecasts relax quickly to open-loop bias in the forecast. This has been partially addressed by an experimental bias correction approach which was found to have issues with phase errors during flooding events. In this work, we present an ensemble streamflow data assimilation approach combining new channel-only capabilities of the NWM and HydroDART (a coupling of the offline WRF-Hydro model and NCAR's Data Assimilation Research Testbed; DART). Our approach focuses on the single model state of discharge and incorporates error distributions on channel-influxes (overland and groundwater) in the assimilation via an ensemble Kalman filter (EnKF). In order to avoid filter degeneracy associated with a limited number of ensemble at large scale, DART's covariance inflation (Anderson, 2009) and localization capabilities are implemented and evaluated. The current NWM data assimilation scheme is compared to preliminary results from the EnKF application for several flooding case studies across the US.

  2. Carbon assimilation and transfer through kelp forests in the NE Atlantic is diminished under a warmer ocean climate.

    Science.gov (United States)

    Pessarrodona, Albert; Moore, Pippa J; Sayer, Martin D J; Smale, Dan A

    2018-06-03

    Global climate change is affecting carbon cycling by driving changes in primary productivity and rates of carbon fixation, release and storage within Earth's vegetated systems. There is, however, limited understanding of how carbon flow between donor and recipient habitats will respond to climatic changes. Macroalgal-dominated habitats, such as kelp forests, are gaining recognition as important carbon donors within coastal carbon cycles, yet rates of carbon assimilation and transfer through these habitats are poorly resolved. Here, we investigated the likely impacts of ocean warming on coastal carbon cycling by quantifying rates of carbon assimilation and transfer in Laminaria hyperborea kelp forests-one of the most extensive coastal vegetated habitat types in the NE Atlantic-along a latitudinal temperature gradient. Kelp forests within warm climatic regimes assimilated, on average, more than three times less carbon and donated less than half the amount of particulate carbon compared to those from cold regimes. These patterns were not related to variability in other environmental parameters. Across their wider geographical distribution, plants exhibited reduced sizes toward their warm-water equatorward range edge, further suggesting that carbon flow is reduced under warmer climates. Overall, we estimated that Laminaria hyperborea forests stored ~11.49 Tg C in living biomass and released particulate carbon at a rate of ~5.71 Tg C year -1 . This estimated flow of carbon was markedly higher than reported values for most other marine and terrestrial vegetated habitat types in Europe. Together, our observations suggest that continued warming will diminish the amount of carbon that is assimilated and transported through temperate kelp forests in NE Atlantic, with potential consequences for the coastal carbon cycle. Our findings underline the need to consider climate-driven changes in the capacity of ecosystems to fix and donate carbon when assessing the impacts of

  3. Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea

    Science.gov (United States)

    Kaufman, Daniel E.; Friedrichs, Marjorie A. M.; Hemmings, John C. P.; Smith, Walker O., Jr.

    2018-01-01

    The Ross Sea is a region characterized by high primary productivity in comparison to other Antarctic coastal regions, and its productivity is marked by considerable variability both spatially (1-50 km) and temporally (days to weeks). This variability presents a challenge for inferring phytoplankton dynamics from observations that are limited in time or space, which is often the case due to logistical limitations of sampling. To better understand the spatiotemporal variability in Ross Sea phytoplankton dynamics and to determine how restricted sampling may skew dynamical interpretations, high-resolution bio-optical glider measurements were assimilated into a one-dimensional biogeochemical model adapted for the Ross Sea. The assimilation of data from the entire glider track using the micro-genetic and local search algorithms in the Marine Model Optimization Testbed improves the model-data fit by ˜ 50 %, generating rates of integrated primary production of 104 g C m-2 yr-1 and export at 200 m of 27 g C m-2 yr-1. Assimilating glider data from three different latitudinal bands and three different longitudinal bands results in minimal changes to the simulations, improves the model-data fit with respect to unassimilated data by ˜ 35 %, and confirms that analyzing these glider observations as a time series via a one-dimensional model is reasonable on these scales. Whereas assimilating the full glider data set produces well-constrained simulations, assimilating subsampled glider data at a frequency consistent with cruise-based sampling results in a wide range of primary production and export estimates. These estimates depend strongly on the timing of the assimilated observations, due to the presence of high mesoscale variability in this region. Assimilating surface glider data subsampled at a frequency consistent with available satellite-derived data results in 40 % lower carbon export, primarily resulting from optimized rates generating more slowly sinking diatoms. This

  4. Dynamical reconstruction of the global ocean state during the Last Glacial Maximum

    Science.gov (United States)

    Kurahashi-Nakamura, Takasumi; Paul, André; Losch, Martin

    2017-04-01

    The global ocean state for the modern age and for the Last Glacial Maximum (LGM) was dynamically reconstructed with a sophisticated data assimilation technique. A substantial amount of data including global seawater temperature, salinity (only for the modern estimate), and the isotopic composition of oxygen and carbon (only in the Atlantic for the LGM) were integrated into an ocean general circulation model with the help of the adjoint method, thereby the model was optimized to reconstruct plausible continuous fields of tracers, overturning circulation and water mass distribution. The adjoint-based LGM state estimation of this study represents the state of the art in terms of the length of forward model runs, the number of observations assimilated, and the model domain. Compared to the modern state, the reconstructed continuous sea-surface temperature field for the LGM shows a global-mean cooling of 2.2 K, and the reconstructed LGM ocean has a more vigorous Atlantic meridional overturning circulation, shallower North Atlantic Deep Water (NADW) equivalent, stronger stratification, and more saline deep water.

  5. Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

    Science.gov (United States)

    Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.

    2018-02-01

    The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.

  6. An Improved GRACE Terrestrial Water Storage Assimilation System For Estimating Large-Scale Soil Moisture and Shallow Groundwater

    Science.gov (United States)

    Girotto, M.; De Lannoy, G. J. M.; Reichle, R. H.; Rodell, M.

    2015-12-01

    The Gravity Recovery And Climate Experiment (GRACE) mission is unique because it provides highly accurate column integrated estimates of terrestrial water storage (TWS) variations. Major limitations of GRACE-based TWS observations are related to their monthly temporal and coarse spatial resolution (around 330 km at the equator), and to the vertical integration of the water storage components. These challenges can be addressed through data assimilation. To date, it is still not obvious how best to assimilate GRACE-TWS observations into a land surface model, in order to improve hydrological variables, and many details have yet to be worked out. This presentation discusses specific recent features of the assimilation of gridded GRACE-TWS data into the NASA Goddard Earth Observing System (GEOS-5) Catchment land surface model to improve soil moisture and shallow groundwater estimates at the continental scale. The major recent advancements introduced by the presented work with respect to earlier systems include: 1) the assimilation of gridded GRACE-TWS data product with scaling factors that are specifically derived for data assimilation purposes only; 2) the assimilation is performed through a 3D assimilation scheme, in which reasonable spatial and temporal error standard deviations and correlations are exploited; 3) the analysis step uses an optimized calculation and application of the analysis increments; 4) a poor-man's adaptive estimation of a spatially variable measurement error. This work shows that even if they are characterized by a coarse spatial and temporal resolution, the observed column integrated GRACE-TWS data have potential for improving our understanding of soil moisture and shallow groundwater variations.

  7. Turbulent viscosity optimized by data assimilation

    Directory of Open Access Journals (Sweden)

    Y. Leredde

    Full Text Available As an alternative approach to classical turbulence modelling using a first or second order closure, the data assimilation method of optimal control is applied to estimate a time and space-dependent turbulent viscosity in a three-dimensional oceanic circulation model. The optimal control method, described for a 3-D primitive equation model, involves the minimization of a cost function that quantifies the discrepancies between the simulations and the observations. An iterative algorithm is obtained via the adjoint model resolution. In a first experiment, a k + L model is used to simulate the one-dimensional development of inertial oscillations resulting from a wind stress at the sea surface and with the presence of a halocline. These results are used as synthetic observations to be assimilated. The turbulent viscosity is then recovered without the k + L closure, even with sparse and noisy observations. The problems of controllability and of the dimensions of the control are then discussed. A second experiment consists of a two-dimensional schematic simulation. A 2-D turbulent viscosity field is estimated from data on the initial and final states of a coastal upwelling event.

    Key words. Oceanography: general (numerical modelling · Oceanography: physical (turbulence · diffusion · and mixing processes

  8. Assessing Hydrological and Energy Budgets in Amazonia through Regional Downscaling, and Comparisons with Global Reanalysis Products

    Science.gov (United States)

    Nunes, A.; Ivanov, V. Y.

    2014-12-01

    Although current global reanalyses provide reasonably accurate large-scale features of the atmosphere, systematic errors are still found in the hydrological and energy budgets of such products. In the tropics, precipitation is particularly challenging to model, which is also adversely affected by the scarcity of hydrometeorological datasets in the region. With the goal of producing downscaled analyses that are appropriate for a climate assessment at regional scales, a regional spectral model has used a combination of precipitation assimilation with scale-selective bias correction. The latter is similar to the spectral nudging technique, which prevents the departure of the regional model's internal states from the large-scale forcing. The target area in this study is the Amazon region, where large errors are detected in reanalysis precipitation. To generate the downscaled analysis, the regional climate model used NCEP/DOE R2 global reanalysis as the initial and lateral boundary conditions, and assimilated NOAA's Climate Prediction Center (CPC) MORPHed precipitation (CMORPH), available at 0.25-degree resolution, every 3 hours. The regional model's precipitation was successfully brought closer to the observations, in comparison to the NCEP global reanalysis products, as a result of the impact of a precipitation assimilation scheme on cumulus-convection parameterization, and improved boundary forcing achieved through a new version of scale-selective bias correction. Water and energy budget terms were also evaluated against global reanalyses and other datasets.

  9. Evaluation of linear ozone photochemistry parametrizations in a stratosphere-troposphere data assimilation system

    Directory of Open Access Journals (Sweden)

    A. J. Geer

    2007-01-01

    Full Text Available This paper evaluates the performance of various linear ozone photochemistry parametrizations using the stratosphere-troposphere data assimilation system of the Met Office. A set of experiments were run for the period 23 September 2003 to 5 November 2003 using the Cariolle (v1.0 and v2.1, LINOZ and Chem2D-OPP (v0.1 and v2.1 parametrizations. All operational meteorological observations were assimilated, together with ozone retrievals from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS. Experiments were validated against independent data from the Halogen Occultation Experiment (HALOE and ozonesondes. Additionally, a simple offline method for comparing the parametrizations is introduced. It is shown that in the upper stratosphere and mesosphere, outside the polar night, ozone analyses are controlled by the photochemistry parametrizations and not by the assimilated observations. The most important factor in getting good results at these levels is to pay attention to the ozone and temperature climatologies in the parametrizations. There should be no discrepancies between the climatologies and the assimilated observations or the model, but there is also a competing demand that the climatologies be objectively accurate in themselves. Conversely, in the lower stratosphere outside regions of heterogeneous ozone depletion, the ozone analyses are dominated by observational increments and the photochemistry parametrizations have little influence. We investigate a number of known problems in LINOZ and Cariolle v1.0 in more detail than previously, and we find discrepancies in Cariolle v2.1 and Chem2D-OPP v2.1, which are demonstrated to have been removed in the latest available versions (v2.8 and v2.6 respectively. In general, however, all the parametrizations work well through much of the stratosphere, helped by the presence of good quality assimilated MIPAS observations.

  10. ASSIMILATION OF DOPPLER RADAR DATA INTO NUMERICAL WEATHER MODELS

    Energy Technology Data Exchange (ETDEWEB)

    Chiswell, S.; Buckley, R.

    2009-01-15

    During the year 2008, the United States National Weather Service (NWS) completed an eight fold increase in sampling capability for weather radars to 250 m resolution. This increase is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current NWS operational model domains utilize grid spacing an order of magnitude larger than the radar data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of radar reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution was investigated under a Laboratory Directed Research and Development (LDRD) 'quick hit' grant to determine the impact of improved data resolution on model predictions with specific initial proof of concept application to daily Savannah River Site operations and emergency response. Development of software to process NWS radar reflectivity and radial velocity data was undertaken for assimilation of observations into numerical models. Data values within the radar data volume undergo automated quality control (QC) analysis routines developed in support of this project to eliminate empty/missing data points, decrease anomalous propagation values, and determine error thresholds by utilizing the calculated variances among data values. The Weather Research and Forecasting model (WRF) three dimensional variational data assimilation package (WRF-3DVAR) was used to incorporate the QC'ed radar data into input and boundary conditions. The lack of observational data in the vicinity of SRS available to NWS operational models signifies an important data void where radar observations can provide significant input. These observations greatly enhance the knowledge of storm structures and the environmental conditions which influence their development. As the increase in computational power and availability has

  11. Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China

    Science.gov (United States)

    Zhu, X.; Wen, X.; Zheng, Z.

    2017-12-01

    For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global LandData Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We found that the satellite-derived GVF from MODIS increased over southeast China compared with the default model over the whole year. The simulated results of soil temperature, net radiation and surface energy flux from the HRADC are improved compared with the control simulation and are close to GLDAS outputs. The values of net radiation from HRADC are higher than the GLDAS outputs, and the differences in the simulations are large in the east region but are smaller in northwest China and on the Qinghai-Tibet Plateau. The spatial distribution of the sensible heat flux and the ground

  12. A hybrid modeling with data assimilation to evaluate human exposure level

    Science.gov (United States)

    Koo, Y. S.; Cheong, H. K.; Choi, D.; Kim, A. L.; Yun, H. Y.

    2015-12-01

    Exposure models are designed to better represent human contact with PM (Particulate Matter) and other air pollutants such as CO, SO2, O3, and NO2. The exposure concentrations of the air pollutants to human are determined by global and regional long range transport of global and regional scales from Europe and China as well as local emissions from urban and road vehicle sources. To assess the exposure level in detail, the multiple scale influence from background to local sources should be considered. A hybrid air quality modeling methodology combing a grid-based chemical transport model with a local plume dispersion model was used to provide spatially and temporally resolved air quality concentration for human exposure levels in Korea. In the hybrid modeling approach, concentrations from a grid-based chemical transport model and a local plume dispersion model are added to provide contributions from photochemical interactions, long-range (regional) transport and local-scale dispersion. The CAMx (Comprehensive Air quality Model with Extensions was used for the background concentrations from anthropogenic and natural emissions in East Asia including Korea while the road dispersion by vehicle emission was calculated by CALPUFF model. The total exposure level of the pollutants was finally assessed by summing the background and road contributions. In the hybrid modeling, the data assimilation method based on the optimal interpolation was applied to overcome the discrepancies between the model predicted concentrations and observations. The air quality data from the air quality monitoring stations in Korea. The spatial resolution of the hybrid model was 50m for the Seoul Metropolitan Ares. This example clearly demonstrates that the exposure level could be estimated to the fine scale for the exposure assessment by using the hybrid modeling approach with data assimilation.

  13. ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER

    KAUST Repository

    Efendiev, Yalchin

    2011-01-01

    Reservoir data is usually scale dependent and exhibits multiscale features. In this paper we use the ensemble Kalman filter (EnKF) to integrate data at different spatial scales for estimating reservoir fine-scale characteristics. Relationships between the various scales is modeled via upscaling techniques. We propose two versions of the EnKF to assimilate the multiscale data, (i) where all the data are assimilated together and (ii) the data are assimilated sequentially in batches. Ensemble members obtained after assimilating one set of data are used as a prior to assimilate the next set of data. Both of these versions are easily implementable with any other upscaling which links the fine to the coarse scales. The numerical results with different methods are presented in a twin experiment setup using a two-dimensional, two-phase (oil and water) flow model. Results are shown with coarse-scale permeability and coarse-scale saturation data. They indicate that additional data provides better fine-scale estimates and fractional flow predictions. We observed that the two versions of the EnKF differed in their estimates when coarse-scale permeability is provided, whereas their results are similar when coarse-scale saturation is used. This behavior is thought to be due to the nonlinearity of the upscaling operator in the case of the former data. We also tested our procedures with various precisions of the coarse-scale data to account for the inexact relationship between the fine and coarse scale data. As expected, the results show that higher precision in the coarse-scale data yielded improved estimates. With better coarse-scale modeling and inversion techniques as more data at multiple coarse scales is made available, the proposed modification to the EnKF could be relevant in future studies.

  14. The impact of atmospheric data assimilation on wave simulations in the Red Sea

    KAUST Repository

    Langodan, Sabique

    2016-03-11

    Although wind and wave modeling is rather successful in the open ocean, modeling enclosed seas, particularly seas with small basins and complex orography, presents challenges. Here, we use data assimilation to improve wind and wave simulations in the Red Sea. We generated two sets of wind fields using a nested, high-resolution Weather Research and Forecasting model implemented with (VARFC) and without (CTL) assimilation of observations. Available conventional and satellite data were assimilated using the consecutive integration method with daily initializations over one year (2009). By evaluating the two wind products against in-situ data from synoptic stations, buoys, scatterometers, and altimeters, we found that seasonal patterns of wind and wave variability were well reproduced in both experiments. Statistical scores for simulated winds computed against QuikSCAT, buoy, and synoptic station observations suggest that data assimilation decreases the root-mean-square error to values between 1 and 2 m s-1 and reduces the scatter index by 30% compared to the CTL. Sensitivity clearly increased around mountain gaps, where the channeling effect is better described by VARFC winds. The impact of data assimilation is more pronounced in wave simulations, particularly during extreme winds and in the presence of mountain jets. © 2016 Elsevier Ltd. All rights reserved.

  15. Comparison of Sequential and Variational Data Assimilation

    Science.gov (United States)

    Alvarado Montero, Rodolfo; Schwanenberg, Dirk; Weerts, Albrecht

    2017-04-01

    Data assimilation is a valuable tool to improve model state estimates by combining measured observations with model simulations. It has recently gained significant attention due to its potential in using remote sensing products to improve operational hydrological forecasts and for reanalysis purposes. This has been supported by the application of sequential techniques such as the Ensemble Kalman Filter which require no additional features within the modeling process, i.e. it can use arbitrary black-box models. Alternatively, variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function describes the trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables. While sequential techniques have been commonly applied to hydrological processes, variational techniques are seldom used. In our believe, this is mainly attributed to the required computation of first order sensitivities by algorithmic differentiation techniques and related model enhancements, but also to lack of comparison between both techniques. We contribute to filling this gap and present the results from the assimilation of streamflow data in two basins located in Germany and Canada. The assimilation introduces noise to precipitation and temperature to produce better initial estimates of an HBV model. The results are computed for a hindcast period and assessed using lead time performance metrics. The study concludes with a discussion of the main features of each technique and their advantages/disadvantages in hydrological applications.

  16. Data Assimilation of Dead Fuel Moisture Observations from Remote automated Weather Stations

    Czech Academy of Sciences Publication Activity Database

    Vejmelka, Martin; Kochanski, A.; Mandel, Jan

    2016-01-01

    Roč. 25, č. 5 (2016), s. 558-568 ISSN 1049-8001 R&D Projects: GA ČR GA13-34856S Grant - others:National Science Foundation(US) AGS-0835579 and DMS-1216481; NASA (US) NNX12AQ85G and NNX13AH9G. Institutional support: RVO:67985807 Keywords : data assimilation * dead fuel moisture * equilibrium * Kalman filter * remote automated weather stations * time lag model * trend surface model Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.748, year: 2016

  17. Variational Data Assimilative Modeling of the Gulf of Maine Circulation in Spring and Summer 2010

    OpenAIRE

    Li, Yizhen; He, Ruoying; Chen, Ke; McGillicuddy, Dennis J.

    2015-01-01

    A data assimilative ocean circulation model is used to hindcast the Gulf of Maine (GOM) circulation in spring and summer 2010. Using the recently developed incremental strong constraint 4D Variational data assimilation algorithm, the model assimilates satellite sea surface temperature and in situ temperature and salinity profiles measured by expendable bathythermograph, Argo floats, and shipboard CTD casts. Validation against independent observations shows that the model skill is significantl...

  18. Assimilation of lightning data by nudging tropospheric water vapor and applications to numerical forecasts of convective events

    Science.gov (United States)

    Dixon, Kenneth

    A lightning data assimilation technique is developed for use with observations from the World Wide Lightning Location Network (WWLLN). The technique nudges the water vapor mixing ratio toward saturation within 10 km of a lightning observation. This technique is applied to deterministic forecasts of convective events on 29 June 2012, 17 November 2013, and 19 April 2011 as well as an ensemble forecast of the 29 June 2012 event using the Weather Research and Forecasting (WRF) model. Lightning data are assimilated over the first 3 hours of the forecasts, and the subsequent impact on forecast quality is evaluated. The nudged deterministic simulations for all events produce composite reflectivity fields that are closer to observations. For the ensemble forecasts of the 29 June 2012 event, the improvement in forecast quality from lightning assimilation is more subtle than for the deterministic forecasts, suggesting that the lightning assimilation may improve ensemble convective forecasts where conventional observations (e.g., aircraft, surface, radiosonde, satellite) are less dense or unavailable.

  19. Observing the continental-scale carbon balance: assessment of sampling complementarity and redundancy in a terrestrial assimilation system by means of quantitative network design

    OpenAIRE

    Kaminski, T.; Rayner, P. J.; Vossbeck, M.; Scholze, M.; Koffi, E.

    2012-01-01

    This paper investigates the relationship between the heterogeneity of the terrestrial carbon cycle and the optimal design of observing networks to constrain it. We combine the methods of quantitative network design and carbon-cycle data assimilation to a hierarchy of increasingly heterogeneous descriptions of the European terrestrial biosphere as indicated by increasing diversity of plant functional types. We employ three types of observat...

  20. Combined assimilation of screen-level observations and radar-derived precipitation for soil moisture analysis

    Czech Academy of Sciences Publication Activity Database

    Mahfouf, J.; F.; Bližňák, Vojtěch

    2011-01-01

    Roč. 137, č. 656 (2011), s. 709-722 ISSN 0035-9009 Institutional research plan: CEZ:AV0Z30420517 Keywords : data assimilation * weather prediction * land surface schemes Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.907, year: 2011 http://onlinelibrary.wiley.com/doi/10.1002/qj.791/abstract

  1. Examining the Suitability of a Sparse In Situ Soil Moisture Monitoring Network for Assimilation into a Spatially Distributed Hydrologic Model

    Science.gov (United States)

    De Vleeschouwer, N.; Verhoest, N.; Pauwels, V. R. N.

    2015-12-01

    The continuous monitoring of soil moisture in a permanent network can yield an interesting data product for use in hydrological data assimilation. Major advantages of in situ observations compared to remote sensing products are the potential vertical extent of the measurements, the finer temporal resolution of the observation time series, the smaller impact of land cover variability on the observation bias, etc. However, two major disadvantages are the typical small integration volume of in situ measurements and the often large spacing between monitoring locations. This causes only a small part of the modelling domain to be directly observed. Furthermore, the spatial configuration of the monitoring network is typically temporally non-dynamic. Therefore two questions can be raised. Do spatially sparse in situ soil moisture observations contain a sufficient data representativeness to successfully assimilate them into the largely unobserved spatial extent of a distributed hydrological model? And if so, how is this assimilation best performed? Consequently two important factors that can influence the success of assimilating in situ monitored soil moisture are the spatial configuration of the monitoring network and the applied assimilation algorithm. In this research the influence of those factors is examined by means of synthetic data-assimilation experiments. The study area is the ± 100 km² catchment of the Bellebeek in Flanders, Belgium. The influence of the spatial configuration is examined by varying the amount of locations and their position in the landscape. The latter is performed using several techniques including temporal stability analysis and clustering. Furthermore the observation depth is considered by comparing assimilation of surface layer (5 cm) and deeper layer (50 cm) observations. The impact of the assimilation algorithm is assessed by comparing the performance obtained with two well-known algorithms: Newtonian nudging and the Ensemble Kalman

  2. Biomass assimilation in coupled ecohydrodynamical model of the Mediterranean Sea

    Science.gov (United States)

    Crispi, G.; Bournaski, E.; Crise, A.

    2003-04-01

    Data assimilation has raised new interest in the last years in the context of the environmental sciences. The swift increment of the attention paid to it in oceanography is due to the coming age of operational services for the marine environment which is going to dramatically increase the demand for accurate, timely and reliable estimates of the space and time distribution both for physical and in a near future for biogeochemical fields. Data assimilation combines information derived from measurements with knowledge of the rules that govern the evolution of the system of interest through formalization and implementation in numerical models. The importance of ocean data assimilation has been recognized by several international programmes as JGOFS, GOOS and CLIVAR. This work presents an eco-hydrodynamic model of the Mediterranean Sea developed at the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy. It includes 3-D MOM-based hydrodynamics of the Mediterranean Sea, coupled with biochemical model of Nitrogen, Phytoplankton, Zooplankton, and Detritus (NPZD). Monthly mean wind forcings are adopted to force this MOM-NPZD model. For better prediction and analysis of N, P, Z and D distributions in the sea the model needs data assimilation from biomass observations on the sea surface. Chosen approach for evaluating performances of data assimilation techniques in coupled model is the definition of a twin experiment testbed where a reference run is carried out assuming its result as the truth. We define a sampling strategy to obtain different datasets to be incorporated in another ecological model in successive runs in order to appraise the potential of the data assimilation and sampling strategy. The runs carried out with different techniques and different spatio-temporal coverages are compared in order to evaluate the sensitivity to different coverage of dataset. The discussed alternative way is to assume the ecosystem at steady state and

  3. Recent Updates to SWANFAR (registered trademark), a 5DVAR Data Assimilation System for SWAN

    Science.gov (United States)

    2016-11-10

    inside surf zone. Blue, red, and black lines illustrate heights of small, medium, and large waves as they approach shore (from right to left). If...small, medium, or large offshore waves. Only assimilation from outside the surf zone (such as within the dashed box region) will accu- rately reflect...involv- ing a systematic and consistent examination of much larger global wave datasets and many more model simulations at global , regional, and local

  4. The Met Office Coupled Atmosphere/Land/Ocean/Sea-Ice Data Assimilation System

    Science.gov (United States)

    Lea, Daniel; Mirouze, Isabelle; King, Robert; Martin, Matthew; Hines, Adrian

    2015-04-01

    The Met Office has developed a weakly-coupled data assimilation (DA) system using the global coupled model HadGEM3 (Hadley Centre Global Environment Model, version 3). At present the analysis from separate ocean and atmosphere DA systems are combined to produced coupled forecasts. The aim of coupled DA is to produce a more consistent analysis for coupled forecasts which may lead to less initialisation shock and improved forecast performance. The HadGEM3 coupled model combines the atmospheric model UM (Unified Model) at 60 km horizontal resolution on 85 vertical levels, the ocean model NEMO (Nucleus for European Modelling of the Ocean) at 25 km (at the equator) horizontal resolution on 75 vertical levels, and the sea-ice model CICE at the same resolution as NEMO. The atmosphere and the ocean/sea-ice fields are coupled every 1-hour using the OASIS coupler. The coupled model is corrected using two separate 6-hour window data assimilation systems: a 4D-Var for the atmosphere with associated soil moisture content nudging and snow analysis schemes on the one hand, and a 3D-Var FGAT for the ocean and sea-ice on the other hand. The background information in the DA systems comes from a previous 6-hour forecast of the coupled model. To isolate the impact of the coupled DA, 13-month experiments have been carried out, including 1) a full atmosphere/land/ocean/sea-ice coupled DA run, 2) an atmosphere-only run forced by OSTIA SSTs and sea-ice with atmosphere and land DA, and 3) an ocean-only run forced by atmospheric fields from run 2 with ocean and sea-ice DA. In addition, 5-day and 10-day forecast runs, have been produced from initial conditions generated by either run 1 or a combination of runs 2 and 3. The different results have been compared to each other and, whenever possible, to other references such as the Met Office atmosphere and ocean operational analyses or the OSTIA SST data. The performance of the coupled DA is similar to the existing separate ocean and atmosphere

  5. Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

    Science.gov (United States)

    Lu, Yang; Steele-Dunne, Susan C.; Farhadi, Leila; van de Giesen, Nick

    2017-12-01

    Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large-scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual-source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST-only and a soil moisture-only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.

  6. Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

    KAUST Repository

    Khaki, M.

    2017-07-06

    The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

  7. Targeted observations to improve tropical cyclone track forecasts in the Atlantic and eastern Pacific basins

    Science.gov (United States)

    Aberson, Sim David

    In 1997, the National Hurricane Center and the Hurricane Research Division began conducting operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve operational forecast models. During the first two years, twenty-four missions were conducted around tropical cyclones threatening the continental United States, Puerto Rico, and the Virgin Islands. Global Positioning System dropwindsondes were released from the aircraft at 150--200 km intervals along the flight track in the tropical cyclone environment to obtain wind, temperature, and humidity profiles from flight level (around 150 hPa) to the surface. The observations were processed and formatted aboard the aircraft and transmitted to the National Centers for Environmental Prediction (NCEP). There, they were ingested into the Global Data Assimilation System that subsequently provides initial and time-dependent boundary conditions for numerical models that forecast tropical cyclone track and intensity. Three dynamical models were employed in testing the targeting and sampling strategies. With the assimilation into the numerical guidance of all the observations gathered during the surveillance missions, only the 12-h Geophysical Fluid Dynamics Laboratory Hurricane Model forecast showed statistically significant improvement. Neither the forecasts from the Aviation run of the Global Spectral Model nor the shallow-water VICBAR model were improved with the assimilation of the dropwindsonde data. This mediocre result is found to be due mainly to the difficulty in operationally quantifying the storm-motion vector used to create accurate synthetic data to represent the tropical cyclone vortex in the models. A secondary limit on forecast improvements from the surveillance missions is the limited amount of data provided by the one surveillance aircraft in regular missions. The inability of some surveillance missions to surround the tropical cyclone with dropwindsonde observations is a possible

  8. An Observing System Simulation Experiment (OSSE to Assess the Impact of Doppler Wind Lidar (DWL Measurements on the Numerical Simulation of a Tropical Cyclone

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2010-01-01

    Full Text Available The importance of wind observations has been recognized for many years. However, wind observations—especially three-dimensional global wind measurements—are very limited. A satellite-based Doppler Wind Lidar (DWL is proposed to measure three-dimensional wind profiles using remote sensing techniques. Assimilating these observations into a mesoscale model is expected to improve the performance of the numerical weather prediction (NWP models. In order to examine the potential impact of the DWL three-dimensional wind profile observations on the numerical simulation and prediction of tropical cyclones, a set of observing simulation system experiments (OSSEs is performed using the advanced research version of the Weather Research and Forecasting (WRF model and its three-dimensional variational (3DVAR data assimilation system. Results indicate that assimilating the DWL wind observations into the mesoscale numerical model has significant potential for improving tropical cyclone track and intensity forecasts.

  9. Development of a multi-data assimilation scheme to integrate Bio-Argo floats data with ocean colour satellite data into the CMEMS MFC-Biogeochemistry

    Science.gov (United States)

    Cossarini, Gianpiero; D'Ortenzio, Fabrizio; Mariotti, Laura; Mignot, Alexandre; Salon, Stefano

    2017-04-01

    The Mediterranean Sea is a very promising site to develop and test the assimilation of Bio-Argo data since 1) the Bio-Argo network is one of the densest of the global ocean, and 2) a consolidate data assimilation framework of biogeochemical variables (3DVAR-BIO, presently based on assimilation of satellite-estimated surface chlorophyll data) already exists within the CMEMS biogeochemical model system for Mediterranean Sea. The MASSIMILI project, granted by the CMEMS Service Evolution initiative, is aimed to develop the assimilation of Bio-Argo Floats data into the CMEMS biogeochemical model system of the Mediterranean Sea, by means of an upgrade of the 3DVAR-BIO scheme. Specific developments of the 3DVAR-BIO scheme focus on the estimate of new operators of the variational decomposition of the background error covariance matrix and on the implementation of the new observation operator specifically for the Bio-Argo float vertical profile data. In particular, a new horizontal covariance operator for chlorophyll, nitrate and oxygen is based on 3D fields of horizontal correlation radius calculated from a long-term reanalysis simulation. A new vertical covariance operator is built on monthly and spatial varying EOF decomposition to account for the spatiotemporal variability of vertical structure of the three variables error covariance. Further, the observation error covariance is a key factor for an effective assimilation of the Bio-Argo data into the model dynamics. The sensitivities of assimilation to the different factors are estimated. First results of the implementation of the new 3DVAR-BIO scheme show the impact of Bio-Argo data on the 3D fields of chlorophyll, nitrate and oxygen. Tuning the length scale factors of horizontal covariance, analysing the sensitivity of the observation error covariance, introducing non-diagonal biogeochemical covariance operator and non-diagonal multi-platform operator (i.e. Bio-Argo and satellite) are crucial future steps for the

  10. A prototype data assimilation framework for generating spatiotemporally continuous SWOT data products

    Science.gov (United States)

    Andreadis, K.; Margulis, S. A.; Li, D.; Lettenmaier, D. P.

    2017-12-01

    The Surface Water and Ocean Topography (SWOT) satellite will provide critical surface water observations for the hydrologic community. However, production of key SWOT variables, such as river discharge and surface inundation, as well as lake, reservoir, and wetland storage change will be complicated by the discontinuity of the observations in space and time. A methodology that generates products with spatially and temporally continuous fields based on SWOT observables would be highly desirable. Data assimilation provides a mechanism for merging observations from SWOT with model predictions in order to produce estimates of quantities such as river discharge, storage change, and water heights for locations and times when there is no satellite overpass or other constraints (such as layover) render the measurement unusable. We describe here a prototype assimilation system with application to the Upper Mississippi basin, implemented using synthetic SWOT observations. We use a hydrologic model (VIC) coupled with a hydrodynamic model (LISFLOOD-FP) which generates "true" fields of surface water variables. The true fields are then used to generate synthetic SWOT observations using the SWOT Instrument Simulator. We also perform a "first-guess" (or open-loop) simulation with the coupled model using a configuration that contains errors representative of the imperfect knowledge of parameters and input data, including channel topography, bankfull widths and depths, and inflows, to create an ensemble of 20 model trajectories. Subsequently we assimilate the synthetic SWOT observations into the open-loop model results to estimate water surface elevation, discharge, and storage change. Our preliminary results using three data assimilation strategies show that all improve the water surface elevation estimate accuracy by 25% - 35% for a river reach of the upper Mississippi River. Ongoing work is examining whether the improved water surface elevation estimates propagate to improvements

  11. Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

    Directory of Open Access Journals (Sweden)

    Ronan Fablet

    2018-02-01

    Full Text Available The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST. We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale.

  12. Coordination and Integration of Global Ocean Observing through JCOMM

    Science.gov (United States)

    Legler, D. M.; Meldrum, D. T.; Hill, K. L.; Charpentier, E.

    2016-02-01

    The primary objective of the JCOMM Observations Coordination Group (OCG) is to provide technical coordination to implement fully integrated ocean observing system across the entire marine meteorology and oceanographic community. JCOMM OCG works in partnership with the Global Ocean Observing System, , which focusses on setting observing system requirements and conducting evalutions. JCOMM OCG initially focused on major global observing networks (e.g. Argo profiling floats, moored buoys, ship based observations, sea level stations, reference sites, etc), and is now expanding its horizon in recognition of new observing needs and new technologies/networks (e.g. ocean gliders). Over the next five years the JCOMM OCG is focusing its attention on integration and coordination in four major areas: observing network implementation particularly in response to integrated ocean observing requirements; observing system monitoring and metrics; standards and best practices; and improving integrated data management and access. This presentation will describe the scope and mission of JCOMM OCG; summarize the state of the global ocean observing system; highlight recent successes and resources for the research, prediction, and assessment communities; summarize our plans for the next several years; and suggest engagement opportunities.

  13. The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

    Science.gov (United States)

    Kurzrock, Frederik; Cros, Sylvain; Chane-Ming, Fabrice; Potthast, Roland; Linguet, Laurent; Sébastien, Nicolas

    2016-04-01

    As an intermittent energy source, the injection of solar power into electricity grids requires irradiance forecasting in order to ensure grid stability. On time scales of more than six hours ahead, numerical weather prediction (NWP) is recognized as the most appropriate solution. However, the current representation of clouds in NWP models is not sufficiently precise for an accurate forecast of solar irradiance at ground level. Dynamical downscaling does not necessarily increase the quality of irradiance forecasts. Furthermore, incorrectly simulated cloud evolution is often the cause of inaccurate atmospheric analyses. In non-interconnected tropical areas, the large amplitudes of solar irradiance variability provide abundant solar yield but present significant problems for grid safety. Irradiance forecasting is particularly important for solar power stakeholders in these regions where PV electricity penetration is increasing. At the same time, NWP is markedly more challenging in tropic areas than in mid-latitudes due to the special characteristics of tropical homogeneous convective air masses. Numerous data assimilation methods and strategies have evolved and been applied to a large variety of global and regional NWP models in the recent decades. Assimilating data from geostationary meteorological satellites is an appropriate approach. Indeed, models converting radiances measured by satellites into cloud properties already exist. Moreover, data are available at high temporal frequencies, which enable a pertinent cloud cover evolution modelling for solar energy forecasts. In this work, we present a survey of different approaches which aim at improving cloud cover forecasts using the assimilation of geostationary meteorological satellite data into regional NWP models. Various approaches have been applied to a variety of models and satellites and in different regions of the world. Current methods focus on the assimilation of cloud-top information, derived from infrared

  14. Methodological Developments in Geophysical Assimilation Modeling

    Science.gov (United States)

    Christakos, George

    2005-06-01

    This work presents recent methodological developments in geophysical assimilation research. We revisit the meaning of the term "solution" of a mathematical model representing a geophysical system, and we examine its operational formulations. We argue that an assimilation solution based on epistemic cognition (which assumes that the model describes incomplete knowledge about nature and focuses on conceptual mechanisms of scientific thinking) could lead to more realistic representations of the geophysical situation than a conventional ontologic assimilation solution (which assumes that the model describes nature as is and focuses on form manipulations). Conceptually, the two approaches are fundamentally different. Unlike the reasoning structure of conventional assimilation modeling that is based mainly on ad hoc technical schemes, the epistemic cognition approach is based on teleologic criteria and stochastic adaptation principles. In this way some key ideas are introduced that could open new areas of geophysical assimilation to detailed understanding in an integrated manner. A knowledge synthesis framework can provide the rational means for assimilating a variety of knowledge bases (general and site specific) that are relevant to the geophysical system of interest. Epistemic cognition-based assimilation techniques can produce a realistic representation of the geophysical system, provide a rigorous assessment of the uncertainty sources, and generate informative predictions across space-time. The mathematics of epistemic assimilation involves a powerful and versatile spatiotemporal random field theory that imposes no restriction on the shape of the probability distributions or the form of the predictors (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated) and accounts rigorously for the uncertainty features of the geophysical system. In the epistemic cognition context the assimilation concept may be used to

  15. Data assimilation in hydrological modelling

    DEFF Research Database (Denmark)

    Drecourt, Jean-Philippe

    Data assimilation is an invaluable tool in hydrological modelling as it allows to efficiently combine scarce data with a numerical model to obtain improved model predictions. In addition, data assimilation also provides an uncertainty analysis of the predictions made by the hydrological model....... In this thesis, the Kalman filter is used for data assimilation with a focus on groundwater modelling. However the developed techniques are general and can be applied also in other modelling domains. Modelling involves conceptualization of the processes of Nature. Data assimilation provides a way to deal...... with model non-linearities and biased errors. A literature review analyzes the most popular techniques and their application in hydrological modelling. Since bias is an important problem in groundwater modelling, two bias aware Kalman filters have been implemented and compared using an artificial test case...

  16. Regime-dependence of Impacts of Radar Rainfall Data Assimilation

    Science.gov (United States)

    Craig, G. C.; Keil, C.

    2009-04-01

    Experience from the first operational trials of assimilation of radar data in kilometre scale numerical weather prediction models (operating without cumulus parameterisation) shows that the positive impact of the radar data on convective precipitation forecasts typically decay within a few hours, although certain cases show much longer impacts. Here the impact time of radar data assimilation is related to characteristics of the meteorological environment. This QPF uncertainty is investigated using an ensemble of 10 forecasts at 2.8 km horizontal resolution based on different initial and boundary conditions from a global forecast ensemble. Control forecasts are compared with forecasts where radar reflectivity data is assimilated using latent heat nudging. Examination of different cases of convection in southern Germany suggests that the forecasts can be separated into two regimes using a convective timescale. Short impact times are associated with short convective timescales that are characteristic of equilibrium convection. In this regime the statistical properties of the convection are constrained by the large-scale forcing, and effects of the radar data are lost within a few hours as the convection rapidly returns to equilibrium. When the convective timescale is large (non-equilibrium conditions), the impact of the radar data is longer since convective systems are triggered by the latent heat nudging and are able to persist for many hours in the very unstable conditions present in these cases.

  17. Rainfall assimilation in RAMS by means of the Kuo parameterisation inversion: method and preliminary results

    Science.gov (United States)

    Orlandi, A.; Ortolani, A.; Meneguzzo, F.; Levizzani, V.; Torricella, F.; Turk, F. J.

    2004-03-01

    In order to improve high-resolution forecasts, a specific method for assimilating rainfall rates into the Regional Atmospheric Modelling System model has been developed. It is based on the inversion of the Kuo convective parameterisation scheme. A nudging technique is applied to 'gently' increase with time the weight of the estimated precipitation in the assimilation process. A rough but manageable technique is explained to estimate the partition of convective precipitation from stratiform one, without requiring any ancillary measurement. The method is general purpose, but it is tuned for geostationary satellite rainfall estimation assimilation. Preliminary results are presented and discussed, both through totally simulated experiments and through experiments assimilating real satellite-based precipitation observations. For every case study, Rainfall data are computed with a rapid update satellite precipitation estimation algorithm based on IR and MW satellite observations. This research was carried out in the framework of the EURAINSAT project (an EC research project co-funded by the Energy, Environment and Sustainable Development Programme within the topic 'Development of generic Earth observation technologies', Contract number EVG1-2000-00030).

  18. Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

    Science.gov (United States)

    Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

    2016-12-01

    The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In

  19. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment

    NARCIS (Netherlands)

    Curnel, Y.; Wit, de A.J.W.; Duveiller, G.; Defourny, P.

    2011-01-01

    An Observing System Simulation Experiment (OSSE) has been defined to assess the potentialities of assimilating winter wheat leaf area index (LAI) estimations derived from remote sensing into the crop growth model WOFOST. Two assimilation strategies are considered: one based on Ensemble Kalman Filter

  20. A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling

    Science.gov (United States)

    Eibern, Hendrik; Schmidt, Hauke

    1999-08-01

    The inverse problem of data assimilation of tropospheric trace gas observations into an Eulerian chemistry transport model has been solved by the four-dimensional variational technique including chemical reactions, transport, and diffusion. The University of Cologne European Air Pollution Dispersion Chemistry Transport Model 2 with the Regional Acid Deposition Model 2 gas phase mechanism is taken as the basis for developing a full four-dimensional variational data assimilation package, on the basis of the adjoint model version, which includes the adjoint operators of horizontal and vertical advection, implicit vertical diffusion, and the adjoint gas phase mechanism. To assess the potential and limitations of the technique without degrading the impact of nonperfect meteorological analyses and statistically not established error covariance estimates, artificial meteorological data and observations are used. The results are presented on the basis of a suite of experiments, where reduced records of artificial "observations" are provided to the assimilation procedure, while other "data" is retained for performance control of the analysis. The paper demonstrates that the four-dimensional variational technique is applicable for a comprehensive chemistry transport model in terms of computational and storage requirements on advanced parallel platforms. It is further shown that observed species can generally be analyzed, even if the "measurements" have unbiased random errors. More challenging experiments are presented, aiming to tax the skill of the method (1) by restricting available observations mostly to surface ozone observations for a limited assimilation interval of 6 hours and (2) by starting with poorly chosen first guess values. In this first such application to a three-dimensional chemistry transport model, success was also achieved in analyzing not only observed but also chemically closely related unobserved constituents.

  1. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

    Directory of Open Access Journals (Sweden)

    R. Q. Thomas

    2017-07-01

    Full Text Available Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2 concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6  ×  105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field

  2. Decadal changes in global surface NO

    NARCIS (Netherlands)

    Miyazaki, Kazuyuki; Eskes, Henk; Sudo, Kengo; Boersma, Folkert; Bowman, Kevin; Kanaya, Yugo

    2017-01-01

    Global surface emissions of nitrogen oxides (NOx ) over a 10-year period (2005-2014) are estimated from an assimilation of multiple satellite data sets: tropospheric NO2 columns from Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment-2 (GOME- 2), and

  3. Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation

    Directory of Open Access Journals (Sweden)

    K. Miyazaki

    2017-01-01

    underestimation of soil NOx sources in the emission inventories. Despite the large trends observed for individual regions, the global total emission is almost constant between 2005 (47.9 Tg N yr−1 and 2014 (47.5 Tg N yr−1.

  4. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS and its application of the Data Assimilation Research Testbed (DART in support of aerosol forecasting

    Directory of Open Access Journals (Sweden)

    J. I. Rubin

    2016-03-01

    Full Text Available An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS at 1 × 1°, combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART. The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact

  5. Continuous data assimilation for downscaling large-footprint soil moisture retrievals

    KAUST Repository

    Altaf, M. U.

    2016-09-01

    Soil moisture is a crucial component of the hydrologic cycle, significantly influencing runoff, infiltration, recharge, evaporation and transpiration processes. Models characterizing these processes require soil moisture as an input, either directly or indirectly. Better characterization of the spatial variability of soil moisture leads to better predictions from hydrologic/climate models. In-situ measurements have fine resolution, but become impractical in terms of coverage over large extents. Remotely sensed data have excellent spatial coverage extents, but suffer from poorer spatial and temporal resolution. We present here an innovative approach to downscaling coarse resolution soil moisture data by combining data assimilation and physically based modeling. In this approach, we exploit the features of Continuous Data Assimilation (CDA). A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model’s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (e.g., HYDRUS) are subjected to data assimilation conditioned upon the coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. The large scale features of the model output are constrained to the observations, and as a consequence, the misfit at the fine scale is reduced. The advantage of this approach is that fine resolution soil moisture maps can be generated across large spatial extents, given the coarse resolution data. The data assimilation approach also enables multi-scale data generation which is helpful to match the soil moisture input data to the corresponding modeling scale. Application of this approach is likely in generating fine and intermediate resolution soil

  6. Improving operational flood forecasting through data assimilation

    Science.gov (United States)

    Rakovec, Oldrich; Weerts, Albrecht; Uijlenhoet, Remko; Hazenberg, Pieter; Torfs, Paul

    2010-05-01

    Accurate flood forecasts have been a challenging topic in hydrology for decades. Uncertainty in hydrological forecasts is due to errors in initial state (e.g. forcing errors in historical mode), errors in model structure and parameters and last but not least the errors in model forcings (weather forecasts) during the forecast mode. More accurate flood forecasts can be obtained through data assimilation by merging observations with model simulations. This enables to identify the sources of uncertainties in the flood forecasting system. Our aim is to assess the different sources of error that affect the initial state and to investigate how they propagate through hydrological models with different levels of spatial variation, starting from lumped models. The knowledge thus obtained can then be used in a data assimilation scheme to improve the flood forecasts. This study presents the first results of this framework and focuses on quantifying precipitation errors and its effect on discharge simulations within the Ourthe catchment (1600 km2), which is situated in the Belgian Ardennes and is one of the larger subbasins of the Meuse River. Inside the catchment, hourly rain gauge information from 10 different locations is available over a period of 15 years. Based on these time series, the bootstrap method has been applied to generate precipitation ensembles. These were then used to simulate the catchment's discharges at the outlet. The corresponding streamflow ensembles were further assimilated with observed river discharges to update the model states of lumped hydrological models (R-PDM, HBV) through Residual Resampling. This particle filtering technique is a sequential data assimilation method and takes no prior assumption of the probability density function for the model states, which in contrast to the Ensemble Kalman filter does not have to be Gaussian. Our further research will be aimed at quantifying and reducing the sources of uncertainty that affect the initial

  7. A data assimilation tool for the Pagasitikos Gulf ecosystem dynamics: Methods and benefits

    KAUST Repository

    Korres, Gerasimos

    2012-06-01

    Within the framework of the European INSEA project, an advanced assimilation system has been implemented for the Pagasitikos Gulf ecosystem. The system is based on a multivariate sequential data assimilation scheme that combines satellite ocean sea color (chlorophyll-a) data with the predictions of a three-dimensional coupled physical-biochemical model of the Pagasitikos Gulf ecosystem presented in a companion paper. The hydrodynamics are solved with a very high resolution (1/100°) implementation of the Princeton Ocean Model (POM). This model is nested within a coarser resolution model of the Aegean Sea which is part of the Greek POSEIDON forecasting system. The forecast of the Aegean Sea model, itself nested and initialized from a Mediterranean implementation of POM, is also used to periodically re-initalize the Pagatisikos hydrodynamics model using variational initialization techniques. The ecosystem dynamics of Pagasitikos are tackled with a stand-alone implementation of the European Seas Ecosystem Model (ERSEM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter, in which the error statistics are parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs).The assimilation experiments were performed for year 2003 and additionally for a 9-month period over 2006 during which the physical model was forced with the POSEIDON-ETA 6-hour atmospheric fields. The assimilation system is validated by assessing the relevance of the system in fitting the data, the impact of the assimilation on non-observed biochemical processes and the overall quality of the forecasts. Assimilation of either GlobColour in 2003 or SeaWiFS in 2006 chlorophyll-a data enhances the identification of the ecological state of the Pagasitikos Gulf. Results, however, suggest that subsurface ecological observations are needed to improve the controllability of the ecosystem in the deep layers. © 2011 Elsevier B.V.

  8. Earth Observation of Vegetation Dynamics in Global Drylands

    DEFF Research Database (Denmark)

    Tian, Feng

    Land degradation in global drylands has been a concern related to both the local livelihoods and the changes in terrestrial biosphere, especially in the context of substantial global environmental changes. Earth Observation (EO) provides a unique way to assess the vegetation dynamics over the past...

  9. Data Assimilation in Marine Models

    DEFF Research Database (Denmark)

    Frydendall, Jan

    maximum likelihood framework. These issues are discussed in paper B. The third part of the thesis falls a bit out of the above context is work published in papers C, F. In the first paper, a simple data assimilation scheme was investigated to examine the potential benefits of incorporating a data......This thesis consists of six research papers published or submitted for publication in the period 2006-2009 together with a summary report. The main topics of this thesis are nonlinear data assimilation techniques and estimation in dynamical models. The focus has been on the nonlinear filtering...... techniques for large scale geophysical numerical models and making them feasible to work with in the data assimilation framework. The filtering techniques investigated are all Monte Carlo simulation based. Some very nice features that can be exploited in the Monte Carlo based data assimilation framework from...

  10. Global inter-annual gravity changes from GRACE: Early results

    DEFF Research Database (Denmark)

    Andersen, Ole Baltazar; Hinderer, J.

    2005-01-01

    with an accuracy of 0.4 muGal corresponding to 9 mm water thickness on spatial scales longer than 1300 km. Four of the most widely used global hydrological models have been investigated for their spatial comparison with GRACE observations of inter-annual gravity field variations due to changes in continental water...... storage. The Global Land Data Assimilation System model has a spatial correlation coefficient with GRACE observations of 0.65 over the northern hemisphere. This demonstrates that the observed gravity field changes on these scales are largely related to changes in continental water storage.......Fifteen monthly gravity field solutions from the GRACE twin satellites launched more than two years ago have been studied to estimate gravity field changes between 2002 and 2003. The results demonstrate that GRACE is capable of capturing the changes in ground water on inter-annual scales...

  11. Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

    Directory of Open Access Journals (Sweden)

    M. Wei

    2012-09-01

    Full Text Available Despite the tremendous progress that has been made in data assimilation (DA methodology, observing systems that reduce observation errors, and model improvements that reduce background errors, the analyses produced by the best available DA systems are still different from the truth. Analysis error and error covariance are important since they describe the accuracy of the analyses, and are directly related to the future forecast errors, i.e., the forecast quality. In addition, analysis error covariance is critically important in building an efficient ensemble forecast system (EFS.

    Estimating analysis error covariance in an ensemble-based Kalman filter DA is straightforward, but it is challenging in variational DA systems, which have been in operation at most NWP (Numerical Weather Prediction centers. In this study, we use the Lanczos method in the NCEP (the National Centers for Environmental Prediction Gridpoint Statistical Interpolation (GSI DA system to look into other important aspects and properties of this method that were not exploited before. We apply this method to estimate the observation impact signals (OIS, which are directly related to the analysis error variances. It is found that the smallest eigenvalue of the transformed Hessian matrix converges to one as the number of minimization iterations increases. When more observations are assimilated, the convergence becomes slower and more eigenvectors are needed to retrieve the observation impacts. It is also found that the OIS over data-rich regions can be represented by the eigenvectors with dominant eigenvalues.

    Since only a limited number of eigenvectors can be computed due to computational expense, the OIS is severely underestimated, and the analysis error variance is consequently overestimated. It is found that the mean OIS values for temperature and wind components at typical model levels are increased by about 1.5 times when the number of eigenvectors is doubled

  12. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh

    2015-01-01

    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...... and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data...

  13. Preliminary Evaluation of Influence of Aerosols on the Simulation of Brightness Temperature in the NASA's Goddard Earth Observing System Atmospheric Data Assimilation System

    Science.gov (United States)

    Kim, Jong; Akella, Santha; da Silva, Arlindo M.; Todling, Ricardo; McCarty, William

    2018-01-01

    This document reports on preliminary results obtained when studying the impact of aerosols on the calculation of brightness temperature (BT) for satellite infrared (IR) instruments that are currently assimilated in a 3DVAR configuration of Goddard Earth Observing System (GEOS)-atmospheric data assimilation system (ADAS). A set of fifteen aerosol species simulated by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model is used to evaluate the influence of the aerosol fields on the Community Radiative Transfer Model (CRTM) calculations taking place in the observation operators of the Gridpoint Statistical Interpolation (GSI) analysis system of GEOSADAS. Results indicate that taking aerosols into account in the BT calculation improves the fit to observations over regions with significant amounts of dust. The cooling effect obtained with the aerosol-affected BT leads to a slight warming of the analyzed surface temperature (by about 0:5oK) in the tropical Atlantic ocean (off northwest Africa), whereas the effect on the air temperature aloft is negligible. In addition, this study identifies a few technical issues to be addressed in future work if aerosol-affected BT are to be implemented in reanalysis and operational settings. The computational cost of applying CRTM aerosol absorption and scattering options is too high to justify their use, given the size of the benefits obtained. Furthermore, the differentiation between clouds and aerosols in GSI cloud detection procedures needs satisfactory revision.

  14. Assimilation and subcellular partitioning of elements by grass shrimp collected along an impact gradient

    International Nuclear Information System (INIS)

    Seebaugh, David R.; Wallace, William G.

    2009-01-01

    Chronic exposure to polluted field conditions can impact metal bioavailability in prey and may influence metal transfer to predators. The present study investigated the assimilation of Cd, Hg and organic carbon by grass shrimp Palaemonetes pugio, collected along an impact gradient within the New York/New Jersey Harbor Estuary. Adult shrimp were collected from five Staten Island, New York study sites, fed 109 Cd- or 203 Hg-labeled amphipods or 14 C-labeled meals and analyzed for assimilation efficiencies (AE). Subsamples of amphipods and shrimp were subjected to subcellular fractionation to isolate metal associated with a compartment presumed to contain trophically available metal (TAM) (metal associated with heat-stable proteins [HSP - e.g., metallothionein-like proteins], heat-denatured proteins [HDP - e.g., enzymes] and organelles [ORG]). TAM- 109 Cd% and TAM- 203 Hg% in radiolabeled amphipods were ∼64% and ∼73%, respectively. Gradients in AE- 109 Cd% (∼54% to ∼75%) and AE- 203 Hg% (∼61% to ∼78%) were observed for grass shrimp, with the highest values exhibited by shrimp collected from sites within the heavily polluted Arthur Kill complex. Population differences in AE- 14 C% were not observed. Assimilated 109 Cd% partitioned to the TAM compartment in grass shrimp varied between ∼67% and ∼75%. 109 Cd bound to HSP in shrimp varied between ∼15% and ∼47%, while 109 Cd associated with metal-sensitive HDP was ∼17% to ∼44%. Percentages of assimilated 109 Cd bound to ORG were constant at ∼10%. Assimilated 203 Hg% associated with TAM in grass shrimp did not exhibit significant variation. Percentages of assimilated 203 Hg bound to HDP (∼47%) and ORG (∼11%) did not vary among populations and partitioning of 203 Hg to HSP was not observed. Using a simplified biokinetic model of metal accumulation from the diet, it is estimated that site-specific variability in Cd AE by shrimp and tissue Cd burdens in field-collected prey (polychaetes Nereis spp

  15. Contextualizing the global relevance of local land change observations

    International Nuclear Information System (INIS)

    Magliocca, N R; Ellis, E C; Oates, T; Schmill, M

    2014-01-01

    To understand global changes in the Earth system, scientists must generalize globally from observations made locally and regionally. In land change science (LCS), local field-based observations are costly and time consuming, and generally obtained by researchers working at disparate local and regional case-study sites chosen for different reasons. As a result, global synthesis efforts in LCS tend to be based on non-statistical inferences subject to geographic biases stemming from data limitations and fragmentation. Thus, a fundamental challenge is the production of generalized knowledge that links evidence of the causes and consequences of local land change to global patterns and vice versa. The GLOBE system was designed to meet this challenge. GLOBE aims to transform global change science by enabling new scientific workflows based on statistically robust, globally relevant integration of local and regional observations using an online social-computational and geovisualization system. Consistent with the goals of Digital Earth, GLOBE has the capability to assess the global relevance of local case-study findings within the context of over 50 global biophysical, land-use, climate, and socio-economic datasets. We demonstrate the implementation of one such assessment – a representativeness analysis – with a recently published meta-study of changes in swidden agriculture in tropical forests. The analysis provides a standardized indicator to judge the global representativeness of the trends reported in the meta-study, and a geovisualization is presented that highlights areas for which sampling efforts can be reduced and those in need of further study. GLOBE will enable researchers and institutions to rapidly share, compare, and synthesize local and regional studies within the global context, as well as contributing to the larger goal of creating a Digital Earth

  16. Are changes in sulfate assimilation pathway needed for evolution of C4 photosynthesis?

    Directory of Open Access Journals (Sweden)

    Silke Christine Weckopp

    2015-01-01

    Full Text Available C4 photosynthesis characteristically features a cell-specific localization of enzymes involved in CO2 assimilation in bundle sheath cells or mesophyll cells. Interestingly, enzymes of sulfur assimilation are also specifically present in bundle sheath cells of maize and many other C4 species. This localization, however, could not be confirmed in C4 species of the genus Flaveria. It was, therefore, concluded that the bundle sheath localization of sulfate assimilation occurs only in C4 monocots. However, recently the sulfate assimilation pathway was found coordinately enriched in bundle sheath cells of Arabidopsis, opening new questions about the significance of such cell-specific localization of the pathway. In addition, next generation sequencing revealed expression gradients of many genes from C3 to C4 species and mathematical modelling proposed a sequence of adaptations during the evolutionary path from C3 to C4. Indeed, such gradient, with higher expression of genes for sulfate reduction in C4 species, has been observed within the genus Flaveria. These new tools provide the basis for reexamining the intriguing question of compartmentalization of sulfur assimilation. Therefore, this review summarizes the findings on spatial separation of sulfur assimilation in C4 plants and Arabidopsis, assesses the information on sulfur assimilation provided by the recent transcriptomics data and discusses their possible impact on understanding this interesting feature of plant sulfur metabolism to find out whether changes in sulfate assimilation are part of a general evolutionary trajectory towards C4 photosynthesis.

  17. Data Assimilation - Advances and Applications

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Brian J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2014-07-30

    This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batch sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.

  18. Application of Bred Vectors To Data Assimilation

    Science.gov (United States)

    Corazza, M.; Kalnay, E.; Patil, Dj

    We introduced a statistic, the BV-dimension, to measure the effective local finite-time dimensionality of the atmosphere. We show that this dimension is often quite low, and suggest that this finding has important implications for data assimilation and the accuracy of weather forecasting (Patil et al, 2001). The original database for this study was the forecasts of the NCEP global ensemble forecasting system. The initial differences between the control forecast and the per- turbed forecasts are called bred vectors. The control and perturbed initial conditions valid at time t=n(t are evolved using the forecast model until time t=(n+1) (t. The differences between the perturbed and the control forecasts are scaled down to their initial amplitude, and constitute the bred vectors valid at (n+1) (t. Their growth rate is typically about 1.5/day. The bred vectors are similar by construction to leading Lya- punov vectors except that they have small but finite amplitude, and they are valid at finite times. The original NCEP ensemble data set has 5 independent bred vectors. We define a local bred vector at each grid point by choosing the 5 by 5 grid points centered at the grid point (a region of about 1100km by 1100km), and using the north-south and east- west velocity components at 500mb pressure level to form a 50 dimensional column vector. Since we have k=5 global bred vectors, we also have k local bred vectors at each grid point. We estimate the effective dimensionality of the subspace spanned by the local bred vectors by performing a singular value decomposition (EOF analysis). The k local bred vector columns form a 50xk matrix M. The singular values s(i) of M measure the extent to which the k column unit vectors making up the matrix M point in the direction of v(i). We define the bred vector dimension as BVDIM={Sum[s(i)]}^2/{Sum[s(i)]^2} For example, if 4 out of the 5 vectors lie along v, and one lies along v, the BV- dimension would be BVDIM[sqrt(4), 1, 0

  19. Global Energy and Water Cycle Experiment (GEWEX) and the Continental-scale International Project (GCIP)

    Science.gov (United States)

    Vane, Deborah

    1993-01-01

    A discussion of the objectives of the Global Energy and Water Cycle Experiment (GEWEX) and the Continental-scale International Project (GCIP) is presented in vugraph form. The objectives of GEWEX are as follows: determine the hydrological cycle by global measurements; model the global hydrological cycle; improve observations and data assimilation; and predict response to environmental change. The objectives of GCIP are as follows: determine the time/space variability of the hydrological cycle over a continental-scale region; develop macro-scale hydrologic models that are coupled to atmospheric models; develop information retrieval schemes; and support regional climate change impact assessment.

  20. The Global Geodetic Observing System: Recent Activities and Accomplishments

    Science.gov (United States)

    Gross, R. S.

    2017-12-01

    The Global Geodetic Observing System (GGOS) of the International Association of Geodesy (IAG) provides the basis on which future advances in geosciences can be built. By considering the Earth system as a whole (including the geosphere, hydrosphere, cryosphere, atmosphere and biosphere), monitoring Earth system components and their interactions by geodetic techniques and studying them from the geodetic point of view, the geodetic community provides the global geosciences community with a powerful tool consisting mainly of high-quality services, standards and references, and theoretical and observational innovations. The mission of GGOS is: (a) to provide the observations needed to monitor, map and understand changes in the Earth's shape, rotation and mass distribution; (b) to provide the global frame of reference that is the fundamental backbone for measuring and consistently interpreting key global change processes and for many other scientific and societal applications; and (c) to benefit science and society by providing the foundation upon which advances in Earth and planetary system science and applications are built. The goals of GGOS are: (1) to be the primary source for all global geodetic information and expertise serving society and Earth system science; (2) to actively promote, sustain, improve, and evolve the integrated global geodetic infrastructure needed to meet Earth science and societal requirements; (3) to coordinate with the international geodetic services that are the main source of key parameters and products needed to realize a stable global frame of reference and to observe and study changes in the dynamic Earth system; (4) to communicate and advocate the benefits of GGOS to user communities, policy makers, funding organizations, and society. In order to accomplish its mission and goals, GGOS depends on the IAG Services, Commissions, and Inter-Commission Committees. The Services provide the infrastructure and products on which all contributions

  1. Variational data assimilation problem for the thermodynamics model with displaced pole

    Science.gov (United States)

    Parmuzin, Eugene; Agosgkov, Valery; Zakharova, Natalia

    2017-04-01

    The most versatile and promising technology for solving problems of monitoring and analysis of the natural environment is a four-dimensional variational data assimilation of observation data. The development of computational algorithms for the solution of data assimilation problems in geophysical hydrodynamics is important in the contemporary computation and informational science to improve the quality of long-term prediction by using the hydrodynamics sea model. These problems are applied to close and solve in practice the appropriate inverse problems of the geophysical hydrodynamics. In this work the variational data assimilation problems in the Baltic Sea water area with displaced pole were formulated and studied [1]. We assume, that the unique function which is obtained by observation data processing is the function and we permit that the function is known only on a part of considering area (for example, on a part of the Baltic Sea). Numerical experiments on restoring the ocean heat flux and obtaining solution of the system (temperature, salinity, velocity, and sea surface height) in the Baltic Sea primitive equation hydrodynamics model [2] with assimilation procedure were carried out. In the calculations we used daily sea surface temperature observation from Danish meteorological Institute, prepared on the basis of measurements of the radiometer (AVHRR, AATSR and AMSRE) and spectroradiometer (SEVIRI and MODIS). The spatial resolution of the model grid with respect to the horizontal variables is uniform on latitude (0.2 degree) and varies on longitude from 0.04 to 0.0004 degree . The results of the numerical experiments are presented. This study was supported by the Russian Foundation for Basic Research (project №16-01-00548) and project №14-11-00609 by the Russian Science Foundation. References: [1] Agoshkov V.I., Parmuzin E.I., Zakharova N.B., Zalesny V.B., Shutyaev V.P., Gusev A.V. Variational assimilation of observation data in the mathematical model of

  2. Global Warming: Evidence from Satellite Observations

    Science.gov (United States)

    Prabhakara, C.; Iacovazzi, R., Jr.; Yoo, J.-M.

    2001-01-01

    Observations made in Channel 2 (53.74 GHz) of the Microwave Sounding Unit (MSU) radiometer, flown on-board sequential, sun-synchronous, polar orbiting NOAA operational satellites, indicate that the mean temperature of the atmosphere over the globe increased during the period 1980 to 1999. In this study we have minimized systematic errors in the time series introduced by the satellite orbital drift in an objective manner. This is done with the help the onboard warm black body temperature, which is used in the calibration of the MSU radiometer. The corrected MSU Channel 2 observations of the NOAA satellite series reveal that the vertically weighted global mean temperature of the atmosphere, with a peak weight near the mid-troposphere, warmed at the rate of 0.13 K per decade (with an uncertainty of 0.05 K per decade) during 1980 to 1999. The global warming deduced from conventional meteorological data that have been corrected for urbanization effects agrees reasonably with this satellite deuced result.

  3. Global Night-Time Lights for Observing Human Activity

    Science.gov (United States)

    Hipskind, Stephen R.; Elvidge, Chris; Gurney, K.; Imhoff, Mark; Bounoua, Lahouari; Sheffner, Edwin; Nemani, Ramakrishna R.; Pettit, Donald R.; Fischer, Marc

    2011-01-01

    We present a concept for a small satellite mission to make systematic, global observations of night-time lights with spatial resolution suitable for discerning the extent, type and density of human settlements. The observations will also allow better understanding of fine scale fossil fuel CO2 emission distribution. The NASA Earth Science Decadal Survey recommends more focus on direct observations of human influence on the Earth system. The most dramatic and compelling observations of human presence on the Earth are the night light observations taken by the Defence Meteorological System Program (DMSP) Operational Linescan System (OLS). Beyond delineating the footprint of human presence, night light data, when assembled and evaluated with complementary data sets, can determine the fine scale spatial distribution of global fossil fuel CO2 emissions. Understanding fossil fuel carbon emissions is critical to understanding the entire carbon cycle, and especially the carbon exchange between terrestrial and oceanic systems.

  4. Investigation of glycerol assimilation and cofactor metabolism in Lactococcus lactis

    DEFF Research Database (Denmark)

    Holm, Anders Koefoed

    of glycerol kinase from L. lactis, introduction of a heterologous glycerol assimilation pathway and construction of a library of NADH oxidase activity. Based on a preliminary analysis of transcription level data, an attempt was made to stimulate glycerol assimilation by overexpressing the glycerol kinase...... already present in L. lactis. The construction and verification of a strain with increased glycerol kinase activity was not fully completed and is still ongoing. Similarly the construction of mutants expressing a heterologous pathway for glycerol dissimilation is also an ongoing task. An artificial...... effects and improve the growth rate, though not completely to the level of the reference strain. The fact that this effect was predominantly observed while utilizing xylose implicates the involvement of the pentose phosphate pathway. A possible mechanism underlying the observed growth characteristics...

  5. Sharing Data in the Global Ocean Observing System (Invited)

    Science.gov (United States)

    Lindstrom, E. J.; McCurdy, A.; Young, J.; Fischer, A. S.

    2010-12-01

    We examine the evolution of data sharing in the field of physical oceanography to highlight the challenges now before us. Synoptic global observation of the ocean from space and in situ platforms has significantly matured over the last two decades. In the early 1990’s the community data sharing challenges facing the World Ocean Circulation Experiment (WOCE) largely focused on the behavior of individual scientists. Satellite data sharing depended on the policy of individual agencies. Global data sets were delivered with considerable delay and with enormous personal sacrifice. In the 2000’s the requirements for global data sets and sustained observations from the likes of the U.N. Framework Convention on Climate Change have led to data sharing and cooperation at a grander level. It is more effective and certainly more efficient. The Joint WMO/IOC Technical Commission on Oceanography and Marine Meteorology (JCOMM) provided the means to organize many aspects of data collection and data dissemination globally, for the common good. In response the Committee on Earth Observing Satellites organized Virtual Constellations to enable the assembly and sharing of like kinds of satellite data (e.g., sea surface topography, ocean vector winds, and ocean color). Individuals in physical oceanography have largely adapted to the new rigors of sharing data for the common good, and as a result of this revolution new science has been enabled. Primary obstacles to sharing have shifted from the individual level to the national level. As we enter into the 2010’s the demands for ocean data continue to evolve with an expanded requirement for more real-time reporting and broader disciplinary coverage, to answer key scientific and societal questions. We are also seeing the development of more numerous national contributions to the global observing system. The drivers for the establishment of global ocean observing systems are expanding beyond climate to include biological and

  6. Economic Assimilation and Outmigration of Immigrants in West-Germany

    NARCIS (Netherlands)

    Bellemare, C.

    2003-01-01

    By analyzing earnings of observed immigrants workers, the literature on the economic assimilation of immigrants has generally overlooked two potentially important selectivity issues.First, earnings of immigrant workers may di¿er substantially from those of non-workers.Second, earnings of immigrants

  7. Unscented Kalman filter assimilation of time-lapse self-potential data for monitoring solute transport

    Science.gov (United States)

    Cui, Yi-an; Liu, Lanbo; Zhu, Xiaoxiong

    2017-08-01

    Monitoring the extent and evolution of contaminant plumes in local and regional groundwater systems from existing landfills is critical in contamination control and remediation. The self-potential survey is an efficient and economical nondestructive geophysical technique that can be used to investigate underground contaminant plumes. Based on the unscented transform, we have built a Kalman filtering cycle to conduct time-lapse data assimilation for monitoring the transport of solute based on the solute transport experiment using a bench-scale physical model. The data assimilation was formed by modeling the evolution based on the random walk model and observation correcting based on the self-potential forward. Thus, monitoring self-potential data can be inverted by the data assimilation technique. As a result, we can reconstruct the dynamic process of the contaminant plume instead of using traditional frame-to-frame static inversion, which may cause inversion artifacts. The data assimilation inversion algorithm was evaluated through noise-added synthetic time-lapse self-potential data. The result of the numerical experiment shows validity, accuracy and tolerance to the noise of the dynamic inversion. To validate the proposed algorithm, we conducted a scaled-down sandbox self-potential observation experiment to generate time-lapse data that closely mimics the real-world contaminant monitoring setup. The results of physical experiments support the idea that the data assimilation method is a potentially useful approach for characterizing the transport of contamination plumes using the unscented Kalman filter (UKF) data assimilation technique applied to field time-lapse self-potential data.

  8. Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

    Science.gov (United States)

    Borovikov, Anna; Rienecker, Michele M.; Keppenne, Christian; Johnson, Gregory C.

    2004-01-01

    One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the

  9. ERP ASSIMILATION: AN END-USER APPROACH

    Directory of Open Access Journals (Sweden)

    Hurbean Luminita

    2013-07-01

    The paper discusses the ERP adoption based on the IT assimilation theory. The ERP lifecycle is associated with the IT assimilation steps. We propose a distribution of these steps along the lifecycle. Derived from the findings in the reviewed literature we will focus the cultural factors, in particular those related to the end-users (determined as a major impact factor in our previous study: Negovan et al., 2011. Our empirical study is centred on the end-users perspective and it tries to determine if and how their behaviour affects the achievement of the ERP assimilation steps. The paper reasons that organizations that understand the IT assimilation steps correlated to the ERP implementation critical factors are more likely to implement and use ERP successfully.

  10. An integrated GIS application system for soil moisture data assimilation

    Science.gov (United States)

    Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

    2014-11-01

    The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

  11. Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea

    Directory of Open Access Journals (Sweden)

    D. E. Kaufman

    2018-01-01

    Full Text Available The Ross Sea is a region characterized by high primary productivity in comparison to other Antarctic coastal regions, and its productivity is marked by considerable variability both spatially (1–50 km and temporally (days to weeks. This variability presents a challenge for inferring phytoplankton dynamics from observations that are limited in time or space, which is often the case due to logistical limitations of sampling. To better understand the spatiotemporal variability in Ross Sea phytoplankton dynamics and to determine how restricted sampling may skew dynamical interpretations, high-resolution bio-optical glider measurements were assimilated into a one-dimensional biogeochemical model adapted for the Ross Sea. The assimilation of data from the entire glider track using the micro-genetic and local search algorithms in the Marine Model Optimization Testbed improves the model–data fit by  ∼ 50 %, generating rates of integrated primary production of 104 g C m−2 yr−1 and export at 200 m of 27 g C m−2 yr−1. Assimilating glider data from three different latitudinal bands and three different longitudinal bands results in minimal changes to the simulations, improves the model–data fit with respect to unassimilated data by  ∼ 35 %, and confirms that analyzing these glider observations as a time series via a one-dimensional model is reasonable on these scales. Whereas assimilating the full glider data set produces well-constrained simulations, assimilating subsampled glider data at a frequency consistent with cruise-based sampling results in a wide range of primary production and export estimates. These estimates depend strongly on the timing of the assimilated observations, due to the presence of high mesoscale variability in this region. Assimilating surface glider data subsampled at a frequency consistent with available satellite-derived data results in 40 % lower carbon export, primarily

  12. An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods

    Directory of Open Access Journals (Sweden)

    Yonghan Choi

    2014-01-01

    Full Text Available An adjoint sensitivity-based data assimilation (ASDA method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system (MCS related to the heavy rainfall was classified as training line/adjoining stratiform (TL/AS-type for the earlier period, and back building (BB-type for the later period. In the ASDA method, an adjoint model is run backwards with forecast-error gradient as input, and the adjoint sensitivity of the forecast error to the initial condition is scaled by an optimal scaling factor. The optimal scaling factor is determined by minimising the observational cost function of the four-dimensional variational (4D-Var method, and the scaled sensitivity is added to the original first guess. Finally, the observations at the analysis time are assimilated using a 3D-Var method with the improved first guess. The simulated rainfall distribution is shifted northeastward compared to the observations when no radar data are assimilated or when radar data are assimilated using the 3D-Var method. The rainfall forecasts are improved when radar data are assimilated using the 4D-Var or ASDA method. Simulated atmospheric fields such as horizontal winds, temperature, and water vapour mixing ratio are also improved via the 4D-Var or ASDA method. Due to the improvement in the analysis, subsequent forecasts appropriately simulate the observed features of the TL/AS- and BB-type MCSs and the corresponding heavy rainfall. The computational cost associated with the ASDA method is significantly lower than that of the 4D-Var method.

  13. Chemical data assimilation of geostationary aerosol optical depth and PM surface observations on regional aerosol modeling over the Korean Peninsula during KORUS-AQ campaign

    Science.gov (United States)

    Jung, J.; Choi, Y.; Souri, A.; Jeon, W.

    2017-12-01

    Particle matter(PM) has played a significantly deleterious role in affecting human health and climate. Recently, continuous high concentrations of PM in Korea attracted public attention to this critical issue, and the Korea-United States Air Quality Study(KORUS-AQ) campaign in 2016 was conducted to investigate the causes. For this study, we adjusted the initial conditions in the chemical transport model(CTM) to improve its performance over Korean Peninsula during KORUS-AQ period, using the campaign data to evaluate our model performance. We used the Optimal Interpolation(OI) approach and used hourly surface air quality measurement data from the Air Quality Monitoring Station(AQMS) by NIER and the aerosol optical depth(AOD) measured by a GOCI sensor from the geostationary orbit onboard the Communication Ocean and Meteorological Satellite(COMS). The AOD at 550nm has a 6km spatial resolution and broad coverage over East Asia. After assimilating the surface air quality observation data, the model accuracy significantly improved compared to base model result (without assimilation). It reported very high correlation value (0.98) and considerably decreased mean bias. Especially, it well captured some high peaks which was underpredicted by the base model. To assimilate satellite data, we applied AOD scaling factors to quantify each specie's contribution to total PM concentration and find-mode fraction(FMF) to define vertical distribution. Finally, the improvement showed fairly good agreement.

  14. [Simulation of water and carbon fluxes in harvard forest area based on data assimilation method].

    Science.gov (United States)

    Zhang, Ting-Long; Sun, Rui; Zhang, Rong-Hua; Zhang, Lei

    2013-10-01

    Model simulation and in situ observation are the two most important means in studying the water and carbon cycles of terrestrial ecosystems, but have their own advantages and shortcomings. To combine these two means would help to reflect the dynamic changes of ecosystem water and carbon fluxes more accurately. Data assimilation provides an effective way to integrate the model simulation and in situ observation. Based on the observation data from the Harvard Forest Environmental Monitoring Site (EMS), and by using ensemble Kalman Filter algorithm, this paper assimilated the field measured LAI and remote sensing LAI into the Biome-BGC model to simulate the water and carbon fluxes in Harvard forest area. As compared with the original model simulated without data assimilation, the improved Biome-BGC model with the assimilation of the field measured LAI in 1998, 1999, and 2006 increased the coefficient of determination R2 between model simulation and flux observation for the net ecosystem exchange (NEE) and evapotranspiration by 8.4% and 10.6%, decreased the sum of absolute error (SAE) and root mean square error (RMSE) of NEE by 17.7% and 21.2%, and decreased the SAE and RMSE of the evapotranspiration by 26. 8% and 28.3%, respectively. After assimilated the MODIS LAI products of 2000-2004 into the improved Biome-BGC model, the R2 between simulated and observed results of NEE and evapotranspiration was increased by 7.8% and 4.7%, the SAE and RMSE of NEE were decreased by 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration were decreased by 24.5% and 25.5%, respectively. It was suggested that the simulation accuracy of ecosystem water and carbon fluxes could be effectively improved if the field measured LAI or remote sensing LAI was integrated into the model.

  15. Global measures of ionospheric electrodynamic activity inferred from combined incoherent scatter radar and ground magnetometer observations

    International Nuclear Information System (INIS)

    Richmond, A.D.; Kamide, Y.; Akasofu, S.I.; Alcayde, D.; Blanc, M.; De LaBeaujardiere, O.; Evans, D.S.; Foster, J.C.; Holt, J.M.; Friis-Christensen, E.; Pellinen, R.J.; Senior, C.; Zaitzev, A.N.

    1990-01-01

    An analysis of several global measures of high-latitude ionospheric electrodynamic activity is undertakn on the basis of results obtained from the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) procedure applied to incoherent scatter radar and ground magnetometer observatons for January 18-19, 1984. Different global measures of electric potentials, currents, resistances, and energy transfer from the magnetosphere show temporal variations that are generally well correlated. The authors present parameterizations of thees quantities in terms of the AE index and the hemispheric power index of precipitating auroral particles. It is shown how error estimates of the mapped electric fields can be used to correct the estimation of Joule heating. Global measures of potential drop, field-aligned current, and Joule heating as obtained by the AMIE procedure are compared with similar measures presented in previous studies. Agreement is found to within the uncertainties inherent in each study. The mean potential drop through which field-aligned currents flow in closing through the ionosphere is approximately 28% of the total polar cap potential drop under all conditions during these 2 days. They note that order-of-magnitude differences can appear when comparing different global measures of total electric current flow and of effective resistances of the global circuit, so that care must be exercised in choosing characteristic values of these parameters for circuit-analogy studies of ionosphere-magnetosphere electrodynamic coupling

  16. A Study of the Carbon Cycle Using NASA Observations and the GEOS Model

    Science.gov (United States)

    Pawson, Steven; Gelaro, Ron; Ott, Lesley; Putman, Bill; Chatterjee, Abhishek; Koster, Randy; Lee, Eunjee; Oda, Tom; Weir, Brad; Zeng, Fanwei

    2018-01-01

    The Goddard Earth Observing System (GEOS) model has been developed in the Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center. From its roots in chemical transport and as a General Circulation Model, the GEOS model has been extended to an Earth System Model based on a modular construction using the Earth System Modeling Framework (ESMF), combining elements developed in house in the GMAO with others that are imported through collaborative research. It is used extensively for research and for product generation, both as a free-running model and as the core of the GMAO's data assimilation system. In recent years, the GMAO's modeling and assimilation efforts have been strongly supported by Piers Sellers, building on both his earlier legacy as an observationally oriented model developer and his post-astronaut career as a dynamic leader into new territory. Piers' long-standing interest in the carbon cycle and the combination of models with observations motivates this presentation, which will focus on the representation of the carbon cycle in the GEOS Earth System Model. Examples will include: (i) the progression from specified land-atmosphere surface fluxes to computations with an interactive model component (Catchment-CN), along with constraints on vegetation distributions using satellite observations; (ii) the use of high-resolution satellite observations to constrain human-generated inputs to the atmosphere; (iii) studies of the consistency of the observed atmospheric carbon dioxide concentrations with those in the model simulations. The presentation will focus on year-to-year variations in elements of the carbon cycle, specifically on how the observations can inform the representation of mechanisms in the model and lead to integrity in global carbon dioxide simulations. Further, applications of the GEOS model to the planning of new carbon-climate observations will be addressed, as an example of the work that was strongly supported by

  17. A statistical data assimilation method for seasonal streamflow forecasting to optimize hydropower reservoir management in data-scarce regions

    Science.gov (United States)

    Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.

    2017-12-01

    Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was

  18. Enhanced Soil Moisture Initialization Using Blended Soil Moisture Product and Regional Optimization of LSM-RTM Coupled Land Data Assimilation System.

    Science.gov (United States)

    Nair, A. S.; Indu, J.

    2017-12-01

    Prediction of soil moisture dynamics is high priority research challenge because of the complex land-atmosphere interaction processes. Soil moisture (SM) plays a decisive role in governing water and energy balance of the terrestrial system. An accurate SM estimate is imperative for hydrological and weather prediction models. Though SM estimates are available from microwave remote sensing and land surface model (LSM) simulations, it is affected by uncertainties from several sources during estimation. Past studies have generally focused on land data assimilation (DA) for improving LSM predictions by assimilating soil moisture from single satellite sensor. This approach is limited by the large time gap between two consequent soil moisture observations due to satellite repeat cycle of more than three days at the equator. To overcome this, in the present study, we have performed DA using ensemble products from the soil moisture operational product system (SMOPS) blended soil moisture retrievals from different satellite sensors into Noah LSM. Before the assimilation period, the Noah LSM is initialized by cycling through seven multiple loops from 2008 to 2010 forcing with Global data assimilation system (GDAS) data over the Indian subcontinent. We assimilated SMOPS into Noah LSM for a period of two years from 2010 to 2011 using Ensemble Kalman Filter within NASA's land information system (LIS) framework. Results show that DA has improved Noah LSM prediction with a high correlation of 0.96 and low root mean square difference of 0.0303 m3/m3 (figure 1a). Further, this study has also investigated the notion of assimilating microwave brightness temperature (Tb) as a proxy for SM estimates owing to the close proximity of Tb and SM. Preliminary sensitivity analysis show a strong need for regional parameterization of radiative transfer models (RTMs) to improve Tb simulation. Towards this goal, we have optimized the forward RTM using swarm optimization technique for direct Tb

  19. Motion estimation by data assimilation in reduced dynamic models

    International Nuclear Information System (INIS)

    Drifi, Karim

    2013-01-01

    Motion estimation is a major challenge in the field of image sequence analysis. This thesis is a study of the dynamics of geophysical flows visualized by satellite imagery. Satellite image sequences are currently underused for the task of motion estimation. A good understanding of geophysical flows allows a better analysis and forecast of phenomena in domains such as oceanography and meteorology. Data assimilation provides an excellent framework for achieving a compromise between heterogeneous data, especially numerical models and observations. Hence, in this thesis we set out to apply variational data assimilation methods to estimate motion on image sequences. As one of the major drawbacks of applying these assimilation techniques is the considerable computation time and memory required, we therefore define and use a model reduction method in order to significantly decrease the necessary computation time and the memory. We then explore the possibilities that reduced models provide for motion estimation, particularly the possibility of strictly imposing some known constraints on the computed solutions. In particular, we show how to estimate a divergence free motion with boundary conditions on a complex spatial domain [fr

  20. A two-update ensemble Kalman filter for land hydrological data assimilation with an uncertain constraint

    Science.gov (United States)

    Khaki, M.; Ait-El-Fquih, B.; Hoteit, I.; Forootan, E.; Awange, J.; Kuhn, M.

    2017-12-01

    Assimilating Gravity Recovery And Climate Experiment (GRACE) data into land hydrological models provides a valuable opportunity to improve the models' forecasts and increases our knowledge of terrestrial water storages (TWS). The assimilation, however, may harm the consistency between hydrological water fluxes, namely precipitation, evaporation, discharge, and water storage changes. To address this issue, we propose a weak constrained ensemble Kalman filter (WCEnKF) that maintains estimated water budgets in balance with other water fluxes. Therefore, in this study, GRACE terrestrial water storages data are assimilated into the World-Wide Water Resources Assessment (W3RA) hydrological model over the Earth's land areas covering 2002-2012. Multi-mission remotely sensed precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM) and evaporation products from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as ground-based water discharge measurements are applied to close the water balance equation. The proposed WCEnKF contains two update steps; first, it incorporates observations from GRACE to improve model simulations of water storages, and second, uses the additional observations of precipitation, evaporation, and water discharge to establish the water budget closure. These steps are designed to account for error information associated with the included observation sets during the assimilation process. In order to evaluate the assimilation results, in addition to monitoring the water budget closure errors, in situ groundwater measurements over the Mississippi River Basin in the US and the Murray-Darling Basin in Australia are used. Our results indicate approximately 24% improvement in the WCEnKF groundwater estimates over both basins compared to the use of (constraint-free) EnKF. WCEnKF also further reduces imbalance errors by approximately 82.53% (on average) and at the same time increases the correlations between the

  1. A Two-update Ensemble Kalman Filter for Land Hydrological Data Assimilation with an Uncertain Constraint

    KAUST Repository

    Khaki, M.

    2017-10-25

    Assimilating Gravity Recovery And Climate Experiment (GRACE) data into land hydrological models provides a valuable opportunity to improve the models’ forecasts and increases our knowledge of terrestrial water storages (TWS). The assimilation, however, may harm the consistency between hydrological water fluxes, namely precipitation, evaporation, discharge, and water storage changes. To address this issue, we propose a weak constrained ensemble Kalman filter (WCEnKF) that maintains estimated water budgets in balance with other water fluxes. Therefore, in this study, GRACE terrestrial water storages data are assimilated into the World-Wide Water Resources Assessment (W3RA) hydrological model over the Earth’s land areas covering 2002 – 2012. Multi-mission remotely sensed precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM) and evaporation products from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as ground-based water discharge measurements are applied to close the water balance equation. The proposed WCEnKF contains two update steps; first, it incorporates observations from GRACE to improve model simulations of water storages, and second, uses the additional observations of precipitation, evaporation, and water discharge to establish the water budget closure. These steps are designed to account for error information associated with the included observation sets during the assimilation process. In order to evaluate the assimilation results, in addition to monitoring the water budget closure errors, in-situ groundwater measurements over the Mississippi River Basin in the US and the Murray-Darling Basin in Australia are used. Our results indicate approximately 24% improvement in the WCEnKF groundwater estimates over both basins compared to the use of (constraint-free) EnKF. WCEnKF also further reduces imbalance errors by approximately 82.53% (on average) and at the same time increases the correlations between the

  2. Nonlinear error dynamics for cycled data assimilation methods

    International Nuclear Information System (INIS)

    Moodey, Alexander J F; Lawless, Amos S; Potthast, Roland W E; Van Leeuwen, Peter Jan

    2013-01-01

    We investigate the error dynamics for cycled data assimilation systems, such that the inverse problem of state determination is solved at t k , k = 1, 2, 3, …, with a first guess given by the state propagated via a dynamical system model M k from time t k−1 to time t k . In particular, for nonlinear dynamical systems M k that are Lipschitz continuous with respect to their initial states, we provide deterministic estimates for the development of the error ‖e k ‖ ≔ ‖x (a) k − x (t) k ‖ between the estimated state x (a) and the true state x (t) over time. Clearly, observation error of size δ > 0 leads to an estimation error in every assimilation step. These errors can accumulate, if they are not (a) controlled in the reconstruction and (b) damped by the dynamical system M k under consideration. A data assimilation method is called stable, if the error in the estimate is bounded in time by some constant C. The key task of this work is to provide estimates for the error ‖e k ‖, depending on the size δ of the observation error, the reconstruction operator R α , the observation operator H and the Lipschitz constants K (1) and K (2) on the lower and higher modes of M k controlling the damping behaviour of the dynamics. We show that systems can be stabilized by choosing α sufficiently small, but the bound C will then depend on the data error δ in the form c‖R α ‖δ with some constant c. Since ‖R α ‖ → ∞ for α → 0, the constant might be large. Numerical examples for this behaviour in the nonlinear case are provided using a (low-dimensional) Lorenz ‘63 system. (paper)

  3. Dark matter assimilation into the baryon asymmetry

    International Nuclear Information System (INIS)

    D'Eramo, Francesco; Fei, Lin; Thaler, Jesse

    2012-01-01

    Pure singlets are typically disfavored as dark matter candidates, since they generically have a thermal relic abundance larger than the observed value. In this paper, we propose a new dark matter mechanism called a ssimilation , which takes advantage of the baryon asymmetry of the universe to generate the correct relic abundance of singlet dark matter. Through assimilation, dark matter itself is efficiently destroyed, but dark matter number is stored in new quasi-stable heavy states which carry the baryon asymmetry. The subsequent annihilation and late-time decay of these heavy states yields (symmetric) dark matter as well as (asymmetric) standard model baryons. We study in detail the case of pure bino dark matter by augmenting the minimal supersymmetric standard model with vector-like chiral multiplets. In the parameter range where this mechanism is effective, the LHC can discover long-lived charged particles which were responsible for assimilating dark matter

  4. Annual global tree cover estimated by fusing optical and SAR satellite observations

    Science.gov (United States)

    Feng, M.; Sexton, J. O.; Channan, S.; Townshend, J. R.

    2017-12-01

    Tree cover defined structurally as the proportional, vertically projected area of vegetation (including leaves, stems, branches, etc.) of woody plants above a given height affects terrestrial energy and water exchanges, photosynthesis and transpiration, net primary production, and carbon and nutrient fluxes. Tree cover provides a measurable attribute upon which forest cover may be defined. Changes in tree cover over time can be used to monitor and retrieve site-specific histories of forest disturbance, succession, and degradation. Measurements of Earth's tree cover have been produced at regional, national, and global extents. However, most representations are static, and those for which multiple time periods have been produced are neither intended nor adequate for consistent, long-term monitoring. Moreover, although a substantial proportion of change has been shown to occur at resolutions below 250 m, existing long-term, Landsat-resolution datasets are either produced as static layers or with annual, five- or ten-year temporal resolution. We have developed an algorithms to retrieve seamless and consistent, sub-hectare resolution estimates of tree-canopy from optical and radar satellite data sources (e.g., Landsat, Sentinel-2, and ALOS-PALSAR). Our approach to estimation enables assimilation of multiple data sources and produces estimates of both cover and its uncertainty at the scale of pixels. It has generated the world's first Landsat-based percent tree cover dataset in 2013. Our previous algorithms are being adapted to produce prototype percent-tree and water-cover layers globally in 2000, 2005, and 2010—as well as annually over North and South America from 2010 to 2015—from passive-optical (Landsat and Sentinel-2) and SAR measurements. Generating a global, annual dataset is beyond the scope of this support; however, North and South America represent all of the world's major biomes and so offer the complete global range of environmental sources of error and

  5. Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

    Directory of Open Access Journals (Sweden)

    A. L. Barbu

    2014-01-01

    Full Text Available The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM and leaf area index (LAI. This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs land surface model within the the externalised surface model (SURFEX modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function matching technique. A multivariate multi-scale land data assimilation system (LDAS based on the extended Kalman Filter (EKF is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011. The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil

  6. Single-column data assimilation for the Atmospheric Radiation Measurement (ARM) Program

    International Nuclear Information System (INIS)

    Louis, J.F.

    1994-01-01

    The main purpose of the ARM program is to provide the necessary data to develop, test and validate the parameterization of clouds and of their interactions with the radiation field, and the computation of radiative transfer in climate models. For various reasons, much of the ARM observations will be imperfect, incomplete, redundant, indirect and unrepresentative. Various techniques of data assimilation have been developed to deal with these problems. The variational data assimilation and adjoint method applied to a single column model is described here. A model is used to simulate the evolution of the atmosphere during an assimilation period. As the model is run, a cost function is computed which is essentially a measure of simulation errors. The method then consists in adjusting some model parameters to minimize the cost function. Optimization of the model parameters needs to be done with a much longer series of data, to cover different meteorological situations. Once parameters are set, nudging terms are used as control variables. The Derber nudging method will require considerable tuning, especially in defining the vertical profiles of the nudging terms. Extensive tests are currently underway of both model optimization and data assimilation

  7. Regional Ocean Data Assimilation

    KAUST Repository

    Edwards, Christopher A.

    2015-01-03

    This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

  8. Assimilation of radar altimeter data in numerical wave models: an impact study in two different wave climate regions

    Directory of Open Access Journals (Sweden)

    G. Emmanouil

    2007-03-01

    Full Text Available An operational assimilation system incorporating significant wave height observations in high resolution numerical wave models is studied and evaluated. In particular, altimeter satellite data provided by the European Space Agency (ESA-ENVISAT are assimilated in the wave model WAM which operates in two different wave climate areas: the Mediterranean Sea and the Indian Ocean. The first is a wind-sea dominated area while in the second, swell is the principal part of the sea state, a fact that seriously affects the performance of the assimilation scheme. A detailed study of the different impact is presented and the resulting forecasts are evaluated against available buoy and satellite observations. The corresponding results show a considerable improvement in wave forecasting for the Indian Ocean while in the Mediterranean Sea the assimilation impact is restricted to isolated areas.

  9. Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation

    Directory of Open Access Journals (Sweden)

    Min Yan

    2016-07-01

    Full Text Available This study improved simulation of forest carbon fluxes in the Changbai Mountains with a process-based model (Biome-BGC using incorporation and data assimilation. Firstly, the original remote sensing-based MODIS MOD_17 GPP (MOD_17 model was optimized using refined input data and biome-specific parameters. The key ecophysiological parameters of the Biome-BGC model were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST sensitivity analysis. Then the optimized MOD_17 model was used to calibrate the Biome-BGC model by adjusting the sensitive ecophysiological parameters. Once the best match was found for the 10 selected forest plots for the 8-day GPP estimates from the optimized MOD_17 and from the Biome-BGC, the values of sensitive ecophysiological parameters were determined. The calibrated Biome-BGC model agreed better with the eddy covariance (EC measurements (R2 = 0.87, RMSE = 1.583 gC·m−2·d−1 than the original model did (R2 = 0.72, RMSE = 2.419 gC·m−2·d−1. To provide a best estimate of the true state of the model, the Ensemble Kalman Filter (EnKF was used to assimilate five years (of eight-day periods between 2003 and 2007 of Global LAnd Surface Satellite (GLASS LAI products into the calibrated Biome-BGC model. The results indicated that LAI simulated through the assimilated Biome-BGC agreed well with GLASS LAI. GPP performances obtained from the assimilated Biome-BGC were further improved and verified by EC measurements at the Changbai Mountains forest flux site (R2 = 0.92, RMSE = 1.261 gC·m−2·d−1.

  10. A Generic Software Framework for Data Assimilation and Model Calibration

    NARCIS (Netherlands)

    Van Velzen, N.

    2010-01-01

    The accuracy of dynamic simulation models can be increased by using observations in conjunction with a data assimilation or model calibration algorithm. However, implementing such algorithms usually increases the complexity of the model software significantly. By using concepts from object oriented

  11. Assimilation of the AVISO Altimetry Data into the Ocean Dynamics Model with a High Spatial Resolution Using Ensemble Optimal Interpolation (EnOI)

    Science.gov (United States)

    Kaurkin, M. N.; Ibrayev, R. A.; Belyaev, K. P.

    2018-01-01

    A parallel realization of the Ensemble Optimal Interpolation (EnOI) data assimilation (DA) method in conjunction with the eddy-resolving global circulation model is implemented. The results of DA experiments in the North Atlantic with the assimilation of the Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) data from the Jason-1 satellite are analyzed. The results of simulation are compared with the independent temperature and salinity data from the ARGO drifters.

  12. Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

    Science.gov (United States)

    Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

    2011-01-01

    This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

  13. Development and validation of a system of assimilation indices: A mixed method approach to understand change in psychotherapy.

    Science.gov (United States)

    Neto, David D; Baptista, Telmo M; Dent-Brown, Kim

    2015-06-01

    Assimilation is an important process in understanding change in psychotherapy. Similar to other psychological processes, assimilation may be traceable in the speech of clients by attending to its signs or indices. In the present research, we aimed to build a system of indices of assimilation. This research follows a mixed method design. The indices were derived through qualitative analysis, using grounded theory. Subsequently, the indices were adjusted quantitatively and applied to 30 single psychotherapy sessions of adult clients with depression and 11 therapists. Forty-two indices were found and grouped into the following five process categories of assimilation: external distress, pain, noticing, decentring and action. The indices showed good inter-rater reliability and internal consistency. Except for noticing, all process categories correlated significantly with each other according to conceptual proximity. The system of indices also showed convergent validity with an existing coding system of assimilation for two process categories. The results suggest that the system of indices is a useful approach for understanding assimilation. The consideration of assimilation in a continuous fashion through sub-processes may help to extend our knowledge of this process and provide a tool for clinical practice. Assimilation is an important process in understanding change in psychotherapy in the sense that it takes into account insight and action-related processes. Clients convey in their speech signs or indices of the assimilation process which can be observed both in the style and content of their utterances. Using these indices, therapists can continuously assess assimilation and use this information in choosing interventions. Limitations: This study follows a cross-sectional design and does not allow consideration of the predictive value of the indices. The outcome of the therapy was not taken into account, which restricts validity considerations to the comparison with

  14. Insight into the Global Carbon Cycle from Assimilation of Satellite CO2 measurements

    Science.gov (United States)

    Baker, D. F.

    2017-12-01

    A key goal of satellite CO2 measurements is to provide sufficient spatio-temporal coverage to constrain portions of the globe poorly observed by the in situ network, especially the tropical land regions. While systematic errors in both measurements and modeling remain a challenge, these satellite data are providing new insight into the functioning of the global carbon cycle, most notably across the recent 2015-16 En Niño. Here we interpret CO2 measurements from the GOSAT and OCO-2 satellites, as well as from the global in situ network (both surface sites and routine aircraft profiles), using a 4DVar-based global CO2 flux inversion across 2009-2017. The GOSAT data indicate that the tropical land regions are responsible for most of the observed global variability in CO2 across the last 8+ years. For the most recent couple of years where they overlap, the OCO-2 data give the same result, an +2 PgC/yr shift towards CO2 release in the ENSO warm phase, while disagreeing somewhat on the absolute value of the flux. The variability given by both these satellites disagrees with that given by an in situ-only inversion across the recent 2015-16 El Niño: the +2 PgC/yr shift from the satellites is double that given by the in situ data alone, suggesting that the more complete coverage is providing a more accurate view. For the current release of OCO-2 data (version 7), however, the flux results given by the OCO-2 land data (from both nadir- and glint-viewing modes) disagree significantly with those given by the ocean glint data; we examine the soon-to-be-released v8 data to assess whether these systematic retrieval errors have been reduced, and whether the corrected OCO-2 ocean data support the result from the land data. We discuss finer-scale features flux results given by the satellite data, and examine the importance of the flux prior, as well.

  15. A balanced Kalman filter ocean data assimilation system with application to the South Australian Sea

    Science.gov (United States)

    Li, Yi; Toumi, Ralf

    2017-08-01

    In this paper, an Ensemble Kalman Filter (EnKF) based regional ocean data assimilation system has been developed and applied to the South Australian Sea. This system consists of the data assimilation algorithm provided by the NCAR Data Assimilation Research Testbed (DART) and the Regional Ocean Modelling System (ROMS). We describe the first implementation of the physical balance operator (temperature-salinity, hydrostatic and geostrophic balance) to DART, to reduce the spurious waves which may be introduced during the data assimilation process. The effect of the balance operator is validated in both an idealised shallow water model and the ROMS model real case study. In the shallow water model, the geostrophic balance operator eliminates spurious ageostrophic waves and produces a better sea surface height (SSH) and velocity analysis and forecast. Its impact increases as the sea surface height and wind stress increase. In the real case, satellite-observed sea surface temperature (SST) and SSH are assimilated in the South Australian Sea with 50 ensembles using the Ensemble Adjustment Kalman Filter (EAKF). Assimilating SSH and SST enhances the estimation of SSH and SST in the entire domain, respectively. Assimilation with the balance operator produces a more realistic simulation of surface currents and subsurface temperature profile. The best improvement is obtained when only SSH is assimilated with the balance operator. A case study with a storm suggests that the benefit of the balance operator is of particular importance under high wind stress conditions. Implementing the balance operator could be a general benefit to ocean data assimilation systems.

  16. HYbrid Coordinate Ocean Model (HYCOM): Global

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Global HYbrid Coordinate Ocean Model (HYCOM) and U.S. Navy Coupled Ocean Data Assimilation (NCODA) 3-day, daily forecast at approximately 9-km (1/12-degree)...

  17. Evaluation of relevant information for optimal reflector modeling through data assimilation procedures

    International Nuclear Information System (INIS)

    Argaud, J.P.; Bouriquet, B.; Clerc, T.; Lucet-Sanchez, F.; Poncot, A.

    2015-01-01

    The goal of this study is to look after the amount of information that is mandatory to get a relevant parameters optimisation by data assimilation for physical models in neutronic diffusion calculations, and to determine what is the best information to reach the optimum of accuracy at the cheapest cost. To evaluate the quality of the optimisation, we study the covariance matrix that represents the accuracy of the optimised parameter. This matrix is a classical output of the data assimilation procedure, and it is the main information about accuracy and sensitivity of the parameter optimal determination. We present some results collected in the field of neutronic simulation for PWR type reactor. We seek to optimise the reflector parameters that characterise the neutronic reflector surrounding the whole reactive core. On the basis of the configuration studies, it has been shown that with data assimilation we can determine a global strategy to optimise the quality of the result with respect to the amount of information provided. The consequence of this is a cost reduction in terms of measurement and/or computing time with respect to the basic approach. Another result is that using multi-campaign data rather data from a unique campaign significantly improves the efficiency of parameters optimisation

  18. Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

    Directory of Open Access Journals (Sweden)

    Seungpil Jung

    2018-02-01

    Full Text Available This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF and ensemble smoother (ES as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization.

  19. Development of a GSI-Based, 2D-VAR Data Assimilation System for Operational Wave Guidance at the National Weather Service

    Science.gov (United States)

    Flampouris, S.; Alves, H.; Pondeca, M.

    2016-02-01

    The US National Centers for Environmental Prediction (NCEP) provides wave guidance to the National Weather Service (NWS) via a suite of operational wave models, which include three global-scale systems. An approach is being developed to include data assimilation into the global wave models using a 2D version of NCEP's grid-point statistical interpolation (2D-GSI), as described in Derber & Rosatti (1989), and Pondeca et al (2011). As a first step to the global implementation of a wave DA system, a prototype is being developed that will consist of adding wave heights as an analysis variable to the operational Real-Time Mesoscale Analysis (RTMA), which provides hourly analyses of several near sea-surface meteorological parameters, and supports a variety of applications within the NWS. The core of the RTMA is a 2D version of the GSI, which is a variational data assimilation system, and the first guess for the wave-height analysis is provided by NCEP's global wave models. For the new application, the RTMA will be modified to reflect background error covariances consistent with wave-height fields for regional and nearshore applications. In addition, quality control modules for in situ and altimeter significant wave height have been developed and integrated into the system. The strengths and the performance of the 2D-GSI are illustrated with both in situ and satellite measurements of significant wave height in the NW Atlantic and the Gulf of Mexico. The validation of follows the typical cross-validation procedure of RTMA products, based on 10% of the observations, for a period of 15 days. The error statistics (mean, root-mean-square) of the wave-height analysis shows significant improvement, relative to the first guess.

  20. Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations

    KAUST Repository

    Viswanadhapalli, Yesubabu; Srinivas, C.V.; Langodan, Sabique; Hoteit, Ibrahim

    2015-01-01

    The impact of variational data assimilation for predicting two heavy rainfall events that caused devastating floods in Jeddah, Saudi Arabia is studied using the Weather Research and Forecasting (WRF) model. On 25 November 2009 and 26 January 2011

  1. Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

    Science.gov (United States)

    Putman, William M.

    2010-01-01

    NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

  2. Advanced Data Assimilation for Geosciences : Lecture Notes of the Les Houches School of Physics

    CERN Document Server

    Bocquet, Marc; Cosme, Emmanuel; Cugliandolo, Leticia F

    2014-01-01

    This book gathers notes from lectures and seminars given during a three-week school on theoretical and applied data assimilation held in Les Houches in 2012. Data assimilation aims at determining as accurately as possible the state of a dynamical system by combining heterogeneous sources of information in an optimal way. Generally speaking, the mathematical methods of data assimilation describe algorithms for forming optimal combinations of observations of a system, a numerical model that describes its evolution, and appropriate prior information. Data assimilation has a long history of application to high-dimensional geophysical systems dating back to the 1960s, with application to the estimation of initial conditions for weather forecasts. It has become a major component of numerical forecasting systems in geophysics, and an intensive field of research, with numerous additional applications in oceanography and atmospheric chemistry, with extensions to other geophysical sciences. The physical complexity and ...

  3. Spatial dependence of color assimilation by the watercolor effect.

    Science.gov (United States)

    Devinck, Frédéric; Delahunt, Peter B; Hardy, Joseph L; Spillmann, Lothar; Werner, John S

    2006-01-01

    Color assimilation with bichromatic contours was quantified for spatial extents ranging from von Bezold-type color assimilation to the watercolor effect. The magnitude and direction of assimilative hue change was measured as a function of the width of a rectangular stimulus. Assimilation was quantified by hue cancellation. Large hue shifts were required to null the color of stimuli < or = 9.3 min of arc in width, with an exponential decrease for stimuli increasing up to 7.4 deg. When stimuli were viewed through an achromatizing lens, the magnitude of the assimilation effect was reduced for narrow stimuli, but not for wide ones. These results demonstrate that chromatic aberration may account, in part, for color assimilation over small, but not large, surface areas.

  4. Improving representation of nitrogen uptake, allocation, and carbon assimilation in the Community Land Model

    Science.gov (United States)

    Ghimire, B.; Riley, W. J.; Koven, C.

    2013-12-01

    Nitrogen is the most important nutrient limiting plant carbon assimilation and growth, and is required for production of photosynthetic enzymes, growth and maintenance respiration, and maintaining cell structure. The forecasted rise in plant available nitrogen through atmospheric nitrogen deposition and the release of locked soil nitrogen by permafrost thaw in high latitude ecosystems is likely to result in an increase in plant productivity. However a mechanistic representation of plant nitrogen dynamics is lacking in earth system models. Most earth system models ignore the dynamic nature of plant nutrient uptake and allocation, and further lack tight coupling of below- and above-ground processes. In these models, the increase in nitrogen uptake does not translate to a corresponding increase in photosynthesis parameters, such as maximum Rubisco capacity and electron transfer rate. We present an improved modeling framework implemented in the Community Land Model version 4.5 (CLM4.5) for dynamic plant nutrient uptake, and allocation to different plant parts, including leaf enzymes. This modeling framework relies on imposing a more realistic flexible carbon to nitrogen stoichiometric ratio for different plant parts. The model mechanistically responds to plant nitrogen uptake and leaf allocation though changes in photosynthesis parameters. We produce global simulations, and examine the impacts of the improved nitrogen cycling. The improved model is evaluated against multiple observations including TRY database of global plant traits, nitrogen fertilization observations and 15N tracer studies. Global simulations with this new version of CLM4.5 showed better agreement with the observations than the default CLM4.5-CN model, and captured the underlying mechanisms associated with plant nitrogen cycle.

  5. Multi-centennial upper-ocean heat content reconstruction using online data assimilation

    Science.gov (United States)

    Perkins, W. A.; Hakim, G. J.

    2017-12-01

    The Last Millennium Reanalysis (LMR) provides an advanced paleoclimate ensemble data assimilation framework for multi-variate climate field reconstructions over the Common Era. Although reconstructions in this framework with full Earth system models remain prohibitively expensive, recent work has shown improved ensemble reconstruction validation using computationally inexpensive linear inverse models (LIMs). Here we leverage these techniques in pursuit of a new multi-centennial field reconstruction of upper-ocean heat content (OHC), synthesizing model dynamics with observational constraints from proxy records. OHC is an important indicator of internal climate variability and responds to planetary energy imbalances. Therefore, a consistent extension of the OHC record in time will help inform aspects of low-frequency climate variability. We use the Community Climate System Model version 4 (CCSM4) and Max Planck Institute (MPI) last millennium simulations to derive the LIMs, and the PAGES2K v.2.0 proxy database to perform annually resolved reconstructions of upper-OHC, surface air temperature, and wind stress over the last 500 years. Annual OHC reconstructions and uncertainties for both the global mean and regional basins are compared against observational and reanalysis data. We then investigate differences in dynamical behavior at decadal and longer time scales between the reconstruction and simulations in the last-millennium Coupled Model Intercomparison Project version 5 (CMIP5). Preliminary investigation of 1-year forecast skill for an OHC-only LIM shows largely positive spatial grid point local anomaly correlations (LAC) with a global average LAC of 0.37. Compared to 1-year OHC persistence forecast LAC (global average LAC of 0.30), the LIM outperforms the persistence forecasts in the tropical Indo-Pacific region, the equatorial Atlantic, and in certain regions near the Antarctic Circumpolar Current. In other regions, the forecast correlations are less than the

  6. Delineating Hydrofacies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics

    Energy Technology Data Exchange (ETDEWEB)

    Song, Xuehang [Florida State Univ., Tallahassee, FL (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Chen, Xingyuan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ye, Ming [Florida State Univ., Tallahassee, FL (United States); Dai, Zhenxue [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Hammond, Glenn Edward [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-07-01

    This study develops a new framework of facies-based data assimilation for characterizing spatial distribution of hydrofacies and estimating their associated hydraulic properties. This framework couples ensemble data assimilation with transition probability-based geostatistical model via a parameterization based on a level set function. The nature of ensemble data assimilation makes the framework efficient and flexible to be integrated with various types of observation data. The transition probability-based geostatistical model keeps the updated hydrofacies distributions under geological constrains. The framework is illustrated by using a two-dimensional synthetic study that estimates hydrofacies spatial distribution and permeability in each hydrofacies from transient head data. Our results show that the proposed framework can characterize hydrofacies distribution and associated permeability with adequate accuracy even with limited direct measurements of hydrofacies. Our study provides a promising starting point for hydrofacies delineation in complex real problems.

  7. A case study of GWE satellite data impact on GLA assimilation analyses of two ocean cyclones

    Science.gov (United States)

    Gallimore, R. G.; Johnson, D. R.

    1986-01-01

    The effects of the Global Weather Experiment (GWE) data obtained on January 18-20, 1979 on Goddard Laboratory for Atmospheres assimilation analyses of simultaneous cyclones in the western Pacific and Atlantic oceans are examined. The ability of satellite data within assimilation models to determine the baroclinic structures of developing extratropical cyclones is evaluated. The impact of the satellite data on the amplitude and phase of the temperature structure within the storm domain, potential energy, and baroclinic growth rate is studied. The GWE data are compared with Data Systems Test results. It is noted that it is necessary to characterize satellite effects on the baroclinic structure of cyclone waves which degrade numerical weather predictions of cyclogenesis.

  8. Kalman filters for assimilating near-surface observations into the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

    Science.gov (United States)

    Medina, H.; Romano, N.; Chirico, G. B.

    2014-07-01

    This study presents a dual Kalman filter (DSUKF - dual standard-unscented Kalman filter) for retrieving states and parameters controlling the soil water dynamics in a homogeneous soil column, by assimilating near-surface state observations. The DSUKF couples a standard Kalman filter for retrieving the states of a linear solver of the Richards equation, and an unscented Kalman filter for retrieving the parameters of the soil hydraulic functions, which are defined according to the van Genuchten-Mualem closed-form model. The accuracy and the computational expense of the DSUKF are compared with those of the dual ensemble Kalman filter (DEnKF) implemented with a nonlinear solver of the Richards equation. Both the DSUKF and the DEnKF are applied with two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil water matric pressure head (h). The comparison analyses are conducted with reference to synthetic time series of the true states, noise corrupted observations, and synthetic time series of the meteorological forcing. The performance of the retrieval algorithms are examined accounting for the effects exerted on the output by the input parameters, the observation depth and assimilation frequency, as well as by the relationship between retrieved states and assimilated variables. The uncertainty of the states retrieved with DSUKF is considerably reduced, for any initial wrong parameterization, with similar accuracy but less computational effort than the DEnKF, when this is implemented with ensembles of 25 members. For ensemble sizes of the same order of those involved in the DSUKF, the DEnKF fails to provide reliable posterior estimates of states and parameters. The retrieval performance of the soil hydraulic parameters is strongly affected by several factors, such as the initial guess of the unknown parameters, the wet or dry

  9. Open source data assimilation framework for hydrological modeling

    Science.gov (United States)

    Ridler, Marc; Hummel, Stef; van Velzen, Nils; Katrine Falk, Anne; Madsen, Henrik

    2013-04-01

    processes from a different domain or have different spatial and temporal resolutions. An open source framework that bridges OpenMI and OpenDA is presented. The framework provides a generic and easy means for any OpenMI compliant model to assimilate observation measurements. An example test case will be presented using MikeSHE, and OpenMI compliant fully coupled integrated hydrological model that can accurately simulate the feedback dynamics of overland flow, unsaturated zone and saturated zone.

  10. Local Convergence and Global Diversity : From Interpersonal to Social Influence

    NARCIS (Netherlands)

    Flache, Andreas; Macy, Michael W.

    2011-01-01

    How can minority cultures resist assimilation into a global monolith in an increasingly "small world"? Paradoxically, Axelrod found that local convergence can actually preserve global diversity if cultural influence is combined with homophily, the principle that "likes attract." However, follow-up

  11. Variance-based Sensitivity Analysis of Large-scale Hydrological Model to Prepare an Ensemble-based SWOT-like Data Assimilation Experiments

    Science.gov (United States)

    Emery, C. M.; Biancamaria, S.; Boone, A. A.; Ricci, S. M.; Garambois, P. A.; Decharme, B.; Rochoux, M. C.

    2015-12-01

    Land Surface Models (LSM) coupled with River Routing schemes (RRM), are used in Global Climate Models (GCM) to simulate the continental part of the water cycle. They are key component of GCM as they provide boundary conditions to atmospheric and oceanic models. However, at global scale, errors arise mainly from simplified physics, atmospheric forcing, and input parameters. More particularly, those used in RRM, such as river width, depth and friction coefficients, are difficult to calibrate and are mostly derived from geomorphologic relationships, which may not always be realistic. In situ measurements are then used to calibrate these relationships and validate the model, but global in situ data are very sparse. Additionally, due to the lack of existing global river geomorphology database and accurate forcing, models are run at coarse resolution. This is typically the case of the ISBA-TRIP model used in this study.A complementary alternative to in-situ data are satellite observations. In this regard, the Surface Water and Ocean Topography (SWOT) satellite mission, jointly developed by NASA/CNES/CSA/UKSA and scheduled for launch around 2020, should be very valuable to calibrate RRM parameters. It will provide maps of water surface elevation for rivers wider than 100 meters over continental surfaces in between 78°S and 78°N and also direct observation of river geomorphological parameters such as width ans slope.Yet, before assimilating such kind of data, it is needed to analyze RRM temporal sensitivity to time-constant parameters. This study presents such analysis over large river basins for the TRIP RRM. Model output uncertainty, represented by unconditional variance, is decomposed into ordered contribution from each parameter. Doing a time-dependent analysis allows then to identify to which parameters modeled water level and discharge are the most sensitive along a hydrological year. The results show that local parameters directly impact water levels, while

  12. Data assimilation of depth-distributed satellite chlorophyll-α in two Mediterranean contrasting sites

    KAUST Repository

    Kalaroni, S.

    2016-04-12

    A new approach for processing the remote sensing chlorophyll-α (Chl-α) before assimilating into an ecosystem model is applied in two contrasting, regarding productivity and nutrients availability, Mediterranean sites: the DYFAMED and POSEIDON E1-M3A fixed point open ocean observatories. The new approach derives optically weighted depth-distributed Chl-α profiles from satellite data based on the model simulated Chl-α vertical distribution and light attenuation coefficient. We use the 1D version of the operational ecological 3D POSEIDON model, based on the European Regional Seas Ecosystem Model (ERSEM). The required hydrodynamic properties are obtained (off-line) from the POSEIDON operational 3D hydrodynamic Mediterranean basin scale model. The data assimilation scheme is the Singular Evolutive Interpolated Kalman (SEIK) filter, the ensemble variant of the Singular Evolutive Extended Kalman (SEEK) filter. The performance of the proposed assimilation approach was evaluated against the Chl-α satellite data and the seasonal averages of available in-situ data for nitrate, phosphate and Chl-α. An improvement of the model simulated near-surface and subsurface maximum Chl-α concentrations is obtained, especially at the DYFAMED site. Model nitrate is improved with assimilation, particularly with the new approach assimilating depth-distributed Chl-α, while model phosphate is slightly worse after assimilation. Additional sensitivity experiments were performed, showing a better performance of the new approach under different scenarios of model Chl-α deviation from pseudo-observations of surface Chl-α.

  13. Data assimilation of depth-distributed satellite chlorophyll-α in two Mediterranean contrasting sites

    KAUST Repository

    Kalaroni, S.; Tsiaras, K.; Petihakis, G.; Hoteit, Ibrahim; Economou-Amilli, A.; G.Triantafyllou

    2016-01-01

    A new approach for processing the remote sensing chlorophyll-α (Chl-α) before assimilating into an ecosystem model is applied in two contrasting, regarding productivity and nutrients availability, Mediterranean sites: the DYFAMED and POSEIDON E1-M3A fixed point open ocean observatories. The new approach derives optically weighted depth-distributed Chl-α profiles from satellite data based on the model simulated Chl-α vertical distribution and light attenuation coefficient. We use the 1D version of the operational ecological 3D POSEIDON model, based on the European Regional Seas Ecosystem Model (ERSEM). The required hydrodynamic properties are obtained (off-line) from the POSEIDON operational 3D hydrodynamic Mediterranean basin scale model. The data assimilation scheme is the Singular Evolutive Interpolated Kalman (SEIK) filter, the ensemble variant of the Singular Evolutive Extended Kalman (SEEK) filter. The performance of the proposed assimilation approach was evaluated against the Chl-α satellite data and the seasonal averages of available in-situ data for nitrate, phosphate and Chl-α. An improvement of the model simulated near-surface and subsurface maximum Chl-α concentrations is obtained, especially at the DYFAMED site. Model nitrate is improved with assimilation, particularly with the new approach assimilating depth-distributed Chl-α, while model phosphate is slightly worse after assimilation. Additional sensitivity experiments were performed, showing a better performance of the new approach under different scenarios of model Chl-α deviation from pseudo-observations of surface Chl-α.

  14. Using Deep Learning for Targeted Data Selection, Improving Satellite Observation Utilization for Model Initialization

    Science.gov (United States)

    Lee, Y. J.; Bonfanti, C. E.; Trailovic, L.; Etherton, B.; Govett, M.; Stewart, J.

    2017-12-01

    At present, a fraction of all satellite observations are ultimately used for model assimilation. The satellite data assimilation process is computationally expensive and data are often reduced in resolution to allow timely incorporation into the forecast. This problem is only exacerbated by the recent launch of Geostationary Operational Environmental Satellite (GOES)-16 satellite and future satellites providing several order of magnitude increase in data volume. At the NOAA Earth System Research Laboratory (ESRL) we are researching the use of machine learning the improve the initial selection of satellite data to be used in the model assimilation process. In particular, we are investigating the use of deep learning. Deep learning is being applied to many image processing and computer vision problems with great success. Through our research, we are using convolutional neural network to find and mark regions of interest (ROI) to lead to intelligent extraction of observations from satellite observation systems. These targeted observations will be used to improve the quality of data selected for model assimilation and ultimately improve the impact of satellite data on weather forecasts. Our preliminary efforts to identify the ROI's are focused in two areas: applying and comparing state-of-art convolutional neural network models using the analysis data from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) weather model, and using these results as a starting point to optimize convolution neural network model for pattern recognition on the higher resolution water vapor data from GOES-WEST and other satellite. This presentation will provide an introduction to our convolutional neural network model to identify and process these ROI's, along with the challenges of data preparation, training the model, and parameter optimization.

  15. Retrieving moisture profiles from precipitable water measurements using a variational data assimilation approach

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Y.R.; Zou, X.; Kuo, Y.H. [National Center for Atmospheric Research, Boulder, CO (United States)

    1996-04-01

    Atmospheric moisture distribution is directly related to the formation of clouds and precipitation and affects the atmospheric radiation and climate. Currently, several remote sensing systems can measure precipitable water (PW) with fairly high accuracy. As part of the development of an Integrated Data Assimilation and Sounding System in support of the Atmospheric Radiation Measurement Program, retrieving the 3-D water vapor fields from PW measurements is an important problem. A new four dimensional variational (4DVAR) data assimilation system based on the Penn State/National Center for Atmospheric Research (NCAR) mesoscale model (MM5) has been developed by Zou et al. (1995) with the adjoint technique. In this study, we used this 4DVAR system to retrieve the moisture profiles. Because we do not have a set of real observed PW measurements now, the special soundings collected during the Severe Environmental Storm and Mesoscale Experiment (SESAME) in 1979 were used to simulate a set of PW measurements, which were then assimilated into the 4DVAR system. The accuracy of the derived water vapor fields was assessed by direct comparison with the detailed specific humidity soundings. The impact of PW assimilation on precipitation forecast was examined by conducting a series of model forecast experiments started from the different initial conditions with or without data assimilation.

  16. Global Land Data Assimilation System (GLDAS) Products, Services and Application from NASA Hydrology Data and Information Services Center (HDISC)

    Science.gov (United States)

    Fang, Hongliang; Beaudoing, Hiroko K.; Rodell, matthew; Teng, William L.; Vollmer, Bruce E.

    2009-01-01

    The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data holdings include a set of 1.0 degree resolution data products from the four models, covering 1979 to the present; and a 0.25 degree data product from the Noah model, covering 2000 to the present. The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces. Users can search the products through keywords and perform on-the-fly spatial and parameter subsetting and format conversion of selected data. More advanced visualization, access and analysis capabilities will be available in the future. The long term GLDAS data are used to develop climatology of water cycle components and to explore the teleconnections of droughts and pluvial.

  17. Data assimilation techniques in modeling ocean processes

    Digital Repository Service at National Institute of Oceanography (India)

    Mahadevan, R.; Fernandes, A.A.; Naqvi, S.W.A.

    are usually called data analysis or assimilation. These homogeneous fields are prerequisites for various practical applications and theoretical studies. The fields produced by an analysi the one hand, they must be close to the observations.... The practical usefulness of variational methods for meteorological problems are pointed out very early by Sasaki (1955, 1958), but in spite of that these methods have not been fully utilized. Probably, the complex mathematical technicality of these methods...

  18. On the effectiveness of surface assimilation in probabilistic nowcasts of planetary boundary layer profiles

    Science.gov (United States)

    Rostkier-Edelstein, Dorita; Hacker, Joshua

    2013-04-01

    Surface observations comprise a wide, non-expensive and reliable source of information about the state of the near-surface planetary boundary layer (PBL). Operational data assimilation systems have encountered several difficulties in effectively assimilating them, among others due to their local-scale representativeness, the transient coupling between the surface and the atmosphere aloft and the balance constraints usually used. A long-term goal of this work is to find an efficient system for probabilistic PBL nowcasting that can be employed wherever surface observations are present. Earlier work showed that surface observations can be an important source of information with a single column model (SCM) and an ensemble filter (EF). Here we extend that work to quantify the probabilistic skill of ensemble SCM predictions with a model including added complexity. We adopt a factor separation analysis to quantify the contribution of surface assimilation relative to that of selected model components (parameterized radiation and externally imposed horizontal advection) to the probabilistic skill of the system, and of any beneficial or detrimental interactions between them. To assess the real utility of the flow-dependent covariances estimated with the EF and of the SCM of the PBL we compare the skill of the SCM/EF system to that of a reference one based on climatological covariances and a 30-min persistence model. It consists of a dressing technique, whereby a deterministic 3D mesoscale forecast (e.g. from WRF model) is adjusted and dressed with uncertainty using a seasonal sample of mesoscale forecasts and surface forecast errors. Results show that assimilation of surface observations can improve deterministic and probabilistic profile predictions more significantly than major model improvements. Flow-dependent covariances estimated with the SCM/EF show clear advantage over the use of climatological covariances when the flow is characterized by wide variability, when

  19. First assimilations of COSMIC radio occultation data into the Electron Density Assimilative Model (EDAM

    Directory of Open Access Journals (Sweden)

    M. J. Angling

    2008-02-01

    Full Text Available Ground based measurements of slant total electron content (TEC can be assimilated into ionospheric models to produce 3-D representations of ionospheric electron density. The Electron Density Assimilative Model (EDAM has been developed for this purpose. Previous tests using EDAM and ground based data have demonstrated that the information on the vertical structure of the ionosphere is limited in this type of data. The launch of the COSMIC satellite constellation provides the opportunity to use radio occultation data which has more vertical information. EDAM assimilations have been run for three time periods representing quiet, moderate and disturbed geomagnetic conditions. For each run, three data sets have been ingested – only ground based data, only COSMIC data and both ground based and COSMIC data. The results from this preliminary study show that both ground and space based data are capable of improving the representation of the vertical structure of the ionosphere. However, the analysis is limited by the incomplete deployment of the COSMIC constellation and the use of auto-scaled ionosonde data. The first of these can be addressed by repeating this type of study once full deployment has been achieved. The latter requires the manual scaling of ionosonde data; ideally an agreed data set would be scaled and made available to the community to facilitate comparative testing of assimilative models.

  20. A case for variational geomagnetic data assimilation: insights from a one-dimensional, nonlinear, and sparsely observed MHD system

    Directory of Open Access Journals (Sweden)

    A. Fournier

    2007-01-01

    Full Text Available Secular variations of the geomagnetic field have been measured with a continuously improving accuracy during the last few hundred years, culminating nowadays with satellite data. It is however well known that the dynamics of the magnetic field is linked to that of the velocity field in the core and any attempt to model secular variations will involve a coupled dynamical system for magnetic field and core velocity. Unfortunately, there is no direct observation of the velocity. Independently of the exact nature of the above-mentioned coupled system – some version being currently under construction – the question is debated in this paper whether good knowledge of the magnetic field can be translated into good knowledge of core dynamics. Furthermore, what will be the impact of the most recent and precise geomagnetic data on our knowledge of the geomagnetic field of the past and future? These questions are cast into the language of variational data assimilation, while the dynamical system considered in this paper consists in a set of two oversimplified one-dimensional equations for magnetic and velocity fields. This toy model retains important features inherited from the induction and Navier-Stokes equations: non-linear magnetic and momentum terms are present and its linear response to small disturbances contains Alfvén waves. It is concluded that variational data assimilation is indeed appropriate in principle, even though the velocity field remains hidden at all times; it allows us to recover the entire evolution of both fields from partial and irregularly distributed information on the magnetic field. This work constitutes a first step on the way toward the reassimilation of historical geomagnetic data and geomagnetic forecast.

  1. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib

    2017-05-26

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  2. A variational ensemble scheme for noisy image data assimilation

    Science.gov (United States)

    Yang, Yin; Robinson, Cordelia; Heitz, Dominique; Mémin, Etienne

    2014-05-01

    Data assimilation techniques aim at recovering a system state variables trajectory denoted as X, along time from partially observed noisy measurements of the system denoted as Y. These procedures, which couple dynamics and noisy measurements of the system, fulfill indeed a twofold objective. On one hand, they provide a denoising - or reconstruction - procedure of the data through a given model framework and on the other hand, they provide estimation procedures for unknown parameters of the dynamics. A standard variational data assimilation problem can be formulated as the minimization of the following objective function with respect to the initial discrepancy, η, from the background initial guess: δ« J(η(x)) = 1∥Xb (x) - X (t ,x)∥2 + 1 tf∥H(X (t,x ))- Y (t,x)∥2dt. 2 0 0 B 2 t0 R (1) where the observation operator H links the state variable and the measurements. The cost function can be interpreted as the log likelihood function associated to the a posteriori distribution of the state given the past history of measurements and the background. In this work, we aim at studying ensemble based optimal control strategies for data assimilation. Such formulation nicely combines the ingredients of ensemble Kalman filters and variational data assimilation (4DVar). It is also formulated as the minimization of the objective function (1), but similarly to ensemble filter, it introduces in its objective function an empirical ensemble-based background-error covariance defined as: B ≡ )(Xb - )T>. (2) Thus, it works in an off-line smoothing mode rather than on the fly like sequential filters. Such resulting ensemble variational data assimilation technique corresponds to a relatively new family of methods [1,2,3]. It presents two main advantages: first, it does not require anymore to construct the adjoint of the dynamics tangent linear operator, which is a considerable advantage with respect to the method's implementation, and second, it enables the handling of a flow

  3. Chromatic assimilation unaffected by perceived depth of inducing light.

    Science.gov (United States)

    Shevell, Steven K; Cao, Dingcai

    2004-01-01

    Chromatic assimilation is a shift toward the color of nearby light. Several studies conclude that a neural process contributes to assimilation but the neural locus remains in question. Some studies posit a peripheral process, such as retinal receptive-field organization, while others claim the neural mechanism follows depth perception, figure/ground segregation, or perceptual grouping. The experiments here tested whether assimilation depends on a neural process that follows stereoscopic depth perception. By introducing binocular disparity, the test field judged in color was made to appear in a different depth plane than the light that induced assimilation. The chromaticity and spatial frequency of the inducing light, and the chromaticity of the test light, were varied. Chromatic assimilation was found with all inducing-light sizes and chromaticities, but the magnitude of assimilation did not depend on the perceived relative depth planes of the test and inducing fields. We found no evidence to support the view that chromatic assimilation depends on a neural process that follows binocular combination of the two eyes' signals.

  4. Advances in Global Adjoint Tomography -- Massive Data Assimilation

    Science.gov (United States)

    Ruan, Y.; Lei, W.; Bozdag, E.; Lefebvre, M. P.; Smith, J. A.; Krischer, L.; Tromp, J.

    2015-12-01

    Azimuthal anisotropy and anelasticity are key to understanding a myriad of processes in Earth's interior. Resolving these properties requires accurate simulations of seismic wave propagation in complex 3-D Earth models and an iterative inversion strategy. In the wake of successes in regional studies(e.g., Chen et al., 2007; Tape et al., 2009, 2010; Fichtner et al., 2009, 2010; Chen et al.,2010; Zhu et al., 2012, 2013; Chen et al., 2015), we are employing adjoint tomography based on a spectral-element method (Komatitsch & Tromp 1999, 2002) on a global scale using the supercomputer ''Titan'' at Oak Ridge National Laboratory. After 15 iterations, we have obtained a high-resolution transversely isotropic Earth model (M15) using traveltime data from 253 earthquakes. To obtain higher resolution images of the emerging new features and to prepare the inversion for azimuthal anisotropy and anelasticity, we expanded the original dataset with approximately 4,220 additional global earthquakes (Mw5.5-7.0) --occurring between 1995 and 2014-- and downloaded 300-minute-long time series for all available data archived at the IRIS Data Management Center, ORFEUS, and F-net. Ocean Bottom Seismograph data from the last decade are also included to maximize data coverage. In order to handle the huge dataset and solve the I/O bottleneck in global adjoint tomography, we implemented a python-based parallel data processing workflow based on the newly developed Adaptable Seismic Data Format (ASDF). With the help of the data selection tool MUSTANG developed by IRIS, we cleaned our dataset and assembled event-based ASDF files for parallel processing. We have started Centroid Moment Tensors (CMT) inversions for all 4,220 earthquakes with the latest model M15, and selected high-quality data for measurement. We will statistically investigate each channel using synthetic seismograms calculated in M15 for updated CMTs and identify problematic channels. In addition to data screening, we also modified

  5. On the importance of measurement error correlations in data assimilation for integrated hydrological models

    Science.gov (United States)

    Camporese, Matteo; Botto, Anna

    2017-04-01

    Data assimilation is becoming increasingly popular in hydrological and earth system modeling, as it allows us to integrate multisource observation data in modeling predictions and, in doing so, to reduce uncertainty. For this reason, data assimilation has been recently the focus of much attention also for physically-based integrated hydrological models, whereby multiple terrestrial compartments (e.g., snow cover, surface water, groundwater) are solved simultaneously, in an attempt to tackle environmental problems in a holistic approach. Recent examples include the joint assimilation of water table, soil moisture, and river discharge measurements in catchment models of coupled surface-subsurface flow using the ensemble Kalman filter (EnKF). One of the typical assumptions in these studies is that the measurement errors are uncorrelated, whereas in certain situations it is reasonable to believe that some degree of correlation occurs, due for example to the fact that a pair of sensors share the same soil type. The goal of this study is to show if and how the measurement error correlations between different observation data play a significant role on assimilation results in a real-world application of an integrated hydrological model. The model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental hillslope. The physical model, located in the Department of Civil, Environmental and Architectural Engineering of the University of Padova (Italy), consists of a reinforced concrete box containing a soil prism with maximum height of 3.5 m, length of 6 m, and width of 2 m. The hillslope is equipped with sensors to monitor the pressure head and soil moisture responses to a series of generated rainfall events applied onto a 60 cm thick sand layer overlying a sandy clay soil. The measurement network is completed by two tipping bucket flow gages to measure the two components (subsurface and surface) of the outflow. By collecting

  6. Observability of discretized partial differential equations

    Science.gov (United States)

    Cohn, Stephen E.; Dee, Dick P.

    1988-01-01

    It is shown that complete observability of the discrete model used to assimilate data from a linear partial differential equation (PDE) system is necessary and sufficient for asymptotic stability of the data assimilation process. The observability theory for discrete systems is reviewed and applied to obtain simple observability tests for discretized constant-coefficient PDEs. Examples are used to show how numerical dispersion can result in discrete dynamics with multiple eigenvalues, thereby detracting from observability.

  7. National contributions to observed global warming

    International Nuclear Information System (INIS)

    Matthews, H Damon; Graham, Tanya L; Keverian, Serge; Lamontagne, Cassandra; Seto, Donny; Smith, Trevor J

    2014-01-01

    There is considerable interest in identifying national contributions to global warming as a way of allocating historical responsibility for observed climate change. This task is made difficult by uncertainty associated with national estimates of historical emissions, as well as by difficulty in estimating the climate response to emissions of gases with widely varying atmospheric lifetimes. Here, we present a new estimate of national contributions to observed climate warming, including CO 2 emissions from fossil fuels and land-use change, as well as methane, nitrous oxide and sulfate aerosol emissions While some countries’ warming contributions are reasonably well defined by fossil fuel CO 2 emissions, many countries have dominant contributions from land-use CO 2 and non-CO 2 greenhouse gas emissions, emphasizing the importance of both deforestation and agriculture as components of a country’s contribution to climate warming. Furthermore, because of their short atmospheric lifetime, recent sulfate aerosol emissions have a large impact on a country’s current climate contribution We show also that there are vast disparities in both total and per-capita climate contributions among countries, and that across most developed countries, per-capita contributions are not currently consistent with attempts to restrict global temperature change to less than 2 °C above pre-industrial temperatures. (paper)

  8. Development of a WRF-RTFDDA-based high-resolution hybrid data-assimilation and forecasting system toward to operation in the Middle East

    Science.gov (United States)

    Liu, Y.; Wu, W.; Zhang, Y.; Kucera, P. A.; Liu, Y.; Pan, L.

    2012-12-01

    Weather forecasting in the Middle East is challenging because of its complicated geographical nature including massive coastal area and heterogeneous land, and regional spare observational network. Strong air-land-sea interactions form multi-scale weather regimes in the area, which require a numerical weather prediction model capable of properly representing multi-scale atmospheric flow with appropriate initial conditions. The WRF-based Real-Time Four Dimensional Data Assimilation (RTFDDA) system is one of advanced multi-scale weather analysis and forecasting facilities developed at the Research Applications Laboratory (RAL) of NCAR. The forecasting system is applied for the Middle East with careful configuration. To overcome the limitation of the very sparsely available conventional observations in the region, we develop a hybrid data assimilation algorithm combining RTFDDA and WRF-3DVAR, which ingests remote sensing data from satellites and radar. This hybrid data assimilation blends Newtonian nudging FDDA and 3DVAR technology to effectively assimilate both conventional observations and remote sensing measurements and provide improved initial conditions for the forecasting system. For brevity, the forecasting system is called RTF3H (RTFDDA-3DVAR Hybrid). In this presentation, we will discuss the hybrid data assimilation algorithm, and its implementation, and the applications for high-impact weather events in the area. Sensitivity studies are conducted to understand the strength and limitations of this hybrid data assimilation algorithm.

  9. Assimilation of Earth rotation parameters into a global ocean model (FESOM)

    Science.gov (United States)

    Androsov, A.; Schröter, J.; Brunnabend, S.; Saynisch, J.

    2012-04-01

    Earth Rotation Parameters (ERP) are used to improve estimates of the ocean circulation and mass budget. GRACE data can be used for verification or for further improvements. The Finite Element Sea-ice Ocean Model (FESOM) is used to simulate weekly ocean circulation and mass variations. The FESOM model is a hydrostatic ocean circulation model with a fully non-linear free surface. It solves the hydrostatic primitive equations with volume (Boussinesq approximation) and mass (Greatbatch correction) conservation. Fresh water exchange with the atmosphere and land is modelled as mass flux. This flux is the weakest part of the mass budget as it is the difference of large and uncertain quantities: evaporation, precipitation and river runoff. All uncertainties included in these parameters are directly reflected in the model results. ERP help in closing the budget in a realistic manner. Our strategy is designed for testing parametric estimation on a weekly basis. First, Oceanographic Earth rotation parameters (OERP) are calculated by subtracting atmospheric and hydrologic estimates from observed ERP. They are compared to OERP derived from a global ocean circulation model. The difference can be inverted to diagnose a correction of the oceanic mass budget. Additionally mass variations measured by GRACE are used for verification. In a second step, the global mass correction parameter, derived by the inversion, is used to improve the fresh water budget of FESOM.

  10. Evidence for non-assimilation of Chlorella by the African freshwater snail Bulinus (Physopsis) globosus

    International Nuclear Information System (INIS)

    Van Aardt, W.J.; Wolmarans, C.T.

    1981-01-01

    Little is known about the assimilation of its natural food by South African basommatophorans. It is generally assumed that the snails are microphagus herbivores which ingest mainly periphytic algae, detritus and the bacterial component of their food. Preliminary observations indicated that Chlorella spp. were by far the dominant algal species on stems and leaves of Juncus on which the snails were usually found in our study. This report describes experiments to see whether Chlorella is ingested and assimilated by Bulinus (Physopsis) globosus. A closely related species, B. (B.) tropicus, which occupies the same niche was also included in the study for purposes of comparison. It was found that, although Chlorella was continuously ingested by both species, it was assimilated by neither. Possible reasons for this are given

  11. Global CO2 fluxes estimated from GOSAT retrievals of total column CO2

    Directory of Open Access Journals (Sweden)

    S. Basu

    2013-09-01

    Full Text Available We present one of the first estimates of the global distribution of CO2 surface fluxes using total column CO2 measurements retrieved by the SRON-KIT RemoTeC algorithm from the Greenhouse gases Observing SATellite (GOSAT. We derive optimized fluxes from June 2009 to December 2010. We estimate fluxes from surface CO2 measurements to use as baselines for comparing GOSAT data-derived fluxes. Assimilating only GOSAT data, we can reproduce the observed CO2 time series at surface and TCCON sites in the tropics and the northern extra-tropics. In contrast, in the southern extra-tropics GOSAT XCO2 leads to enhanced seasonal cycle amplitudes compared to independent measurements, and we identify it as the result of a land–sea bias in our GOSAT XCO2 retrievals. A bias correction in the form of a global offset between GOSAT land and sea pixels in a joint inversion of satellite and surface measurements of CO2 yields plausible global flux estimates which are more tightly constrained than in an inversion using surface CO2 data alone. We show that assimilating the bias-corrected GOSAT data on top of surface CO2 data (a reduces the estimated global land sink of CO2, and (b shifts the terrestrial net uptake of carbon from the tropics to the extra-tropics. It is concluded that while GOSAT total column CO2 provide useful constraints for source–sink inversions, small spatiotemporal biases – beyond what can be detected using current validation techniques – have serious consequences for optimized fluxes, even aggregated over continental scales.

  12. NOAA's Role in Sustaining Global Ocean Observations: Future Plans for OAR's Ocean Observing and Monitoring Division

    Science.gov (United States)

    Todd, James; Legler, David; Piotrowicz, Stephen; Raymond, Megan; Smith, Emily; Tedesco, Kathy; Thurston, Sidney

    2017-04-01

    The Ocean Observing and Monitoring Division (OOMD, formerly the Climate Observation Division) of the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office provides long-term, high-quality global observations, climate information and products for researchers, forecasters, assessments and other users of environmental information. In this context, OOMD-supported activities serve a foundational role in an enterprise that aims to advance 1) scientific understanding, 2) monitoring and prediction of climate and 3) understanding of potential impacts to enable a climate resilient society. Leveraging approximately 50% of the Global Ocean Observing System, OOMD employs an internationally-coordinated, multi-institution global strategy that brings together data from multiple platforms including surface drifting buoys, Argo profiling floats, flux/transport moorings (RAMA, PIRATA, OceanSITES), GLOSS tide gauges, SOOP-XBT and SOOP-CO2, ocean gliders and repeat hydrographic sections (GO-SHIP). OOMD also engages in outreach, education and capacity development activities to deliver training on the social-economic applications of ocean data. This presentation will highlight recent activities and plans for 2017 and beyond.

  13. Combined constraints on global ocean primary production using observations and models

    Science.gov (United States)

    Buitenhuis, Erik T.; Hashioka, Taketo; Quéré, Corinne Le

    2013-09-01

    production is at the base of the marine food web and plays a central role for global biogeochemical cycles. Yet global ocean primary production is known to only a factor of 2, with previous estimates ranging from 38 to 65 Pg C yr-1 and no formal uncertainty analysis. Here, we present an improved global ocean biogeochemistry model that includes a mechanistic representation of photosynthesis and a new observational database of net primary production (NPP) in the ocean. We combine the model and observations to constrain particulate NPP in the ocean with statistical metrics. The PlankTOM5.3 model includes a new photosynthesis formulation with a dynamic representation of iron-light colimitation, which leads to a considerable improvement of the interannual variability of surface chlorophyll. The database includes a consistent set of 50,050 measurements of 14C primary production. The model best reproduces observations when global NPP is 58 ± 7 Pg C yr-1, with a most probable value of 56 Pg C yr-1. The most probable value is robust to the model used. The uncertainty represents 95% confidence intervals. It considers all random errors in the model and observations, but not potential biases in the observations. We show that tropical regions (23°S-23°N) contribute half of the global NPP, while NPPs in the Northern and Southern Hemispheres are approximately equal in spite of the larger ocean area in the South.

  14. Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

    Science.gov (United States)

    Montero, Rodolfo Alvarado; Schwanenberg, Dirk; Krahe, Peter; Lisniak, Dmytro; Sensoy, Aynur; Sorman, A. Arda; Akkol, Bulut

    2016-06-01

    Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand

  15. The Crc global regulator inhibits the Pseudomonas putida pWW0 toluene/xylene assimilation pathway by repressing the translation of regulatory and structural genes.

    Science.gov (United States)

    Moreno, Renata; Fonseca, Pilar; Rojo, Fernando

    2010-08-06

    In Pseudomonas putida, the expression of the pWW0 plasmid genes for the toluene/xylene assimilation pathway (the TOL pathway) is subject to complex regulation in response to environmental and physiological signals. This includes strong inhibition via catabolite repression, elicited by the carbon sources that the cells prefer to hydrocarbons. The Crc protein, a global regulator that controls carbon flow in pseudomonads, has an important role in this inhibition. Crc is a translational repressor that regulates the TOL genes, but how it does this has remained unknown. This study reports that Crc binds to sites located at the translation initiation regions of the mRNAs coding for XylR and XylS, two specific transcription activators of the TOL genes. Unexpectedly, eight additional Crc binding sites were found overlapping the translation initiation sites of genes coding for several enzymes of the pathway, all encoded within two polycistronic mRNAs. Evidence is provided supporting the idea that these sites are functional. This implies that Crc can differentially modulate the expression of particular genes within polycistronic mRNAs. It is proposed that Crc controls TOL genes in two ways. First, Crc inhibits the translation of the XylR and XylS regulators, thereby reducing the transcription of all TOL pathway genes. Second, Crc inhibits the translation of specific structural genes of the pathway, acting mainly on proteins involved in the first steps of toluene assimilation. This ensures a rapid inhibitory response that reduces the expression of the toluene/xylene degradation proteins when preferred carbon sources become available.

  16. Dacite petrogenesis on mid-ocean ridges: Evidence for oceanic crustal melting and assimilation

    Science.gov (United States)

    Wanless, V.D.; Perfit, M.R.; Ridley, W.I.; Klein, E.

    2010-01-01

    Whereas the majority of eruptions at oceanic spreading centers produce lavas with relatively homogeneous mid-ocean ridge basalt (MORB) compositions, the formation of tholeiitic andesites and dacites at mid-ocean ridges (MORs) is a petrological enigma. Eruptions of MOR high-silica lavas are typically associated with ridge discontinuities and have produced regionally significant volumes of lava. Andesites and dacites have been observed and sampled at several locations along the global MOR system; these include propagating ridge tips at ridge-transform intersections on the Juan de Fuca Ridge and eastern Gal??pagos spreading center, and at the 9??N overlapping spreading center on the East Pacific Rise. Despite the formation of these lavas at various ridges, MOR dacites show remarkably similar major element trends and incompatible trace element enrichments, suggesting that similar processes are controlling their chemistry. Although most geochemical variability in MOR basalts is consistent with low-pressure fractional crystallization of various mantle-derived parental melts, our geochemical data for MOR dacitic glasses suggest that contamination from a seawater-altered component is important in their petrogenesis. MOR dacites are characterized by elevated U, Th, Zr, and Hf, low Nb and Ta concentrations relative to rare earth elements (REE), and Al2O3, K2O, and Cl concentrations that are higher than expected from low-pressure fractional crystallization alone. Petrological modeling of MOR dacites suggests that partial melting and assimilation are both integral to their petrogenesis. Extensive fractional crystallization of a MORB parent combined with partial melting and assimilation of amphibole-bearing altered crust produces a magma with a geochemical signature similar to a MOR dacite. This supports the hypothesis that crustal assimilation is an important process in the formation of highly evolved MOR lavas and may be significant in the generation of evolved MORB in

  17. An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

    Institute of Scientific and Technical Information of China (English)

    Xiaogu ZHENG

    2009-01-01

    An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.

  18. Global geodetic observing system meeting the requirements of a global society on a changing planet in 2020

    CERN Document Server

    Plag, Hans-Peter

    2009-01-01

    Geodesy plays a key role in geodynamics, geohazards, the global water cycle, global change, atmosphere and ocean dynamics. This book covers geodesy's contribution to science and society and identifies user needs regarding geodetic observations and products.

  19. Variational data assimilation using targetted random walks

    KAUST Repository

    Cotter, S. L.

    2011-02-15

    The variational approach to data assimilation is a widely used methodology for both online prediction and for reanalysis. In either of these scenarios, it can be important to assess uncertainties in the assimilated state. Ideally, it is desirable to have complete information concerning the Bayesian posterior distribution for unknown state given data. We show that complete computational probing of this posterior distribution is now within the reach in the offline situation. We introduce a Markov chain-Monte Carlo (MCMC) method which enables us to directly sample from the Bayesian posterior distribution on the unknown functions of interest given observations. Since we are aware that these methods are currently too computationally expensive to consider using in an online filtering scenario, we frame this in the context of offline reanalysis. Using a simple random walk-type MCMC method, we are able to characterize the posterior distribution using only evaluations of the forward model of the problem, and of the model and data mismatch. No adjoint model is required for the method we use; however, more sophisticated MCMC methods are available which exploit derivative information. For simplicity of exposition, we consider the problem of assimilating data, either Eulerian or Lagrangian, into a low Reynolds number flow in a two-dimensional periodic geometry. We will show that in many cases it is possible to recover the initial condition and model error (which we describe as unknown forcing to the model) from data, and that with increasing amounts of informative data, the uncertainty in our estimations reduces. © 2011 John Wiley & Sons, Ltd.

  20. Assimilative model for ionospheric dynamics employing delay, Doppler, and direction of arrival measurements from multiple HF channels

    Science.gov (United States)

    Fridman, Sergey V.; Nickisch, L. J.; Hausman, Mark; Zunich, George

    2016-03-01

    We describe the development of new HF data assimilation capabilities for our ionospheric inversion algorithm called GPSII (GPS Ionospheric Inversion). Previously existing capabilities of this algorithm included assimilation of GPS total electron content data as well as assimilation of backscatter ionograms. In the present effort we concentrated on developing assimilation tools for data related to HF propagation channels. Measurements of propagation delay, angle of arrival, and the ionosphere-induced Doppler from any number of known propagation links can now be utilized by GPSII. The resulting ionospheric model is consistent with all assimilated measurements. This means that ray tracing simulations of the assimilated propagation links are guaranteed to be in agreement with measured data within the errors of measurement. The key theoretical element for assimilating HF data is the raypath response operator (RPRO) which describes response of raypath parameters to infinitesimal variations of electron density in the ionosphere. We construct the RPRO out of the fundamental solution of linearized ray tracing equations for a dynamic magnetoactive plasma. We demonstrate performance and internal consistency of the algorithm using propagation delay data from multiple oblique ionograms (courtesy of Defence Science and Technology Organisation, Australia) as well as with time series of near-vertical incidence sky wave data (courtesy of the Intelligence Advanced Research Projects Activity HFGeo Program Government team). In all cases GPSII produces electron density distributions which are smooth in space and in time. We simulate the assimilated propagation links by performing ray tracing through GPSII-produced ionosphere and observe that simulated data are indeed in agreement with assimilated measurements.