WorldWideScience

Sample records for global observation assimilation

  1. Global assimilation of X Project Loon stratospheric balloon observations

    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.

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

    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

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

    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. Global Data Assimilation System (GDAS)

    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...

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

    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

  11. Effective Assimilation of Global Precipitation

    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

  12. Effective assimilation of global precipitation: simulation experiments

    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

  13. Data Assimilation: Making Sense of Earth Observation

    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.

  14. Data assimilation of CALIPSO aerosol observations

    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.

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

    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.

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

    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).

  17. Assimilation of GNSS radio occultation observations in GRAPES

    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.

  18. Hyper-Resolution Global Land Surface Model at Regional-to-Local Scales with observed Groundwater data assimilation

    Singh, Raj Shekhar

    2014-01-01

    Modeling groundwater is challenging: it is not readily visible and is difficult to measure, with limited sets of observations available. Even though groundwater models can reproduce water table and head variations, considerable drift in modeled land surface states can nonetheless result from partially known geologic structure, errors in the input forcing fields, and imperfect Land Surface Model (LSM) parameterizations. These models frequently have biased results that are very different from o...

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

    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

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

    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.

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

    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.

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

    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

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

    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.

  4. Accelerating assimilation development for new observing systems using EFSO

    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.

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

    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.

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

    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.

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

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

  13. Observability of global rivers with future SWOT observations

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

    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

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

    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).

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

    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.

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

    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.

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

    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.

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

    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

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

    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

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

    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

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

    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

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

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

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

    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

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

    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.

  20. Observing the Global Water Cycle from Space

    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.

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

    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.

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

    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

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

    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

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

    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.

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

    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).

  6. Deriving Global Discharge Records from SWOT Observations

    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

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

  14. Global observations from PHOBOS

    Phobos Collaboration; Baker, Mark D.; Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Ballintijn, M.; Bickley, A. A.; Bindel, R.; Bal, A.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwon, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kan, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Michałowski, J.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Steinberg, P.; Stodulski, G. S. T. M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L. H.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

    2003-03-01

    Particle production in Au+Au collisions has been measured in the PHOBOS experiment at RHIC for a range of collision energies. Three empirical observations have emerged from this dataset which require theoretical examination. First, there is clear evidence of limiting fragmentation. Namely, particle production in central Au+Au collisions, when expressed as $dN/d\\eta'$ ($\\eta' \\equiv \\eta-y_{beam}$), becomes energy independent at high energy for a broad region of $\\eta'$ around $\\eta'=0$. This energy-independent region grows with energy, allowing only a limited region (if any) of longitudinal boost-invariance. Second, there is a striking similarity between particle production in e+e- and Au+Au collisions (scaled by the number of participating nucleon pairs). Both the total number of produced particles and the longitudinal distribution of produced particles are approximately the same in e+e- and in scaled Au+Au. This observation was not predicted and has not been explained. Finally, particle production has been found to scale approximately with the number of participating nucleon pairs for $N_{part}>65$. This scaling occurs both for the total multiplicity and for high $\\pT$ particles (3 $<\\pT<$ 4.5 GeV/c).

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

    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

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

  1. Advances in Global Adjoint Tomography -- Massive Data Assimilation

    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

  2. Space Observations for Global Change

    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.

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

    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

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

    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.

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

    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

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

    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%.

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

    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

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

    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.

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

    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.

  10. Assimilating Remote Ammonia Observations with a Refined Aerosol Thermodynamics Adjoint"

    Ammonia emissions parameters in North America can be refined in order to improve the evaluation of modeled concentrations against observations. Here, we seek to do so by developing and applying the GEOS-Chem adjoint nested over North America to conductassimilation of observations...

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

    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.

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

    Lee, Wonjung; Farmer, Chris

    2014-01-01

    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

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

    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

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

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

    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.

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

    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.

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

    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.

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

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

    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

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

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

    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

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

    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.

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

    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

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

    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

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

    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.

  16. Land Surface Data Assimilation

    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

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

    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).

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

    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.

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

    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.

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

    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.

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

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

    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.

  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

    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. Earth Observations for Global Water Security

    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.

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

    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

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

    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

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

    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

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

    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

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

    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.

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

    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.

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

    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).

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

    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...

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

    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.

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

    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.

  15. Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation

    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.

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

    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,...

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

    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.

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

    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/).

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

    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.

  20. Organic matter assimilation and selective feeding by holothurians in the deep sea: some observations and comments

    Ginger, Michael L.; Billett, David S. M.; Mackenzie, Karen L.; Konstandinos Kiriakoulakis; Neto, Renato R.; K. Boardman, Daniel; Santos, Vera L. C. S.; Horsfall, Ian M.; A. Wolff, George

    The selective feeding behaviour and assimilation efficiencies of deep-sea holothurians were investigated in order to assess their impact on carbon and nitrogen remineralisation on the Porcupine Abyssal Plain (PAP; ˜ 49°N 16°W, ˜ 4850 m water depth). Unfortunately, reliable determination of organic matter in the gut contents of the organisms proved to be difficult, because of the lysis of cells associated with the death of the animals on recovery. This was expressed in high levels of free fatty acids in the gut contents of Oneirophanta mutabilis, which we ascribe to unregulated lipolysis of phospholipids and triacylglycerides. It was not possible to estimate accurately the contribution that such material made to the gut contents, but based on the distributions of sterols in the gut sediments, it is likely to have been substantial. Therefore, all assimilation efficiencies calculated for holothurians in the deep sea should be treated with caution. Fortuitously, a bloom of holothurians that feed on the sediment surface (namely Amperima rosea and Ellipinion molle) during the period of study provided an opportunity indirectly to assess the impact of megafauna on organic matter cycling at the PAP. Observations suggest that the depletion of phytosterols from the surficial sediments between July and October 1997 resulted from the selective uptake of fresh phytodetritus by the blooming species. Deep-sea holothurians do not biosynthesise sterols de novo and an estimate of the sterol required by the increased population of A. rosea and E. molle is equivalent to the sterol flux to the seafloor during the spring/summer of 1997. The implications are dramatic. Firstly, these and other megafauna apparently turned over and selectively removed phytosterols from the freshly arrived phytodetritus and the surficial sediment (0-5 mm) at the PAP in less than four months. Secondly, their action impacted the food resource available to other organisms. Finally, as phytosterols are

  1. Obtaining a Pragmatic Representation of Fire Disturbance in Dynamic Vegetation Models by Assimilating Earth Observation Data

    Kantzas, Euripides; Quegan, Shaun

    2015-04-01

    Fire constitutes a violent and unpredictable pathway of carbon from the terrestrial biosphere into the atmosphere. Despite fire emissions being in many biomes of similar magnitude to that of Net Ecosystem Exchange, even the most complex Dynamic Vegetation Models (DVMs) embedded in IPCC General Circulation Models poorly represent fire behavior and dynamics, a fact which still remains understated. As DVMs operate on a deterministic, grid cell-by-grid cell basis they are unable to describe a host of important fire characteristics such as its propagation, magnitude of area burned and stochastic nature. Here we address these issues by describing a model-independent methodology which assimilates Earth Observation (EO) data by employing image analysis techniques and algorithms to offer a realistic fire disturbance regime in a DVM. This novel approach, with minimum model restructuring, manages to retain the Fire Return Interval produced by the model whilst assigning pragmatic characteristics to its fire outputs thus allowing realistic simulations of fire-related processes such as carbon injection into the atmosphere and permafrost degradation. We focus our simulations in the Arctic and specifically Canada and Russia and we offer a snippet of how this approach permits models to engage in post-fire dynamics hitherto absent from any other model regardless of complexity.

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

    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

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

    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.

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

    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

  5. Technical Report Series on Global Modeling and Data Assimilation, Volume 41 : GDIS Workshop Report

    Koster, Randal D. (Editor); Schubert, Siegfried; Pozzi, Will; Mo, Kingtse; Wood, Eric F.; Stahl, Kerstin; Hayes, Mike; Vogt, Juergen; Seneviratne, Sonia; Stewart, Ron; hide

    2015-01-01

    The workshop "An International Global Drought Information System Workshop: Next Steps" was held on 10-13 December 2014 in Pasadena, California. The more than 60 participants from 15 countries spanned the drought research community and included select representatives from applications communities as well as providers of regional and global drought information products. The workshop was sponsored and supported by the US National Integrated Drought Information System (NIDIS) program, the World Climate Research Program (WCRP: GEWEX, CLIVAR), the World Meteorological Organization (WMO), the Group on Earth Observations (GEO), the European Commission Joint Research Centre (JRC), the US Climate Variability and Predictability (CLIVAR) program, and the US National Oceanic and Atmospheric Administration (NOAA) programs on Modeling, Analysis, Predictions and Projections (MAPP) and Climate Variability & Predictability (CVP). NASA/JPL hosted the workshop with logistical support provided by the GEWEX program office. The goal of the workshop was to build on past Global Drought Information System (GDIS) progress toward developing an experimental global drought information system. Specific goals were threefold: (i) to review recent research results focused on understanding drought mechanisms and their predictability on a wide range of time scales and to identify gaps in understanding that could be addressed by coordinated research; (ii) to help ensure that WRCP research priorities mesh with efforts to build capacity to address drought at the regional level; and (iii) to produce an implementation plan for a short duration pilot project to demonstrate current GDIS capabilities. See http://www.wcrp-climate.org/gdis-wkshp-2014-objectives for more information.

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

    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

  7. Combined assimilation of screen-level observations and radar-derived precipitation for soil moisture analysis

    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

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

    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.

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

    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.

  10. Insight into the Global Carbon Cycle from Assimilation of Satellite CO2 measurements

    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.

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

    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.

  12. Assimilation of Earth rotation parameters into a global ocean model (FESOM)

    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.

  13. Evaluation of the Tropical Pacific Observing System from the Data Assimilation Perspective

    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

  14. Global surface wind and flux fields from model assimilation of Seasat data

    Atlas, R.; Busalacchi, A. J.; Kalnay, E.; Bloom, S.; Ghil, M.

    1986-01-01

    Procedures for dealiasing Seasat data and developing global surface wind and latent and sensible heat flux fields are discussed. Seasat data from September 20, 1978 was dealiased using the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system. The wind data obtained with the objective GLA forecast model are compared to the data subjectively dealiased by Peteherych et al. (1984) and Hoffman (1982, 1984). The GLA procedure is also verified using simulated Seasat data. The areas of high and low heat fluxes and cyclonic and anticyclonic wind stresses detected in the generated fields are analyzed and compared to climatological fields. It is observed that there is good correlation between the time-averaged analyses of wind stress obtained subjectively and objectively, and the monthly mean wind stress and latent fluxes agree with climatological fields and atmospheric and oceanic features.

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

    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

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

    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.

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

    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...

  18. Data Assimilation of Dead Fuel Moisture Observations from Remote automated Weather Stations

    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

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

    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. Global Warming: Evidence from Satellite Observations

    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.

  1. National contributions to observed global warming

    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)

  2. Assimilation of Wave Imaging Radar Observations for Real-Time Wave-by-Wave Forecasting

    Haller, M. C.; Simpson, A. J.; Walker, D. T.; Lynett, P. J.; Pittman, R.; Honegger, D.

    2016-02-01

    It has been shown in various studies that a controls system can dramatically improve Wave Energy Converter (WEC) power production by tuning the device's oscillations to the incoming wave field, as well as protect WEC devices by decoupling them in extreme wave conditions. A requirement of the most efficient controls systems is a phase-resolved, "deterministic" surface elevation profile, alerting the device to what it will experience in the near future. The current study aims to demonstrate a deterministic method of wave forecasting through the pairing of an X-Band marine radar with a predictive Mild Slope Equation (MSE) wave model. Using the radar as a remote sensing technique, the wave field up to 1-4 km surrounding a WEC device can be resolved. Individual waves within the radar scan are imaged through the contrast between high intensity wave faces and low intensity wave troughs. Using a recently developed method, radar images are inverted into the radial component of surface slope, shown in the figure provided using radar data from Newport, Oregon. Then, resolved radial slope images are assimilated into the MSE wave model. This leads to a best-fit model hindcast of the waves within the domain. The hindcast is utilized as an initial condition for wave-by-wave forecasting with a target forecast horizon of 3-5 minutes (tens of wave periods). The methodology is currently being tested with synthetic data and comparisons with field data are imminent.

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

    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.

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

    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.

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

    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.

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

    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

  7. Global Land Data Assimilation System (GLDAS) Products, Services and Application from NASA Hydrology Data and Information Services Center (HDISC)

    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.

  8. Using isotopes for global warming observation

    Namata, K.

    2002-01-01

    This paper, based on a literature review, discusses the main aspects of using isotopic techniques to obtain information about global warming. The rapid increase concentration of carbon dioxide (CO 2 ) and methane (CH 4 ) in the atmosphere will result in global warming by the greenhouse effect, and the isotopic techniques constitute an efficient tool to explain this complex environmental phenomenon. (author)

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

    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.

  10. The Global Emergency Observation and Warning System

    Bukley, Angelia P.; Mulqueen, John A.

    1994-01-01

    Based on an extensive characterization of natural hazards, and an evaluation of their impacts on humanity, a set of functional technical requirements for a global warning and relief system was developed. Since no technological breakthroughs are required to implement a global system capable of performing the functions required to provide sufficient information for prevention, preparedness, warning, and relief from natural disaster effects, a system is proposed which would combine the elements of remote sensing, data processing, information distribution, and communications support on a global scale for disaster mitigation.

  11. A case for variational geomagnetic data assimilation: insights from a one-dimensional, nonlinear, and sparsely observed MHD system

    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.

  12. Exploring image data assimilation in the prospect of high-resolution satellite oceanic observations

    Durán Moro, Marina; Brankart, Jean-Michel; Brasseur, Pierre; Verron, Jacques

    2017-07-01

    Satellite sensors increasingly provide high-resolution (HR) observations of the ocean. They supply observations of sea surface height (SSH) and of tracers of the dynamics such as sea surface salinity (SSS) and sea surface temperature (SST). In particular, the Surface Water Ocean Topography (SWOT) mission will provide measurements of the surface ocean topography at very high-resolution (HR) delivering unprecedented information on the meso-scale and submeso-scale dynamics. This study investigates the feasibility to use these measurements to reconstruct meso-scale features simulated by numerical models, in particular on the vertical dimension. A methodology to reconstruct three-dimensional (3D) multivariate meso-scale scenes is developed by using a HR numerical model of the Solomon Sea region. An inverse problem is defined in the framework of a twin experiment where synthetic observations are used. A true state is chosen among the 3D multivariate states which is considered as a reference state. In order to correct a first guess of this true state, a two-step analysis is carried out. A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a reduced-order Kalman filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis. Experiments show that the proposed technique succeeds in correcting a multivariate state using meso-scale and submeso-scale information contained in HR SSH and image structure observations. It also demonstrates how the surface information can be used to reconstruct the ocean state below

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

    Fang, H.; Kato, H.; Rodell, M.; Teng, W. L.; Vollmer, B. E.

    2008-12-01

    The Global Land Data Assimilation System (GLDAS) has been 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 GLDAS data hosted at HDISC include a set of 1.0° data products, covering 1979 to the present, from the four models and a 0.25° data product, covering 2000 to the present, from the Noah model. In addition to the basic anonymous ftp data downloading, users can avail themselves of several advanced data search and downloading services, such as Mirador and OPeNDAP. Mirador is a Google-based search tool that provides keywords searching, on-the-fly spatial and parameter subsetting of selected data. OPeNDAP (Open-source Project for a Network Data Access Protocol) enables remote OPeNDAP clients to access OPeNDAP served data regardless of local storage format. Additional data services to be available in the near future from HDISC include (1) on-the-fly converter of GLDAS to NetCDF and binary data formats; (2) temporal aggregation of GLDAS files; and (3) Giovanni, an online visualization and analysis tool that provides a simple way to visualize, analyze, and access vast amounts of data without having to download the data.

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

    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.

  15. Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests

    Smallman, T. L.; Exbrayat, J.-F.; Mencuccini, M.; Bloom, A. A.; Williams, M.

    2017-03-01

    Forest carbon sink strengths are governed by plant growth, mineralization of dead organic matter, and disturbance. Across landscapes, remote sensing can provide information about aboveground states of forests and this information can be linked to models to estimate carbon cycling in forests close to steady state. For aggrading forests this approach is more challenging and has not been demonstrated. Here we apply a Bayesian approach, linking a simple model to a range of data, to evaluate their information content, for two aggrading forests. We compare high information content analyses using local observations with retrievals using progressively sparser remotely sensed information (repeated, single, and no woody biomass observations). The net biome productivity of both forests is constrained to be a net sink with litter dynamics at one forest, while at the second forest total dead organic matter estimates are within observational uncertainty. The uncertainty of retrieved ecosystem traits in the repeated biomass analysis is reduced by up to 50% compared to analyses with less biomass information. This study quantifies the importance of repeated woody observations in constraining the dynamics of both wood and dead organic matter, highlighting the benefit of proposed remote sensing missions.

  16. Role of subsurface physics in the assimilation of surface soil moisture observations

    Soil moisture controls the exchange of water and energy between the land surface and the atmosphere and exhibits memory that may be useful for climate prediction at monthly time scales. Though spatially distributed observations of soil moisture are increasingly becoming available from remotely sense...

  17. Hybrid algorithm of ensemble transform and importance sampling for assimilation of non-Gaussian observations

    Shin'ya Nakano

    2014-05-01

    Full Text Available A hybrid algorithm that combines the ensemble transform Kalman filter (ETKF and the importance sampling approach is proposed. Since the ETKF assumes a linear Gaussian observation model, the estimate obtained by the ETKF can be biased in cases with nonlinear or non-Gaussian observations. The particle filter (PF is based on the importance sampling technique, and is applicable to problems with nonlinear or non-Gaussian observations. However, the PF usually requires an unrealistically large sample size in order to achieve a good estimation, and thus it is computationally prohibitive. In the proposed hybrid algorithm, we obtain a proposal distribution similar to the posterior distribution by using the ETKF. A large number of samples are then drawn from the proposal distribution, and these samples are weighted to approximate the posterior distribution according to the importance sampling principle. Since the importance sampling provides an estimate of the probability density function (PDF without assuming linearity or Gaussianity, we can resolve the bias due to the nonlinear or non-Gaussian observations. Finally, in the next forecast step, we reduce the sample size to achieve computational efficiency based on the Gaussian assumption, while we use a relatively large number of samples in the importance sampling in order to consider the non-Gaussian features of the posterior PDF. The use of the ETKF is also beneficial in terms of the computational simplicity of generating a number of random samples from the proposal distribution and in weighting each of the samples. The proposed algorithm is not necessarily effective in case that the ensemble is located distant from the true state. However, monitoring the effective sample size and tuning the factor for covariance inflation could resolve this problem. In this paper, the proposed hybrid algorithm is introduced and its performance is evaluated through experiments with non-Gaussian observations.

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

    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.

  19. Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

    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

  20. Temporal Reference, Attentional Modulation, and Crossmodal Assimilation

    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.

  1. Observing the continental-scale carbon balance: assessment of sampling complementarity and redundancy in a terrestrial assimilation system by means of quantitative network design

    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...

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

    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

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

    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

  4. An efficient method of exploring simulation models by assimilating literature and biological observational data.

    Hasegawa, Takanori; Nagasaki, Masao; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

    2014-07-01

    Recently, several biological simulation models of, e.g., gene regulatory networks and metabolic pathways, have been constructed based on existing knowledge of biomolecular reactions, e.g., DNA-protein and protein-protein interactions. However, since these do not always contain all necessary molecules and reactions, their simulation results can be inconsistent with observational data. Therefore, improvements in such simulation models are urgently required. A previously reported method created multiple candidate simulation models by partially modifying existing models. However, this approach was computationally costly and could not handle a large number of candidates that are required to find models whose simulation results are highly consistent with the data. In order to overcome the problem, we focused on the fact that the qualitative dynamics of simulation models are highly similar if they share a certain amount of regulatory structures. This indicates that better fitting candidates tend to share the basic regulatory structure of the best fitting candidate, which can best predict the data among candidates. Thus, instead of evaluating all candidates, we propose an efficient explorative method that can selectively and sequentially evaluate candidates based on the similarity of their regulatory structures. Furthermore, in estimating the parameter values of a candidate, e.g., synthesis and degradation rates of mRNA, for the data, those of the previously evaluated candidates can be utilized. The method is applied here to the pharmacogenomic pathways for corticosteroids in rats, using time-series microarray expression data. In the performance test, we succeeded in obtaining more than 80% of consistent solutions within 15% of the computational time as compared to the comprehensive evaluation. Then, we applied this approach to 142 literature-recorded simulation models of corticosteroid-induced genes, and consequently selected 134 newly constructed better models. The

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

    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.

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

    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.

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

    2011-02-16

    141 Laboratoire des Ecoulements Geophysiques et Industriels, CNRS, BP53, 38041 Grenoble Cedex 9, France, Bernard. Barnier(a).hmg. inpg. fr "’’NOAA...coaps.fsu.vdu "" Laboratoire des Ecoulements Geophysiques et Indus triels, CNRS, BP53, 38041 Grenoble Cedex 9, France, Pierre. Brasseurfdi.hmg. inpg. fr m

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

    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

  9. Astronaut observations of global biomass burning

    Wood, C.A.; Nelson, R.

    1991-01-01

    One of the most fundamental inputs for understanding and modeling possible effects of biomass burning is knowledge of the size of the area burned. Because the burns are often very large and occur on all continents (except Antarctica), observations from space are essential. Information is presented in this chapter on another method for monitoring biomass burning, including immediate and long-term effects. Examples of astronaut photography of burning during one year give a perspective of the widespread occurrence of burning and the variety of biological materials that are consumed. The growth of burning in the Amazon region is presented over 15 years using smoke as a proxy for actual burning. Possible climate effects of smoke palls are also discussed

  10. 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

    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)

  11. 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)

    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

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

    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.

  13. A study on assimilating potential vorticity data

    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.

  14. Earth Observation of Vegetation Dynamics in Global Drylands

    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...

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

    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

  16. Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System

    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.

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

    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)

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

    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

  19. 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

    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

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

    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.

  1. 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

    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

  2. Modeling of 2008 Kasatochi Volcanic Sulfate Direct Radiative Forcing: Assimilation of OMI SO2 Plume Height Data and Comparison with MODIS and CALIOP Observations

    Wang, J.; Park, S.; Zeng, J.; Ge, C.; Yang, K.; Carn, S.; Krotkov, N.; Omar, A. H.

    2013-01-01

    Volcanic SO2 column amount and injection height retrieved from the Ozone Monitoring Instrument (OMI) with the Extended Iterative Spectral Fitting (EISF) technique are used to initialize a global chemistry transport model (GEOS-Chem) to simulate the atmospheric transport and lifecycle of volcanic SO2 and sulfate aerosol from the 2008 Kasatochi eruption, and to subsequently estimate the direct shortwave, top-of-the-atmosphere radiative forcing of the volcanic sulfate aerosol. Analysis shows that the integrated use of OMI SO2 plume height in GEOS-Chem yields: (a) good agreement of the temporal evolution of 3-D volcanic sulfate distributions between model simulations and satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP), and (b) an e-folding time for volcanic SO2 that is consistent with OMI measurements, reflecting SO2 oxidation in the upper troposphere and stratosphere is reliably represented in the model. However, a consistent (approx. 25 %) low bias is found in the GEOS-Chem simulated SO2 burden, and is likely due to a high (approx.20 %) bias of cloud liquid water amount (as compared to the MODIS cloud product) and the resultant stronger SO2 oxidation in the GEOS meteorological data during the first week after eruption when part of SO2 underwent aqueous-phase oxidation in clouds. Radiative transfer calculations show that the forcing by Kasatochi volcanic sulfate aerosol becomes negligible 6 months after the eruption, but its global average over the first month is -1.3W/sq m, with the majority of the forcing-influenced region located north of 20degN, and with daily peak values up to -2W/sq m on days 16-17. Sensitivity experiments show that every 2 km decrease of SO2 injection height in the GEOS-Chem simulations will result in a approx.25% decrease in volcanic sulfate forcing; similar sensitivity but opposite sign also holds for a 0.03 m increase of geometric radius of

  3. 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

    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.

  4. Contextualizing the global relevance of local land change observations

    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

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

    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.

  6. Chemical data assimilation of geostationary aerosol optical depth and PM surface observations on regional aerosol modeling over the Korean Peninsula during KORUS-AQ campaign

    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.

  7. Assessing the Global Extent of Rivers Observable by SWOT

    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

  8. Coordination and Integration of Global Ocean Observing through JCOMM

    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.

  9. 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

    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

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

    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

  11. Development Of A Data Assimilation Capability For RAPID

    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.

  12. Comments on "Atlantic Tropical Cyclogenetic Processes during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System"

    Braun, Scott A.

    2010-01-01

    Considerable attention has been given to the potential negative impacts of the Saharan air layer (SAL) in recent years. Researchers recently raised questions about the negative impacts of Dunion and Velden and other studies in terms of storms that reached at least tropical storm strength and suggested that the SAL was an intrinsic part of the tropical cyclone environment for both storms that weaken after formation and those that intensify. Braun also suggested that several incorrect assumptions underlie many of the studies on the negative impacts of the SAL, including assumptions that most low-to-midlevel dry tropical air is SAL air, that the SAL is dry throughout its depth, and that the proximity of the SAL to storms struggling to intensify implies some role in that struggle. The recent paper by Reale et al.(RL1) is an example of the problems inherent in some of these assumptions. In their paper, RL1 analyze a simulation from the Global Earth Observing System (GEOS-5) global model and describe an extensive tongue of warm, dry air that stretches southward from at least 30 deg N (the northern limit of their plots) and wraps into a low pressure system during the period 26-29 August 2006, suppressing convection and possibly development of the African easterly wave associated with that low pressure system. They attributed the warm, dry tongue to the SAL (i.e., heating of the air mass during passage over the Sahara and radiative warming of the dust layer). Whether it was their intention, the implication is that this entire feature is due solely to the SAL and not to other possible sources of dry air or warmth. In addition, they suggested that a cool tongue of air in the boundary layer located directly beneath the elevated warm, dry tongue (forming a thermal dipole) was possibly the result of reduced solar radiation caused by an overlying dust layer. They stated that "the cool anomaly in the lower levels does not have any plausible explanation relying only on transport

  13. Global Night-Time Lights for Observing Human Activity

    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.

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

    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.

  15. Displacement data assimilation

    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.

  16. Technical Report Series on Global Modeling and Data Assimilation. Volume 31; Global Surface Ocean Carbon Estimates in a Model Forced by MERRA

    Gregg, Watson W.; Casey, Nancy W.; Rousseaux, Cecile S.

    2013-01-01

    MERRA products were used to force an established ocean biogeochemical model to estimate surface carbon inventories and fluxes in the global oceans. The results were compared to public archives of in situ carbon data and estimates. The model exhibited skill for ocean dissolved inorganic carbon (DIC), partial pressure of ocean CO2 (pCO2) and air-sea fluxes (FCO2). The MERRA-forced model produced global mean differences of 0.02% (approximately 0.3 microns) for DIC, -0.3% (about -1.2 (micro) atm; model lower) for pCO2, and -2.3% (-0.003 mol C/sq m/y) for FCO2 compared to in situ estimates. Basin-scale distributions were significantly correlated with observations for all three variables (r=0.97, 0.76, and 0.73, P<0.05, respectively for DIC, pCO2, and FCO2). All major oceanographic basins were represented as sources to the atmosphere or sinks in agreement with in situ estimates. However, there were substantial basin-scale and local departures.

  17. Sharing Data in the Global Ocean Observing System (Invited)

    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

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

    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.

  19. Preliminary Evaluation of Influence of Aerosols on the Simulation of Brightness Temperature in the NASA's Goddard Earth Observing System Atmospheric Data Assimilation System

    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.

  20. Global distribution of pauses observed with satellite measurements

    We present global distribution of altitudes and temperatures of these pauses observed with long-term space borne high- ... metries between northern and southern hemispheres continue up to the mesopause. We analyze ..... the mean temperature increases from the equa- .... monsoon circulation causes zonal asymmetry in.

  1. The Global Geodetic Observing System: Recent Activities and Accomplishments

    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

  2. Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

    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

  3. The Crc global regulator inhibits the Pseudomonas putida pWW0 toluene/xylene assimilation pathway by repressing the translation of regulatory and structural genes.

    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.

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

    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.

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

    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.

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

    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

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

    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.

  8. Global Trends of Tropospheric NO2 Observed From Space

    Schneider, P.; van der A, R. J.

    2012-04-01

    Nitrogen Dioxide (NO2) is one of the major atmospheric pollutants and is primarily emitted by industrial activity and transport. While observations of NO2 are frequently being carried out at air quality stations, such measurements are not able to provide a global perspective of spatial patterns in NO2 concentrations and their associated trends due to the stations' limited spatial representativity and an extremely sparse and often completely non-existent station coverage in developing countries. Satellite observations of tropospheric NO2 are able to overcome this issue and provide an unprecedented global view of spatial patterns in NO2 levels and due to their homogeneity are well suited for studying trends. Here we present results of a global trend analysis from nearly a decade of NO2 observations made by the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) instrument onboard the Envisat satellite platform. Using only SCIAMACHY data allows for mapping global and regional trends at an unprecedented spatial resolution since no aggregation to the coarser resolution of other sensors is necessary. Monthly average tropospheric NO2 column data was acquired for the period between August 2002 and August 2011. A trend analysis was subsequently performed by fitting a statistical model including a seasonal cycle and linear trend to the time series extracted at each grid cell. The linear trend component and the trend uncertainty were then mapped spatially at both regional and global scales. The results show that spatially contiguous areas of significantly increasing NO2 levels are found primarily in Eastern China, with absolute trends of up to 4.05 (± 0.41) - 1015 molecules cm-2 yr-1 at the gridcell level and large areas showing rapid relative increases of 10-20 percent per year. In addition, many urban agglomerations in Asia and the Middle East similarly exhibit significantly increasing trends, with Dhaka in Bangladesh being the megacity with

  9. The Global Ocean Observing System (GOOS): New developments

    Summerhayes, C.P.

    1999-01-01

    GOOS will provide information about the present and future states of seas and oceans and their living resources, and on the role of the oceans in climate change. Among other things, it will include monitoring the extent to which the sea is polluted, and applying models enabling the behaviour of polluted environments to be forecast given a variety of forcing conditions including anthropogenic and natural changes. Implementation has begun through integration of previously separate existing observing systems into a GOOS Initial Observing System, and through the development of Pilot Projects, most notably in the coastal seas of Europe and North-east Asia. Although the present emphasis is on the measurement of physical properties, plans are underway for increasing the observation of chemical and biological parameters. The main biological thrust at present comes through the Global Coral Reef Monitoring Network (GCRMN). Consideration needs to be given to incorporation into the GOOS Initial Observing System of present national, international and global chemical and biological monitoring systems, and the development and implementation of new chemical and biological monitoring subsystems, especially in coastal seas for monitoring the health of those environments. GOOS will offer marine scientists and other users a scheme of continuing measurements on a scale larger in time and space than can be accomplished by individuals for their own applications, and a vastly improved store of basic marine environmental data for a multitude of purposes. For GOOS news see the GOOS Homepage at http://ioc.unesco.org/GOOS/. (author)

  10. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

    Lopez, Patricia Lopez; Wanders, Niko; Schellekens, Jaap; Renzullo, Luigi J.; Sutanudjaja, Edwin H.; Bierkens, Marc F. P.

    2016-01-01

    The coarse spatial resolution of global hydrological models (typically > 0.25◦ ) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible

  11. Technical Report Series on Global Modeling and Data Assimilation. Volume 32; Estimates of AOD Trends (2002 - 2012) Over the World's Major Cities Based on the MERRA Aerosol Reanalysis

    Provencal, Simon; Kishcha, Pavel; Elhacham, Emily; daSilva, Arlindo M.; Alpert, Pinhas; Suarez, Max J.

    2014-01-01

    NASA's Global Modeling and Assimilation Office has extended the Modern-Era Retrospective Analysis for Research and Application (MERRA) tool with five atmospheric aerosol species (sulfates, organic carbon, black carbon, mineral dust and sea salt). This inclusion of aerosol reanalysis data is now known as MERRAero. This study analyses a ten-year period (July 2002 - June 2012) MERRAero aerosol reanalysis applied to the study of aerosol optical depth (AOD) and its trends for the aforementioned aerosol species over the world's major cities (with a population of over 2 million inhabitants). We found that a proportion of various aerosol species in total AOD exhibited a geographical dependence. Cities in industrialized regions (North America, Europe, central and eastern Asia) are characterized by a strong proportion of sulfate aerosols. Organic carbon aerosols are dominant over cities which are located in regions where biomass burning frequently occurs (South America and southern Africa). Mineral dust dominates other aerosol species in cities located in proximity to the major deserts (northern Africa and western Asia). Sea salt aerosols are prominent in coastal cities but are dominant aerosol species in very few of them. AOD trends are declining over cities in North America, Europe and Japan, as a result of effective air quality regulation. By contrast, the economic boom in China and India has led to increasing AOD trends over most cities in these two highly-populated countries. Increasing AOD trends over cities in the Middle East are caused by increasing desert dust.

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

    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.

  13. Fine structure of sprites and proposed global observations

    Mende, S.B; Frey, H.U.; Rairden, R.l.

    2002-01-01

    structures of columniform sprites (C sprites) consisted of slant directed, nearly vertically aligned columns of intense pinpoint like beads. The distance of the sprites from the observer was measured and the altitude and vertical spacing of the beads were estimated. The distribution of beads showed...... bore-sighted photometers. The imager will locate the sprites near the earth limb and make global synoptic measurements while the photometers will measure the spectral and temporal properties of sprites and other upper atmospheric luminous phenomena in a number of different wavelength regions...

  14. Observational constraints on the global atmospheric budget of ethanol

    V. Naik

    2010-06-01

    Full Text Available Energy security and climate change concerns have led to the promotion of biomass-derived ethanol, an oxygenated volatile organic compound (OVOC, as a substitute for fossil fuels. Although ethanol is ubiquitous in the troposphere, our knowledge of its current atmospheric budget and distribution is limited. Here, for the first time we use a global chemical transport model in conjunction with atmospheric observations to place constraints on the ethanol budget, noting that additional measurements of ethanol (and its precursors are still needed to enhance confidence in our estimated budget. Global sources of ethanol in the model include 5.0 Tg yr−1 from industrial sources and biofuels, 9.2 Tg yr−1 from terrestrial plants, ~0.5 Tg yr−1 from biomass burning, and 0.05 Tg yr−1 from atmospheric reactions of the ethyl peroxy radical (C2H5O2 with itself and with the methyl peroxy radical (CH3O2. The resulting atmospheric lifetime of ethanol in the model is 2.8 days. Gas-phase oxidation by the hydroxyl radical (OH is the primary global sink of ethanol in the model (65%, followed by dry deposition (25%, and wet deposition (10%. Over continental areas, ethanol concentrations predominantly reflect direct anthropogenic and biogenic emission sources. Uncertainty in the biogenic ethanol emissions, estimated at a factor of three, may contribute to the 50% model underestimate of observations in the North American boundary layer. Current levels of ethanol measured in remote regions are an order of magnitude larger than those in the model, suggesting a major gap in understanding. Stronger constraints on the budget and distribution of ethanol and OVOCs are a critical step towards assessing the impacts of increasing the use of ethanol as a fuel.

  15. Changes in observed climate extremes in global urban areas

    Mishra, Vimal; Ganguly, Auroop R; Nijssen, Bart; Lettenmaier, Dennis P

    2015-01-01

    Climate extremes have profound implications for urban infrastructure and human society, but studies of observed changes in climate extremes over the global urban areas are few, even though more than half of the global population now resides in urban areas. Here, using observed station data for 217 urban areas across the globe, we show that these urban areas have experienced significant increases (p-value <0.05) in the number of heat waves during the period 1973–2012, while the frequency of cold waves has declined. Almost half of the urban areas experienced significant increases in the number of extreme hot days, while almost 2/3 showed significant increases in the frequency of extreme hot nights. Extreme windy days declined substantially during the last four decades with statistically significant declines in about 60% in the urban areas. Significant increases (p-value <0.05) in the frequency of daily precipitation extremes and in annual maximum precipitation occurred at smaller fractions (17 and 10% respectively) of the total urban areas, with about half as many urban areas showing statistically significant downtrends as uptrends. Changes in temperature and wind extremes, estimated as the result of a 40 year linear trend, differed for urban and non-urban pairs, while changes in indices of extreme precipitation showed no clear differentiation for urban and selected non-urban stations. (letter)

  16. Coupled assimilation for an intermediated coupled ENSO prediction model

    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.

  17. Improving carbon model phenology using data assimilation

    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

  18. NOAA's Role in Sustaining Global Ocean Observations: Future Plans for OAR's Ocean Observing and Monitoring Division

    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.

  19. Space observations for global and regional studies of the biosphere

    Cihlar, J.; Li, Z.; Chen, J.; Sellers, P.; Hall, F.

    1994-01-01

    The capability to make space-based measurements of Earth at high spatial and temporal resolutions, which would not otherwise be economically or practically feasible, became available just in time to contribute to scientific understanding of the interactive processes governing the total Earth system. Such understanding has now become essential in order to take practical steps which would counteract or mitigate the pervasive impact of the growing human population on the future habitability of the Earth. The paper reviews the rationale for using space observations for studies of climate and terrestrial ecosystems at global and regional scales, as well as the requirements for such observations for studies of climate and ecosystem dynamics. The present status of these developments is reported along with initiatives under way to advance the use of satellite observations for Earth system studies. The most important contribution of space observations is the provision of physical or biophysical parameters for models representing various components of the Earth system. Examples of such parameters are given for climatic and ecosystem studies.

  20. Observation of Wetland Dynamics with Global Navigation Satellite Signals Reflectometry

    Zuffada, C.; Shah, R.; Nghiem, S. V.; Cardellach, E.; Chew, C. C.

    2015-12-01

    Wetland dynamics is crucial to changes in both atmospheric methane and terrestrial water storage. The Intergovernmental Panel on Climate Change's Fifth Assessment Report (IPCC AR5) highlights the role of wetlands as a key driver of methane (CH4) emission, which is more than one order of magnitude stronger than carbon dioxide as a greenhouse gas in the centennial time scale. Among the multitude of methane emission sources (hydrates, livestock, rice cultivation, freshwaters, landfills and waste, fossil fuels, biomass burning, termites, geological sources, and soil oxidation), wetlands constitute the largest contributor with the widest uncertainty range of 177-284 Tg(CH4) yr-1 according to the IPCC estimate. Wetlands are highly susceptible to climate change that might lead to wetland collapse. Such wetland destruction would decrease the terrestrial water storage capacity and thus contribute to sea level rise, consequently exacerbating coastal flooding problems. For both methane change and water storage change, wetland dynamics is a crucial factor with the largest uncertainty. Nevertheless, a complete and consistent map of global wetlands still needs to be obtained as the Ramsar Convention calls for a wetlands inventory and impact assessment. We develop a new method for observations of wetland change using Global Navigation Satellite Signals Reflectometry (GNSS-R) signatures for global wetland mapping in synergy with the existing capability, not only as a static inventory but also as a temporal dataset, to advance the capability for monitoring the dynamics of wetland extent relevant to addressing the science issues of CH4 emission change and terrestrial water storage change. We will demonstrate the capability of the new GNSS-R method over a rice field in the Ebro Delta wetland in Spain.

  1. Global geodetic observing system meeting the requirements of a global society on a changing planet in 2020

    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.

  2. Observational evidence of changes in global snow and ice cover

    Barry, R.G.

    1990-01-01

    Sources of observational data on recent variations in the seasonal extent of snow cover and sea ice, of the terminal position and volume of alpine glaciers, and of ground temperature profiles in areas of permafrost are briefly reviewed. Recent evidence of changes in these variables is then examined. The extent of seasonal snow cover in the Northern hemisphere and of sea ice in both hemispheres has fluctuated irregularly over the last 15-20 years with a range of about 10-15% in each case. There is no clear evidence of any recent trends, despite general global warming. In contrast, most glaciers retreated and thinned from before the turn of the century until the 1960s and alaskan permafrost temperatures have risen 2-4 C per century. Recently, glacier advances have been noted, perhaps in response to increased accumulation. Problems of linking climate forcing and snow/ice responses are discussed

  3. A Global Repository for Planet-Sized Experiments and Observations

    Williams, Dean; Balaji, V.; Cinquini, Luca; Denvil, Sebastien; Duffy, Daniel; Evans, Ben; Ferraro, Robert D.; Hansen, Rose; Lautenschlager, Michael; Trenham, Claire

    2016-01-01

    Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP) output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.

  4. SONG-China Project: A Global Automated Observation Network

    Yang, Z. Z.; Lu, X. M.; Tian, J. F.; Zhuang, C. G.; Wang, K.; Deng, L. C.

    2017-09-01

    Driven by advancements in technology and scientific objectives, data acquisition in observational astronomy has been changed greatly in recent years. Fully automated or even autonomous ground-based network of telescopes has now become a tendency for time-domain observational projects. The Stellar Observations Network Group (SONG) is an international collaboration with the participation and contribution of the Chinese astronomy community. The scientific goal of SONG is time-domain astrophysics such as asteroseismology and open cluster research. The SONG project aims to build a global network of 1 m telescopes equipped with high-precision and high-resolution spectrographs, and two-channel lucky-imaging cameras. It is the Chinese initiative to install a 50 cm binocular photometry telescope at each SONG node sharing the network platform and infrastructure. This work is focused on design and implementation in technology and methodology of SONG/50BiN, a typical ground-based network composed of multiple sites and a variety of instruments.

  5. NASA's Carbon Cycle OSSE Initiative - Informing future space-based observing strategies through advanced modeling and data assimilation

    Ott, L.; Sellers, P. J.; Schimel, D.; Moore, B., III; O'Dell, C.; Crowell, S.; Kawa, S. R.; Pawson, S.; Chatterjee, A.; Baker, D. F.; Schuh, A. E.

    2017-12-01

    Satellite observations of carbon dioxide (CO2) and methane (CH4) are critically needed to improve understanding of the contemporary carbon budget and carbon-climate feedbacks. Though current carbon observing satellites have provided valuable data in regions not covered by surface in situ measurements, limited sampling of key regions and small but spatially coherent biases have limited the ability to estimate fluxes at the time and space scales needed for improved process-level understanding and informed decision-making. Next generation satellites will improve coverage in data sparse regions, either through use of active remote sensing, a geostationary vantage point, or increased swath width, but all techniques have limitations. The relative strengths and weaknesses of these approaches and their synergism have not previously been examined. To address these needs, a significant subset of the US carbon modeling community has come together with support from NASA to conduct a series of coordinated observing system simulation experiments (OSSEs), with close collaboration in framing the experiments and in analyzing the results. Here, we report on the initial phase of this initiative, which focused on creating realistic, physically consistent synthetic CO2 and CH4 observational datasets for use in inversion and signal detection experiments. These datasets have been created using NASA's Goddard Earth Observing System Model (GEOS) to represent the current state of atmospheric carbon as well as best available estimates of expected flux changes. Scenarios represented include changes in urban emissions, release of permafrost soil carbon, changes in carbon uptake in tropical and mid-latitude forests, changes in the Southern Ocean sink, and changes in both anthropogenic and natural methane emissions. This GEOS carbon `nature run' was sampled by instrument simulators representing the most prominent observing strategies with a focus on consistently representing the impacts of

  6. Global Space Weather Observational Network: Challenges and China's Contribution

    Wang, C.

    2017-12-01

    To understand space weather physical processes and predict space weather accurately, global space-borne and ground-based space weather observational network, making simultaneous observations from the Sun to geo-space (magnetosphere, ionosphere and atmosphere), plays an essential role. In this talk, we will present the advances of the Chinese space weather science missions, including the ASO-S (Advanced Space-borne Solar Observatory), MIT (Magnetosphere - Ionosphere- Thermosphere Coupling Exploration), and the ESA-China joint space weather science mission SMILE (Solar wind - Magnetosphere - Ionosphere Link Explore), a new mission to image the magnetosphere. Compared to satellites, ground-based monitors are cheap, convenient, and provide continuous real-time data. We will also introduce the Chinese Meridian Project (CMP), a ground-based program fully utilizing the geographic location of the Chinese landmass to monitor the geo-space environment. CMP is just one arm of a larger program that Chinese scientists are proposing to the international community. The International Meridian Circle Program (IMCP) for space weather hopes to connect chains of ground-based monitors at the longitudinal meridians 120 deg E and 60 deg W. IMCP takes advantage of the fact that these meridians already have the most monitors of any on Earth, with monitors in Russia, Australia, Brazil, the United States, Canada, and other countries. This data will greatly enhance the ability of scientists to monitor and predict the space weather worldwide.

  7. Measuring progress of the global sea level observing system

    Woodworth, Philip L.; Aarup, Thorkild; Merrifield, Mark; Mitchum, Gary T.; Le Provost, Christian

    Sea level is such a fundamental parameter in the sciences of oceanography geophysics, and climate change, that in the mid-1980s, the Intergovernmental Oceanographic Commission (IOC) established the Global Sea Level Observing System (GLOSS). GLOSS was to improve the quantity and quality of data provided to the Permanent Service for Mean Sea Level (PSMSL), and thereby, data for input to studies of long-term sea level change by the Intergovernmental Panel on Climate Change (IPCC). It would also provide the key data needed for international programs, such as the World Ocean Circulation Experiment (WOCE) and later, the Climate Variability and Predictability Programme (CLIVAR).GLOSS is now one of the main observation components of the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) of IOC and the World Meteorological Organization (WMO). Progress and deficiencies in GLOSS were presented in July to the 22nd IOC Assembly at UNESCO in Paris and are contained in the GLOSS Assessment Report (GAR) [IOC, 2003a].

  8. A Spatial Data Infrastructure for the Global Mercury Observation System

    Cinnirella S.

    2013-04-01

    Full Text Available The Global Mercury Observation System (GMOS Project includes a specific Work Package aimed at developing tools (i.e. databases, catalogs, services to collect GMOS datasets, harvest mercury databases, and offer services like search, view, and download spatial datasets from the GMOS portal (www.gmos.eu. The system will be developed under the framework of the Infrastructure for Spatial Information in the European Community (INSPIRE Directive and the Directive 2003/4/EC on public access to environmental information, which both aim to make relevant, harmonized, high-quality geographic information available to support the formulation, implementation, monitoring, and evaluation of policies and activities that have a direct or indirect impact on the environment. Three databases have been proposed (on emissions, field data and model results, and each will be equipped with state-of-the-art, open-source software to allow for the highest performance possible. Web-based user-interfaces and prototype applications will be developed to demonstrate the potential of blending different datasets from different servers for environmental assessment studies. Several services (i.e. catalog browsers, WMS and WCS services, web GIS services will be developed to facilitate data integration, data re-use, and data exchange within and beyond the GMOS project. Different types of measurement and model datasets provided by project partners and other sources will be integrated into PostgreSQL-PostGIS, harmonized by creating INSPIRE-compliant metadata and made available to a larger community of stakeholders, policy makers, scientists, and NGOs (as well as to other public and private institutions, as dictated by the Directive 2003/4/EC. Since interoperability is a central concept for the Global Earth Observation System of Systems (GEOSS, the Global Monitoring for Environmental and Security (GMES and the INSPIRE Directive, guidelines developed in these three frameworks will be

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

    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. Carbon and nitrogen assimilation in deep subseafloor microbial cells

    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...

  11. GLOBIL: WWF's Global Observation and Biodiversity Information Portal

    Shapiro, A. C.; Nijsten, L.; Schmitt, S.; Tibaldeschi, P.

    2015-04-01

    Despite ever increasing availability of satellite imagery and spatial data, conservation managers, decision makers and planners are often unable to analyze data without special knowledge or software. WWF is bridging this gap by putting extensive spatial data into an easy to use online mapping environment, to allow visualization, manipulation and analysis of large data sets by any user. Consistent, reliable and repeatable ecosystem monitoring information for priority eco-regions is needed to increase transparency in WWF's global conservation work, to measure conservation impact, and to provide communications with the general public and organization members. Currently, much of this monitoring and evaluation data is isolated, incompatible, or inaccessible and not readily usable or available for those without specialized software or knowledge. Launched in 2013 by WWF Netherlands and WWF Germany, the Global Observation and Biodiversity Information Portal (GLOBIL) is WWF's new platform to unite, centralize, standardize and visualize geo-spatial data and information from more than 150 active GIS users worldwide via cloud-based ArcGIS Online. GLOBIL is increasing transparency, providing baseline data for monitoring and evaluation while communicating impacts and conservation successes to the public. GLOBIL is currently being used in the worldwide marine campaign as an advocacy tool for establishing more marine protected areas, and a monitoring interface to track the progress towards ocean protection goals. In the Kavango-Zambezi (KAZA) Transfrontier Conservation area, local partners are using the platform to monitor land cover changes, barriers to species migrations, potential human-wildlife conflict and local conservation impacts in vast wildlife corridor. In East Africa, an early warning system is providing conservation practitioners with real-time alerts of threats particularly to protected areas and World Heritage Sites by industrial extractive activities. And for

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

    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

  13. Interpretation of TOMS Observations of Tropical Tropospheric Ozone with a Global Model and In Situ Observations

    Martin, Randall V.; Jacob, Daniel J.; Logan, Jennifer A.; Bey, Isabelle; Yantosca, Robert M.; Staudt, Amanda C.; Fiore, Arlene M.; Duncan, Bryan N.; Liu, Hongyu; Ginoux, Paul

    2004-01-01

    We interpret the distribution of tropical tropospheric ozone columns (TTOCs) from the Total Ozone Mapping Spectrometer (TOMS) by using a global three-dimensional model of tropospheric chemistry (GEOS-CHEM) and additional information from in situ observations. The GEOS-CHEM TTOCs capture 44% of the variance of monthly mean TOMS TTOCs from the convective cloud differential method (CCD) with no global bias. Major discrepancies are found over northern Africa and south Asia where the TOMS TTOCs do not capture the seasonal enhancements from biomass burning found in the model and in aircraft observations. A characteristic feature of these northern topical enhancements, in contrast to southern tropical enhancements, is that they are driven by the lower troposphere where the sensitivity of TOMS is poor due to Rayleigh scattering. We develop an efficiency correction to the TOMS retrieval algorithm that accounts for the variability of ozone in the lower troposphere. This efficiency correction increases TTOC's over biomass burning regions by 3-5 Dobson units (DU) and decreases them by 2-5 DU over oceanic regions, improving the agreement between CCD TTOCs and in situ observations. Applying the correction to CCD TTOCs reduces by approximately DU the magnitude of the "tropical Atlantic paradox" [Thompson et al, 2000], i.e. the presence of a TTOC enhancement over the southern tropical Atlantic during the northern African biomass burning season in December-February. We reproduce the remainder of the paradox in the model and explain it by the combination of upper tropospheric ozone production from lightning NOx, peristent subsidence over the southern tropical Atlantic as part of the Walker circulation, and cross-equatorial transport of upper tropospheric ozone from northern midlatitudes in the African "westerly duct." These processes in the model can also account for the observed 13-17 DU persistent wave-1 pattern in TTOCs with a maximum above the tropical Atlantic and a minimum

  14. Regional Ocean Data Assimilation

    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.

  15. Observed magnified runoff response to rainfall intensification under global warming

    Huang, Jr-Chuan; Lee, Tsung-Yu; Lee, Jun-Yi

    2014-01-01

    Runoff response to rainfall intensification under global warming is crucial, but is poorly discussed due to the limited data length and human alteration. Historical rainfall and runoff records in pristine catchments in Taiwan were investigated through trend analysis and cross temperature difference analysis. Trend analysis showed that both rainfall and runoff in the 99.9-percentile have been significantly increasing in terms of frequency and intensity over the past four decades. Cross temperature difference analysis quantified that the rainfall and runoff extremes (including the 99.0–99.9-percentiles) may increase by 69.5% and 99.8%, respectively, under a future scenario of 1  ° C increase in temperature. This increase in intensity resembles the increase in intensity observed between 1971–1990 and 1991–2010. The amplified runoff response can be related to the limited catchment storage capacity being preoccupied by rainfall extremes. The quantified temperature effect on rainfall and runoff intensification can be a strong basis for designing scenarios, confirming and fusing GCMs’ results. In addition, the runoff amplification should be a warning for other regions with significant rainfall intensification. Appropriate strategies are indispensable and urgently needed to maintain and protect the development of societies. (paper)

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

    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).

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

    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

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

    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.

  19. Global synthesis of long-term cloud condensation nuclei observations

    Schmale, Julia; Henning, Silvia; Stratmann, Frank; Henzing, Bas; Schlag, Patrick; Aalto, Pasi; Keskinen, Helmi; Sellegri, Karine; Ovadnevaite, Jurgita; Krüger, Mira; Jefferson, Anne; Whitehead, James; Carslaw, Ken; Yum, Seong Soo; Kristensson, Adam; Baltensperger, Urs; Gysel, Martin

    2016-04-01

    Cloud condensation nuclei (CCN) are aerosol particles with the ability to activate into droplets at a given super saturation and therefore influence the microphysical and optical properties of clouds. To predict cloud radiative properties understanding the spatial and temporal variability of CCN concentrations in different environments is important. However, currently, the effects of atmospheric particles on changes in cloud radiative forcing are still the largest contribution of uncertainty in climate forcing prediction (IPCC, 2013). Numerous intensive field campaigns have already explored detailed characteristics of CCN in many locations around the world. However, these rather short-term observations can generally not address seasonal or inter-annual variations and a comparison between campaign sites is difficult due to the higher influence of specific environmental circumstances on short-term measurements results. Here, we present results of more long-term CCN and aerosol number concentrations as well as size distribution data covering at least one full year between 2006 and 2014. The 12 locations include ACTRIS stations (http://www.actris.net/) in Europe, and further sites in North America, Brazil and Korea. The sites are located in different environments allowing for temporal and spatial characterization of CCN variability in different atmospheric regimes. Those include marine, remote-continental, boreal forest, rain forest, Arctic and monsoon-influenced environments, as well as boundary layer and free tropospheric conditions. The aerosol populations and their activation behavior show significant differences across the stations. While peak concentrations of CCN are observed in summer at the high altitude sites, in the Arctic the highest concentrations occur during the Haze period in spring. The rural-marine and rural-continental sites exhibit similar CCN concentration characteristics with a relatively flat annual cycle. At some stations, e.g. in the boreal

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

    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

  1. Modelling 1-minute directional observations of the global irradiance.

    Thejll, Peter; Pagh Nielsen, Kristian; Andersen, Elsa; Furbo, Simon

    2016-04-01

    Direct and diffuse irradiances from the sky has been collected at 1-minute intervals for about a year from the experimental station at the Technical University of Denmark for the IEA project "Solar Resource Assessment and Forecasting". These data were gathered by pyrheliometers tracking the Sun, as well as with apertured pyranometers gathering 1/8th and 1/16th of the light from the sky in 45 degree azimuthal ranges pointed around the compass. The data are gathered in order to develop detailed models of the potentially available solar energy and its variations at high temporal resolution in order to gain a more detailed understanding of the solar resource. This is important for a better understanding of the sub-grid scale cloud variation that cannot be resolved with climate and weather models. It is also important for optimizing the operation of active solar energy systems such as photovoltaic plants and thermal solar collector arrays, and for passive solar energy and lighting to buildings. We present regression-based modelling of the observed data, and focus, here, on the statistical properties of the model fits. Using models based on the one hand on what is found in the literature and on physical expectations, and on the other hand on purely statistical models, we find solutions that can explain up to 90% of the variance in global radiation. The models leaning on physical insights include terms for the direct solar radiation, a term for the circum-solar radiation, a diffuse term and a term for the horizon brightening/darkening. The purely statistical model is found using data- and formula-validation approaches picking model expressions from a general catalogue of possible formulae. The method allows nesting of expressions, and the results found are dependent on and heavily constrained by the cross-validation carried out on statistically independent testing and training data-sets. Slightly better fits -- in terms of variance explained -- is found using the purely

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

    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.

  3. Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

    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

  4. Global measures of ionospheric electrodynamic activity inferred from combined incoherent scatter radar and ground magnetometer observations

    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

  5. Data Assimilation for Applied Meteorology

    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

  6. Global Geodetic Observing System: meeting the requirements of a global society on a changing planet in 2020

    Plag, Hans-Peter, 1952; Pearlman, Michael

    2009-01-01

    ..., Earth Observation on a global scale is at the heart of GGOS's activities, which contributes to Global Change research through the monitoring, as well as the modeling, of dynamic Earth processes such as, for example, mass and angular momentum exchanges, mass transport and ocean circulation, and changes in sea, land and ice surfaces. To achieve such a...

  7. The observed sensitivity of the global hydrological cycle to changes in surface temperature

    Arkin, Phillip A; Janowiak, John; Smith, Thomas M; Sapiano, Mathew R P

    2010-01-01

    Climate models project large changes in global surface temperature in coming decades that are expected to be accompanied by significant changes in the global hydrological cycle. Validation of model simulations is essential to support their use in decision making, but observing the elements of the hydrological cycle is challenging, and model-independent global data sets exist only for precipitation. We compute the sensitivity of the global hydrological cycle to changes in surface temperature using available global precipitation data sets and compare the results against the sensitivities derived from model simulations of 20th century climate. The implications of the results for the global climate observing system are discussed.

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

    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

  9. Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

    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

  10. Testbed model and data assimilation for ARM

    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

  11. Advanced Satellite-Derived Wind Observations, Assimilation, and Targeting Strategies during TCS-08 for Developing Improved Operational Analysis and Prediction of Western Pacific Tropical Cyclones

    2013-09-30

    TC structure evolve up to landfall or extratropical transition. In particular, winds derived from geostationary satellites have been shown to be an... extratropical transition, it is clear that a dedicated research effort is needed to optimize the satellite data processing strategies, assimilation, and...applications to better understand the behavior of the near- storm environmental flow fields during these evolutionary TC stages. To our knowledge, this

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

    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.

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

    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.

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

    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.

  15. Global distribution of pauses observed with satellite measurements

    Here we study the commonality and differences observed in the variability of all the pauses. We also examined how good other datasets will represent these features among (and in between) different satellite measurements, re-analysis, and model data. Hemispheric differences observed in all the pauses are also reported.

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

    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.

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

    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.

  18. Error Covariance Estimation of Mesoscale Data Assimilation

    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...

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

    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.

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

    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.

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

    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.

  2. Spacebased Observation of Water Balance Over Global Oceans

    Liu, W.; Xie, X.

    2008-12-01

    We demonstrated that ocean surface fresh water flux less the water discharge into the ocean from river and ice melt balances the mass loss in the ocean both in magnitude and in the phase of annual variation. The surface water flux was computed from the divergence of the water transport integrated over the depth of the atmosphere. The atmospheric water transport is estimated from the precipitable water measured by Special Sensor Microwave Imager, the surface wind vector by QuikSCAT, and the NOAA cloud drift wind through a statistical model. The transport has been extensively validated using global radiosonde and data and operational numerical weather prediction results. Its divergence has been shown to agree with the difference between evaporation estimated from the Advanced Microwave Scanning Radiometer data and the precipitation measured by Tropical Rain Measuring Mission over the global tropical and subtropical oceans both in magnitude and geographical distribution for temporal scales ranging from intraseasonal to interannual. The water loss rate in the ocean is estimated by two methods, one is from Gravity Recovery and Climate Experiment and the other is by subtracting the climatological steric change from the sea level change measured by radar altimeter on Jason. Only climatological river discharge and ice melt from in situ measurements are available and the lack of temporal variation may contribute to discrepancies in the balance. We have successfully used the spacebased surface fluxes to estimate to climatological mean heat transport in the Atlantic ocean and is attempting to estimate the meridional fresh water (or salt) transport from the surface flux. The approximate closure of the water balance gives a powerful indirect validation of the spacebased products.

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

    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...

  4. The dynamic radiation environment assimilation model (DREAM)

    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.

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

    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

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

    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.

  7. Data Assimilation in Integrated and Distributed Hydrological Models

    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...

  8. AO Distal Radius Fracture Classification: Global Perspective on Observer Agreement

    Jayakumar, Prakash; Teunis, Teun; Giménez, Beatriz Bravo; Verstreken, Frederik; Di Mascio, Livio; Jupiter, Jesse B.

    2016-01-01

    Background The primary objective of this study was to test interobserver reliability when classifying fractures by consensus by AO types and groups among a large international group of surgeons. Secondarily, we assessed the difference in inter- and intraobserver agreement of the AO classification in relation to geographical location, level of training, and subspecialty. Methods A randomized set of radiographic and computed tomographic images from a consecutive series of 96 distal radius fractures (DRFs), treated between October 2010 and April 2013, was classified using an electronic web-based portal by an invited group of participants on two occasions. Results Interobserver reliability was substantial when classifying AO type A fractures but fair and moderate for type B and C fractures, respectively. No difference was observed by location, except for an apparent difference between participants from India and Australia classifying type B fractures. No statistically significant associations were observed comparing interobserver agreement by level of training and no differences were shown comparing subspecialties. Intra-rater reproducibility was “substantial” for fracture types and “fair” for fracture groups with no difference accounting for location, training level, or specialty. Conclusion Improved definition of reliability and reproducibility of this classification may be achieved using large international groups of raters, empowering decision making on which system to utilize. Level of Evidence Level III PMID:28119795

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

    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.

  10. Skill Assessment in Ocean Biological Data Assimilation

    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. DATA ASSIMILATION APPROACH FOR FORECAST OF SOLAR ACTIVITY CYCLES

    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.

  12. Annual global tree cover estimated by fusing optical and SAR satellite observations

    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

  13. Mars Global Digital Dune Database (MGD3): Global dune distribution and wind pattern observations

    Hayward, Rosalyn K.; Fenton, Lori; Titus, Timothy N.

    2014-01-01

    The Mars Global Digital Dune Database (MGD3) is complete and now extends from 90°N to 90°S latitude. The recently released south pole (SP) portion (MC-30) of MGD3 adds ∼60,000 km2 of medium to large-size dark dune fields and ∼15,000 km2 of sand deposits and smaller dune fields to the previously released equatorial (EQ, ∼70,000 km2), and north pole (NP, ∼845,000 km2) portions of the database, bringing the global total to ∼975,000 km2. Nearly all NP dunes are part of large sand seas, while the majority of EQ and SP dune fields are individual dune fields located in craters. Despite the differences between Mars and Earth, their dune and dune field morphologies are strikingly similar. Bullseye dune fields, named for their concentric ring pattern, are the exception, possibly owing their distinctive appearance to winds that are unique to the crater environment. Ground-based wind directions are derived from slipface (SF) orientation and dune centroid azimuth (DCA), a measure of the relative location of a dune field inside a crater. SF and DCA often preserve evidence of different wind directions, suggesting the importance of local, topographically influenced winds. In general however, ground-based wind directions are broadly consistent with expected global patterns, such as polar easterlies. Intriguingly, between 40°S and 80°S latitude both SF and DCA preserve their strongest, though different, dominant wind direction, with transport toward the west and east for SF-derived winds and toward the north and west for DCA-derived winds.

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

    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.

  15. Regional Ocean Data Assimilation

    Edwards, Christopher A.; Moore, Andrew M.; Hoteit, Ibrahim; Cornuelle, Bruce D.

    2015-01-01

    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

  16. Assimilate partitioning during reproductive growth

    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

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

    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

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

    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.

  19. Global Earth Observation System of Systems: Characterizing Uncertainties of Space- based Measurements and Earth System Models Informing Decision Tools

    Birk, R. J.; Frederick, M.

    2006-05-01

    The Global Earth Observation System of Systems (GEOSS) framework identifies the benefits of systematically and scientifically networking the capacity of organizations and systems into solutions that benefit nine societal benefit areas. The U.S. Integrated Earth Observation System (IEOS), the U.S. contribution to the GEOSS, focuses on near-term, mid-term, and long-term opportunities to establish integrated system solutions based on capacities and capabilities of member agencies and affiliations. Scientists at NASA, NOAA, DOE, NSF and other U.S. agencies are evolving the predictive capacity of models of Earth processes based on space-based, airborne and surface-based instruments and their measurements. NASA research activities include advancing the power and accessibility of computational resources (i.e. Project Columbia) to enable robust science data analysis, modeling, and assimilation techniques to rapidly advance. The integration of the resulting observations and predictions into decision support tools require characterization of the accuracies of a range of input measurements includes temperature and humidity profiles, wind speed, ocean height, sea surface temperature, and atmospheric constituents that are measured globally by U.S. deployed spacecraft. These measurements are stored in many data formats on many different information systems with widely varying accessibility and have processes whose documentation ranges from extremely detailed to very minimal. Integrated and interdisciplinary modeling (enabled by the Earth System Model Framework) enable the types of ensemble analysis that are useful for decision processes associated with energy management, public health risk assessments, and optimizing transportation safety and efficiency. Interdisciplinary approaches challenge systems integrators (both scientists and engineers) to expand beyond the traditional boundaries of particular disciplines to develop, verify and validate, and ultimately benchmark the

  20. Parameter estimation for a cohesive sediment transport model by assimilating satellite observations in the Hangzhou Bay: Temporal variations and spatial distributions

    Wang, Daosheng; Zhang, Jicai; He, Xianqiang; Chu, Dongdong; Lv, Xianqing; Wang, Ya Ping; Yang, Yang; Fan, Daidu; Gao, Shu

    2018-01-01

    Model parameters in the suspended cohesive sediment transport models are critical for the accurate simulation of suspended sediment concentrations (SSCs). Difficulties in estimating the model parameters still prevent numerical modeling of the sediment transport from achieving a high level of predictability. Based on a three-dimensional cohesive sediment transport model and its adjoint model, the satellite remote sensing data of SSCs during both spring tide and neap tide, retrieved from Geostationary Ocean Color Imager (GOCI), are assimilated to synchronously estimate four spatially and temporally varying parameters in the Hangzhou Bay in China, including settling velocity, resuspension rate, inflow open boundary conditions and initial conditions. After data assimilation, the model performance is significantly improved. Through several sensitivity experiments, the spatial and temporal variation tendencies of the estimated model parameters are verified to be robust and not affected by model settings. The pattern for the variations of the estimated parameters is analyzed and summarized. The temporal variations and spatial distributions of the estimated settling velocity are negatively correlated with current speed, which can be explained using the combination of flocculation process and Stokes' law. The temporal variations and spatial distributions of the estimated resuspension rate are also negatively correlated with current speed, which are related to the grain size of the seabed sediments under different current velocities. Besides, the estimated inflow open boundary conditions reach the local maximum values near the low water slack conditions and the estimated initial conditions are negatively correlated with water depth, which is consistent with the general understanding. The relationships between the estimated parameters and the hydrodynamic fields can be suggestive for improving the parameterization in cohesive sediment transport models.

  1. Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise

    The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible un...

  2. Development of airborne remote sensing data assimilation system

    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

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

    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.

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

    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

  5. NCDC feed of Global Telecommunication System (GTS) marine observations in International Maritime Meteorological Archive (IMMA) Format

    National Oceanic and Atmospheric Administration, Department of Commerce — The data contained here are surface marine observations from many different sources via the NCDC Global Telecommunication System (GTS) Marine in International...

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

    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.

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

    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.

  8. Key aspects of stratospheric tracer modeling using assimilated winds

    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

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

    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.

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

    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.

  11. Orographic precipitation at global and regional scales: Observational uncertainty and evaluation of 25-km global model simulations

    Schiemann, Reinhard; Roberts, Charles J.; Bush, Stephanie; Demory, Marie-Estelle; Strachan, Jane; Vidale, Pier Luigi; Mizielinski, Matthew S.; Roberts, Malcolm J.

    2015-04-01

    Precipitation over land exhibits a high degree of variability due to the complex interaction of the precipitation generating atmospheric processes with coastlines, the heterogeneous land surface, and orography. Global general circulation models (GCMs) have traditionally had very limited ability to capture this variability on the mesoscale (here ~50-500 km) due to their low resolution. This has changed with recent investments in resolution and ensembles of multidecadal climate simulations of atmospheric GCMs (AGCMs) with ~25 km grid spacing are becoming increasingly available. Here, we evaluate the mesoscale precipitation distribution in one such set of simulations obtained in the UPSCALE (UK on PrACE - weather-resolving Simulations of Climate for globAL Environmental risk) modelling campaign with the HadGEM-GA3 AGCM. Increased model resolution also poses new challenges to the observational datasets used to evaluate models. Global gridded data products such as those provided by the Global Precipitation Climatology Project (GPCP) are invaluable for assessing large-scale features of the precipitation distribution but may not sufficiently resolve mesoscale structures. In the absence of independent estimates, the intercomparison of different observational datasets may be the only way to get some insight into the uncertainties associated with these observations. Here, we focus on mid-latitude continental regions where observations based on higher-density gauge networks are available in addition to the global data sets: Europe/the Alps, South and East Asia, and the continental US. The ability of GCMs to represent mesoscale variability is of interest in its own right, as climate information on this scale is required by impact studies. An additional motivation for the research proposed here arises from continuing efforts to quantify the components of the global radiation budget and water cycle. Recent estimates based on radiation measurements suggest that the global mean

  12. Retrieving Temperature Anomaly in the Global Subsurface and Deeper Ocean From Satellite Observations

    Su, Hua; Li, Wene; Yan, Xiao-Hai

    2018-01-01

    Retrieving the subsurface and deeper ocean (SDO) dynamic parameters from satellite observations is crucial for effectively understanding ocean interior anomalies and dynamic processes, but it is challenging to accurately estimate the subsurface thermal structure over the global scale from sea surface parameters. This study proposes a new approach based on Random Forest (RF) machine learning to retrieve subsurface temperature anomaly (STA) in the global ocean from multisource satellite observations including sea surface height anomaly (SSHA), sea surface temperature anomaly (SSTA), sea surface salinity anomaly (SSSA), and sea surface wind anomaly (SSWA) via in situ Argo data for RF training and testing. RF machine-learning approach can accurately retrieve the STA in the global ocean from satellite observations of sea surface parameters (SSHA, SSTA, SSSA, SSWA). The Argo STA data were used to validate the accuracy and reliability of the results from the RF model. The results indicated that SSHA, SSTA, SSSA, and SSWA together are useful parameters for detecting SDO thermal information and obtaining accurate STA estimations. The proposed method also outperformed support vector regression (SVR) in global STA estimation. It will be a useful technique for studying SDO thermal variability and its role in global climate system from global-scale satellite observations.

  13. Model Uncertainty Quantification Methods In Data Assimilation

    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.

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

    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.

  15. Global biogeographical pattern of ecosystem functional types derived from earth observation data

    Ivits, Eva; Cherlet, Michael; Horion, Stéphanie Marie Anne F

    2013-01-01

    correspondence of the EFTs to global climate and also to land use classification. The results show the great potential of Earth Observation derived parameters for the quantification of ecosystem functional dynamics and for providing reference status information for future assessments of ecosystem changes........ The association of the EFTs with existing climate and land cover classifications was demonstrated via Detrended Correspondence Analysis (DCA). The ordination indicated good description of the global environmental gradient by the EFTs, supporting the understanding of phenological and productivity dynamics...... of global ecosystems. Climatic constraints of vegetation growth explained 50% of variation in the phenological data along the EFTs showing that part of the variation in the global phenological gradient is not climate related but is unique to the Earth Observation derived variables. DCA demonstrated good...

  16. Combined constraints on global ocean primary production using observations and models

    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.

  17. Are Global In-Situ Ocean Observations Fit-for-purpose? Applying the Framework for Ocean Observing in the Atlantic.

    Visbeck, M.; Fischer, A. S.; Le Traon, P. Y.; Mowlem, M. C.; Speich, S.; Larkin, K.

    2015-12-01

    There are an increasing number of global, regional and local processes that are in need of integrated ocean information. In the sciences ocean information is needed to support physical ocean and climate studies for example within the World Climate Research Programme and its CLIVAR project, biogeochemical issues as articulated by the GCP, IMBER and SOLAS projects of ICSU-SCOR and Future Earth. This knowledge gets assessed in the area of climate by the IPCC and biodiversity by the IPBES processes. The recently released first World Ocean Assessment focuses more on ecosystem services and there is an expectation that the Sustainable Development Goals and in particular Goal 14 on the Ocean and Seas will generate new demands for integrated ocean observing from Climate to Fish and from Ocean Resources to Safe Navigation and on a healthy, productive and enjoyable ocean in more general terms. In recognition of those increasing needs for integrated ocean information we have recently launched the Horizon 2020 AtlantOS project to promote the transition from a loosely-coordinated set of existing ocean observing activities to a more integrated, more efficient, more sustainable and fit-for-purpose Atlantic Ocean Observing System. AtlantOS takes advantage of the Framework for Ocean observing that provided strategic guidance for the design of the project and its outcome. AtlantOS will advance the requirements and systems design, improving the readiness of observing networks and data systems, and engaging stakeholders around the Atlantic. AtlantOS will bring Atlantic nations together to strengthen their complementary contributions to and benefits from the internationally coordinated Global Ocean Observing System (GOOS) and the Blue Planet Initiative of the Global Earth Observation System of Systems (GEOSS). AtlantOS will fill gaps of the in-situ observing system networks and will ensure that their data are readily accessible and useable. AtlantOS will demonstrate the utility of

  18. Active listening in medical consultations: development of the Active Listening Observation Scale (ALOS-global).

    Fassaert, Thijs; van Dulmen, Sandra; Schellevis, François; Bensing, Jozien

    2007-11-01

    Active listening is a prerequisite for a successful healthcare encounter, bearing potential therapeutic value especially in clinical situations that require no specific medical intervention. Although generally acknowledged as such, active listening has not been studied in depth. This paper describes the development of the Active Listening Observation Scale (ALOS-global), an observation instrument measuring active listening and its validation in a sample of general practice consultations for minor ailments. Five hundred and twenty-four videotaped general practice consultations involving minor ailments were observed with the ALOS-global. Hypotheses were tested to determine validity, incorporating patients' perception of GPs' affective performance, GPs' verbal attention, patients' self-reported anxiety level and gender differences. The final 7-item ALOS-global had acceptable inter- and intra-observer agreement. Factor analysis revealed one homogeneous dimension. The scalescore was positively related to verbal attention measured by RIAS, to patients' perception of GPs' performance and to their pre-visit anxiety level. Female GPs received higher active listening scores. The results of this study are promising concerning the psychometric properties of the ALOS-global. More research is needed to confirm these preliminary findings. After establishing how active listening differentiates between health professionals, the ALOS-global may become a valuable tool in feedback and training aimed at increasing listening skills.

  19. Global assessment of ocean carbon export by combining satellite observations and food-web models

    Siegel, D. A.; Buesseler, K. O.; Doney, S. C.; Sailley, S. F.; Behrenfeld, M. J.; Boyd, P. W.

    2014-03-01

    The export of organic carbon from the surface ocean by sinking particles is an important, yet highly uncertain, component of the global carbon cycle. Here we introduce a mechanistic assessment of the global ocean carbon export using satellite observations, including determinations of net primary production and the slope of the particle size spectrum, to drive a food-web model that estimates the production of sinking zooplankton feces and algal aggregates comprising the sinking particle flux at the base of the euphotic zone. The synthesis of observations and models reveals fundamentally different and ecologically consistent regional-scale patterns in export and export efficiency not found in previous global carbon export assessments. The model reproduces regional-scale particle export field observations and predicts a climatological mean global carbon export from the euphotic zone of 6 Pg C yr-1. Global export estimates show small variation (typically model parameter values. The model is also robust to the choices of the satellite data products used and enables interannual changes to be quantified. The present synthesis of observations and models provides a path for quantifying the ocean's biological pump.

  20. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites

    Belward, Alan S.; Skøien, Jon O.

    2015-05-01

    This paper presents a compendium of satellites under civilian and/or commercial control with the potential to gather global land-cover observations. From this we show that a growing number of sovereign states are acquiring capacity for space based land-cover observations and show how geopolitical patterns of ownership are changing. We discuss how the number of satellites flying at any time has progressed as a function of increased launch rates and mission longevity, and how the spatial resolutions of the data they collect has evolved. The first such satellite was launched by the USA in 1972. Since then government and/or private entities in 33 other sovereign states and geopolitical groups have chosen to finance such missions and 197 individual satellites with a global land-cover observing capacity have been successfully launched. Of these 98 were still operating at the end of 2013. Since the 1970s the number of such missions failing within 3 years of launch has dropped from around 60% to less than 20%, the average operational life of a mission has almost tripled, increasing from 3.3 years in the 1970s to 8.6 years (and still lengthening), the average number of satellites launched per-year/per-decade has increased from 2 to 12 and spatial resolution increased from around 80 m to less than 1 m multispectral and less than half a meter for panchromatic; synthetic aperture radar resolution has also fallen, from 25 m in the 1970s to 1 m post 2007. More people in more countries have access to data from global land-cover observing spaceborne missions at a greater range of spatial resolutions than ever before. We provide a compendium of such missions, analyze the changes and shows how innovation, the need for secure data-supply, national pride, falling costs and technological advances may underpin the trends we document.

  1. Observing Tropospheric Water Vapor by Radio Occultation using the Global Positioning System

    Kursinski, E. R.; Hajj, G. A.; Hardy, K. R.; Romans, L. J.; Schofield, J. T.

    1995-01-01

    Given the importance of water vapor to weather, climate and hydrology, global humidity observations from satellites are critical. At low latitudes, radio occultation observations of Earth's atmosphere using the Global Positioning System (GPS) satellites allow water vapor profiles to be retrieved with accuracies of 10 to 20% below 6 to 7 km altitude and approx. 5% or better within the boundary layer. GPS observations provide a unique combination of accuracy, vertical resolution (less than or equal to 1 km) and insensitivity to cloud and aerosol particles that is well suited to observations of the lower troposphere. These characteristics combined with the inherent stability of radio occultation observations make it an excellent candidate for the measurement of long term trends.

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

    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

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

    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

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

    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. Combining observations and models to reduce uncertainty in the cloud response to global warming

    Norris, J. R.; Myers, T.; Chellappan, S.

    2017-12-01

    Currently there is large uncertainty on how subtropical low-level clouds will respond to global warming and whether they will act as a positive feedback or negative feedback. Global climate models substantially agree on what changes in atmospheric structure and circulation will occur with global warming but greatly disagree over how clouds will respond to these changes in structure and circulation. An examination of models with the most realistic simulations of low-level cloudiness indicates that the model cloud response to atmospheric changes associated with global warming is quantitatively similar to the model cloud response to atmospheric changes at interannual time scales. For these models, the cloud response to global warming predicted by multilinear regression using coefficients derived from interannual time scales is quantitatively similar to the cloud response to global warming directly simulated by the model. Since there is a large spread among cloud response coefficients even among models with the most realistic cloud simulations, substitution of coefficients derived from satellite observations reduces the uncertainty range of the low-level cloud feedback. Increased sea surface temperature associated with global warming acts to reduce low-level cloudiness, which is partially offset by increased lower tropospheric stratification that acts to enhance low-level cloudiness. Changes in free-tropospheric relative humidity, subsidence, and horizontal advection have only a small impact on low-level cloud. The net reduction in subtropical low-level cloudiness increases absorption of solar radiation by the climate system, thus resulting in a weak positive feedback.

  6. Observations of geographically correlated orbit errors for TOPEX/Poseidon using the global positioning system

    Christensen, E. J.; Haines, B. J.; Mccoll, K. C.; Nerem, R. S.

    1994-01-01

    We have compared Global Positioning System (GPS)-based dynamic and reduced-dynamic TOPEX/Poseidon orbits over three 10-day repeat cycles of the ground-track. The results suggest that the prelaunch joint gravity model (JGM-1) introduces geographically correlated errors (GCEs) which have a strong meridional dependence. The global distribution and magnitude of these GCEs are consistent with a prelaunch covariance analysis, with estimated and predicted global rms error statistics of 2.3 and 2.4 cm rms, respectively. Repeating the analysis with the post-launch joint gravity model (JGM-2) suggests that a portion of the meridional dependence observed in JGM-1 still remains, with global rms error of 1.2 cm.

  7. The role of natural climatic variation in perturbing the observed global mean temperature trend

    Hunt, B.G. [CSIRO Marine and Atmospheric Research, Aspendale, VIC (Australia)

    2011-02-15

    Controversy continues to prevail concerning the reality of anthropogenically-induced climatic warming. One of the principal issues is the cause of the hiatus in the current global warming trend. There appears to be a widely held view that climatic change warming should exhibit an inexorable upwards trend, a view that implies there is no longer any input by climatic variability in the existing climatic system. The relative roles of climatic change and climatic variability are examined here using the same coupled global climatic model. For the former, the model is run using a specified CO{sub 2} growth scenario, while the latter consisted of a multi-millennial simulation where any climatic variability was attributable solely to internal processes within the climatic system. It is shown that internal climatic variability can produce global mean surface temperature anomalies of {+-}0.25 K and sustained positive and negative anomalies sufficient to account for the anomalous warming of the 1940s as well as the present hiatus in the observed global warming. The characteristics of the internally-induced negative temperature anomalies are such that if this internal natural variability is the cause of the observed hiatus, then a resumption of the observed global warming trend is to be expected within the next few years. (orig.)

  8. Development of a Ground-Based Atmospheric Monitoring Network for the Global Mercury Observation System (GMOS

    Sprovieri F.

    2013-04-01

    Full Text Available Consistent, high-quality measurements of atmospheric mercury (Hg are necessary in order to better understand Hg emissions, transport, and deposition on a global scale. Although the number of atmospheric Hg monitoring stations has increased in recent years, the available measurement database is limited and there are many regions of the world where measurements have not been extensively performed. Long-term atmospheric Hg monitoring and additional ground-based monitoring sites are needed in order to generate datasets that will offer new insight and information about the global scale trends of atmospheric Hg emissions and deposition. In the framework of the Global Mercury Observation System (GMOS project, a coordinated global observational network for atmospheric Hg is being established. The overall research strategy of GMOS is to develop a state-of-the-art observation system able to provide information on the concentration of Hg species in ambient air and precipitation on the global scale. This network is being developed by integrating previously established ground-based atmospheric Hg monitoring stations with newly established GMOS sites that are located both at high altitude and sea level locations, as well as in climatically diverse regions. Through the collection of consistent, high-quality atmospheric Hg measurement data, we seek to create a comprehensive assessment of atmospheric Hg concentrations and their dependence on meteorology, long-range atmospheric transport and atmospheric emissions.

  9. Data assimilation in the decision support system RODOS

    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...

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

    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.

  11. Technical report series on global modeling and data assimilation. Volume 3: An efficient thermal infrared radiation parameterization for use in general circulation models

    Suarex, Max J. (Editor); Chou, Ming-Dah

    1994-01-01

    A detailed description of a parameterization for thermal infrared radiative transfer designed specifically for use in global climate models is presented. The parameterization includes the effects of the main absorbers of terrestrial radiation: water vapor, carbon dioxide, and ozone. While being computationally efficient, the schemes compute very accurately the clear-sky fluxes and cooling rates from the Earth's surface to 0.01 mb. This combination of accuracy and speed makes the parameterization suitable for both tropospheric and middle atmospheric modeling applications. Since no transmittances are precomputed the atmospheric layers and the vertical distribution of the absorbers may be freely specified. The scheme can also account for any vertical distribution of fractional cloudiness with arbitrary optical thickness. These features make the parameterization very flexible and extremely well suited for use in climate modeling studies. In addition, the numerics and the FORTRAN implementation have been carefully designed to conserve both memory and computer time. This code should be particularly attractive to those contemplating long-term climate simulations, wishing to model the middle atmosphere, or planning to use a large number of levels in the vertical.

  12. Global Positioning System: Observations on Quarterly Reports from the Air Force

    2016-10-17

    Positioning System : Observations on Quarterly Reports from the Air Force The satellite-based Global Positioning System (GPS) provides positioning , navigation...infrastructure, and transportation safety. The Department of Defense (DOD)—specifically, the Air Force—develops and operates the GPS system , which...programs, including the most recent detailed assessment of the next generation operational control system (OCX)

  13. Developing global capabilities for the observation and predication of harmful algal blooms

    Bernard, S

    2014-01-01

    Full Text Available ). The GEO Blue Planet HAB initiative seeks to consolidate and expand on existing capabilities, building a global community to develop and maximise the use and societal benefits of an integrated HAB observation and prediction system. Such a system would...

  14. Active listening in medical consultations: development of the Active Listening Observation Scale (ALOS-global).

    Fassaert, T.; Dulmen, S. van; Schellevis, F.; Bensing, J.

    2007-01-01

    OBJECTIVE: Active listening is a prerequisite for a successful healthcare encounter, bearing potential therapeutic value especially in clinical situations that require no specific medical intervention. Although generally acknowledged as such, active listening has not been studied in depth. This paper describes the development of the Active Listening Observation Scale (ALOS-global), an observation instrument measuring active listening and its validation in a sample of general practice consulta...

  15. Role of Stratospheric Water Vapor in Global Warming from GCM Simulations Constrained by MLS Observation

    Wang, Y.; Stek, P. C.; Su, H.; Jiang, J. H.; Livesey, N. J.; Santee, M. L.

    2014-12-01

    Over the past century, global average surface temperature has warmed by about 0.16°C/decade, largely due to anthropogenic increases in well-mixed greenhouse gases. However, the trend in global surface temperatures has been nearly flat since 2000, raising a question regarding the exploration of the drivers of climate change. Water vapor is a strong greenhouse gas in the atmosphere. Previous studies suggested that the sudden decrease of stratospheric water vapor (SWV) around 2000 may have contributed to the stall of global warming. Since 2004, the SWV observed by Microwave Limb Sounder (MLS) on Aura satellite has shown a slow recovery. The role of recent SWV variations in global warming has not been quantified. We employ a coupled atmosphere-ocean climate model, the NCAR CESM, to address this issue. It is found that the CESM underestimates the stratospheric water vapor by about 1 ppmv due to limited representations of the stratospheric dynamic and chemical processes important for water vapor variabilities. By nudging the modeled SWV to the MLS observation, we find that increasing SWV by 1 ppmv produces a robust surface warming about 0.2°C in global-mean when the model reaches equilibrium. Conversely, the sudden drop of SWV from 2000 to 2004 would cause a surface cooling about -0.08°C in global-mean. On the other hand, imposing the observed linear trend of SWV based on the 10-year observation of MLS in the CESM yields a rather slow surface warming, about 0.04°C/decade. Our model experiments suggest that SWV contributes positively to the global surface temperature variation, although it may not be the dominant factor that drives the recent global warming hiatus. Additional sensitivity experiments show that the impact of SWV on surface climate is mostly governed by the SWV amount at 100 hPa in the tropics. Furthermore, the atmospheric model simulations driven by observed sea surface temperature (SST) show that the inter-annual variation of SWV follows that of SST

  16. Conditions for successful data assimilation

    Morzfeld, M.; Chorin, A. J.

    2013-12-01

    . (3) There is a parameter range in which the model noise and the observation noise are roughly comparable, and in which even the optimal particle filter collapses, even under ideal circumstances. We further study the role of the effective dimension in variational data assimilation and particle smoothing, for both the weak and strong constraint problem. It was found that these methods too require a moderate effective dimension or else no accurate predictions can be expected. Moreover, variational data assimilation or particle smoothing may be applicable in the parameter range where particle filtering fails, because the use of more than one consecutive data set helps reduce the variance which is responsible for the collapse of the filters.

  17. Globally Increased Crop Growth and Cropping Intensity from the Long-Term Satellite-Based Observations

    Chen, Bin

    2018-04-01

    Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI) and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p impact on the crop growth trend.

  18. GLOBALLY INCREASED CROP GROWTH AND CROPPING INTENSITY FROM THE LONG-TERM SATELLITE-BASED OBSERVATIONS

    B. Chen

    2018-04-01

    Full Text Available Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p < 0.001, and as for climatic drivers, the gradual temperature and precipitation changes have had a measurable impact on the crop growth trend.

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

    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.

  20. Scalable and balanced dynamic hybrid data assimilation

    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

  1. Data Assimilation - Advances and Applications

    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.

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

    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

  3. Longitudinal observations of globally distributed design teams: The impacts on Product Development

    Taylor, Thomas Paul; Ahmed-Kristensen, Saeema

    2015-01-01

    Factors impacting the success of Product Development (PD) projects are intensified when teams are distributed globally, making it a challenging task for project management to deal with effects on time, cost and quality. It is important for project management to understand when challenges......, such as communication difficulties, a lack of common vision between team members or issues related to documentation, may occur during PD projects, enabling them to take the necessary preventative action (Edmondson and Nembhard, 2009). When investigating factors impacting the success of PD, the majority of research...... studies of globally distributed design teams in PD projects. This paper aims to contribute to the further understanding of the factors impacting the success of PD projects when teams are distributed globally. With the results from a longitudinal observational study over 8 months, the factors impacting...

  4. The global coastline dataset: the observed relation between erosion and sea-level rise

    Donchyts, G.; Baart, F.; Luijendijk, A.; Hagenaars, G.

    2017-12-01

    Erosion of sandy coasts is considered one of the key risks of sea-level rise. Because sandy coastlines of the world are often highly populated, erosive coastline trends result in risk to populations and infrastructure. Most of our understanding of the relation between sea-level rise and coastal erosion is based on local or regional observations and generalizations of numerical and physical experiments. Until recently there was no reliable global scale assessment of the location of sandy coasts and their rate of erosion and accretion. Here we present the global coastline dataset that covers erosion indicators on a local scale with global coverage. The dataset uses our global coastline transects grid defined with an alongshore spacing of 250 m and a cross shore length extending 1 km seaward and 1 km landward. This grid matches up with pre-existing local grids where available. We present the latest results on validation of coastal-erosion trends (based on optical satellites) and classification of sandy versus non-sandy coasts. We show the relation between sea-level rise (based both on tide-gauges and multi-mission satellite altimetry) and observed erosion trends over the last decades, taking into account broken-coastline trends (for example due to nourishments).An interactive web application presents the publicly-accessible results using a backend based on Google Earth Engine. It allows both researchers and stakeholders to use objective estimates of coastline trends, particularly when authoritative sources are not available.

  5. How to most effectively expand the global surface ozone observing network

    E. D. Sofen

    2016-02-01

    Full Text Available Surface ozone observations with modern instrumentation have been made around the world for more than 40 years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere–biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25 % of the globe is observed (34 % land, 21 % ocean. Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12–17 % show significant gaps. Antarctica is surprisingly well observed (78 %. Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics are significantly under-observed. The current network is unlikely to see the impact of the El Niño–Southern Oscillation (ENSO but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new

  6. How to most effectively expand the global surface ozone observing network

    Sofen, E. D.; Bowdalo, D.; Evans, M. J.

    2016-02-01

    Surface ozone observations with modern instrumentation have been made around the world for more than 40 years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere-biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25 % of the globe is observed (34 % land, 21 % ocean). Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12-17 %) show significant gaps. Antarctica is surprisingly well observed (78 %). Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics) are significantly under-observed. The current network is unlikely to see the impact of the El Niño-Southern Oscillation (ENSO) but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new sites which would help to close

  7. Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions

    Yanxu Zhang,; Daniel J. Jacob,; Hannah M. Horowitz,; Long Chen,; Helen M. Amos,; Krabbenhoft, David P.; Franz Slemr,; Vincent L. St. Louis,; Elsie M. Sunderland,

    2015-01-01

    Observations of elemental mercury (Hg0) at sites in North America and Europe show large decreases (∼1–2% y−1) from 1990 to present. Observations in background northern hemisphere air, including Mauna Loa Observatory (Hawaii) and CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) aircraft flights, show weaker decreases (Asia. Implementation of our inventory in a global 3D atmospheric Hg simulation [GEOS-Chem (Goddard Earth Observing System-Chemistry)] coupled to land and ocean reservoirs reproduces the observed large-scale trends in atmospheric Hg0 concentrations and in HgII wet deposition. The large trends observed in North America and Europe reflect the phase-out of Hg from commercial products as well as the cobenefit from SO2 and NOx emission controls on coal-fired utilities.

  8. Experimental observation of chimera and cluster states in a minimal globally coupled network

    Hart, Joseph D. [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Physics, University of Maryland, College Park, Maryland 20742 (United States); Bansal, Kanika [Department of Mathematics, University at Buffalo, SUNY Buffalo, New York 14260 (United States); US Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005 (United States); Murphy, Thomas E. [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742 (United States); Roy, Rajarshi [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Physics, University of Maryland, College Park, Maryland 20742 (United States); Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742 (United States)

    2016-09-15

    A “chimera state” is a dynamical pattern that occurs in a network of coupled identical oscillators when the symmetry of the oscillator population is broken into synchronous and asynchronous parts. We report the experimental observation of chimera and cluster states in a network of four globally coupled chaotic opto-electronic oscillators. This is the minimal network that can support chimera states, and our study provides new insight into the fundamental mechanisms underlying their formation. We use a unified approach to determine the stability of all the observed partially synchronous patterns, highlighting the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization. Our approach is general in terms of network size and connectivity. We also find that chimera states often appear in regions of multistability between global, cluster, and desynchronized states.

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

    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.

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

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

    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

  12. Super-leading logarithms in non-global observables in QCD colour basis independent calculation

    Forshaw, J R; Seymour, M H

    2008-01-01

    In a previous paper we reported the discovery of super-leading logarithmic terms in a non-global QCD observable. In this short update we recalculate the first super-leading logarithmic contribution to the 'gaps between jets' cross-section using a colour basis independent notation. This sheds light on the structure and origin of the super-leading terms and allows them to be calculated for gluon scattering processes for the first time.

  13. Experimental results concerning global observables from the CERN SPS heavy ion program

    Young, G.R.

    1990-06-01

    A brief overview is given of experimental results obtained during the initial operation of the heavy-ion program at the CERN SPS during the period 1986--1988. This paper confines itself to a presentation of results on so-called global observables, such as energy flow and multiplicity distributions, and on information extracted from them. Of particular interest among the latter are an estimate of the magnitude and spatial distribution of the energy density attained. 3 refs., 27 figs

  14. Spatial Assimilation in Denmark?

    Andersen, Hans Skifter

    2010-01-01

    market and discrimination, which limits the housing possibilities for ethnic minorities. Another explanation could be that immigrants for different reasons choose to settle in so-called ethnic enclaves where they can find an ethnic social network, which can support them in their new country....... In traditional research literature about immigration it has been shown that for many immigrants living in enclaves has been a temporary situation. The 'spatial assimilation theory' says that this situation ends when the family has become more integrated in the new society and then moves to other parts...

  15. Development of a data assimilation algorithm

    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....

  16. Constraints on global oceanic emissions of N2O from observations and models

    Buitenhuis, Erik T.; Suntharalingam, Parvadha; Le Quéré, Corinne

    2018-04-01

    We estimate the global ocean N2O flux to the atmosphere and its confidence interval using a statistical method based on model perturbation simulations and their fit to a database of ΔpN2O (n = 6136). We evaluate two submodels of N2O production. The first submodel splits N2O production into oxic and hypoxic pathways following previous publications. The second submodel explicitly represents the redox transformations of N that lead to N2O production (nitrification and hypoxic denitrification) and N2O consumption (suboxic denitrification), and is presented here for the first time. We perturb both submodels by modifying the key parameters of the N2O cycling pathways (nitrification rates; NH4+ uptake; N2O yields under oxic, hypoxic and suboxic conditions) and determine a set of optimal model parameters by minimisation of a cost function against four databases of N cycle observations. Our estimate of the global oceanic N2O flux resulting from this cost function minimisation derived from observed and model ΔpN2O concentrations is 2.4 ± 0.8 and 2.5 ± 0.8 Tg N yr-1 for the two N2O submodels. These estimates suggest that the currently available observational data of surface ΔpN2O constrain the global N2O flux to a narrower range relative to the large range of results presented in the latest IPCC report.

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

    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.

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

    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.

  19. Solar wind modulation of the Martian ionosphere observed by Mars Global Surveyor

    J.-S. Wang

    2004-06-01

    Full Text Available Electron density profiles in the Martian ionosphere observed by the radio occultation experiment on board Mars Global Surveyor have been analyzed to determine if the densities are influenced by the solar wind. Evidence is presented that the altitude of the maximum ionospheric electron density shows a positive correlation to the energetic proton flux in the solar wind. The solar wind modulation of the Martian ionosphere can be attributed to heating of the neutral atmosphere by the solar wind energetic proton precipitation. The modulation is observed to be most prominent at high solar zenith angles. It is argued that this is consistent with the proposed modulation mechanism.

  20. PACA_Rosetta67P: Global Amateur Observing Support for ESA/Rosetta Mission

    Yanamandra-Fisher, Padma A.; Alexander, Claudia; Morales, Efrain; Feliciano-Rivera, Christiana

    2015-11-01

    The PACA (Professional - Amateur Collaborative Astronomy) Project is an ecosystem of several social media platforms (Facebook, Pinterest, Twitter, Flickr, Vimeo) that takes advantage of the global and immediate connectivity amongst amateur astronomers worldwide, that can be galvanized to participate in a given observing campaign. The PACA Project has participated in organized campaigns such as Comet Observing Campaign (CIOC_ISON) in 2013 and Comet Siding Spring (CIOC_SidingSpring)in 2014. Currently the PACA Project is supporting ESA/Rosetta mission with ground-based observations of the comet 67P/Churyumov-Gerasimenko (CG) through its perihelion in August 2015 and beyond; providing baseline observations of magnitude and evolution from locations around the globe. Comet 67P/CG will reach its brightest post-perihelion and pass closest to Earth in November 2015. We will present the various benefits of our professional - amateur collaboration: developing and building a core astronomer community; defining an observing campaign from basic information of the comet from its previous apparitions; coordinating with professionals and the mission to acquire observations, albeit low-resolution, but on a long timeline; while addressing the creation of several science products such as the variation of its magnitude over time and the changing morphology. We will present some of our results to date and compare with observations from professionals and previous apparations of the comet. We shall also highlight the challenges faced in building a successful collaborative partnership between the professional and amateur observers and their resolution. With the popularity of mobile platforms and instant connections with peers globally, the multi-faceted social universe has become a vital part of engagement of multiple communities for collaborative scientific partnerships and outreach. We shall also highlight other cometary observing campaigns that The PACA Project has initiated to evolve

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

    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

  2. Data assimilation in integrated hydrological modelling

    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...

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

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

    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.

  5. Predictions and observations of global beta-induced Alfven-acoustic modes in JET and NSTX

    Gorelenkov, N N [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Berk, H L [Institute for Fusion Studies, University of Texas, Austin, TX 78712 (United States); Crocker, N A [Institute of Plasma and Fusion Research, University of California, Los Angeles, CA 90095-1354 (United States); Fredrickson, E D [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Kaye, S [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Kubota, S [Institute of Plasma and Fusion Research, University of California, Los Angeles, CA 90095-1354 (United States); Park, H [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Peebles, W [Institute of Plasma and Fusion Research, University of California, Los Angeles, CA 90095-1354 (United States); Sabbagh, S A [Department of Applied Physics, Columbia University, New York, NY 10027-6902 (United States); Sharapov, S E [Euroatom/UKAEA Fusion Association, Culham Science Centre, Abingdon, Oxfordshire OX14 3DB (United Kingdom); Stutmat, D [Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218 (United States); Tritz, K [Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218 (United States); Levinton, F M [Nova Photonics, One Oak Place, Princeton, NJ 08540 (United States); Yuh, H [Nova Photonics, One Oak Place, Princeton, NJ 08540 (United States)

    2007-12-15

    In this paper we report on observations and interpretations of a new class of global MHD eigenmode solutions arising in gaps in the low frequency Alfven-acoustic continuum below the geodesic acoustic mode frequency. These modes have been just reported (Gorelenkov et al 2007 Phys. Lett. 370 70-7) where preliminary comparisons indicate qualitative agreement between theory and experiment. Here we show a more quantitative comparison emphasizing recent NSTX experiments on the observations of the global eigenmodes, referred to as beta-induced Alfven-acoustic eigenmodes (BAAEs), which exist near the extrema of the Alfven-acoustic continuum. In accordance to the linear dispersion relations, the frequency of these modes may shift as the safety factor, q, profile relaxes. We show that BAAEs can be responsible for observations in JET plasmas at relatively low beta <2% as well as in NSTX plasmas at relatively high beta >20%. In NSTX plasma observed magnetic activity has the same properties as predicted by theory for the mode structure and the frequency. Found numerically in NOVA simulations BAAEs are used to explain the observed properties of relatively low frequency experimental signals seen in NSTX and JET tokamaks.

  6. Predications and Observations of Global Beta-induced Alfven-acoustic Modes in JET and NSTX

    Gorelenkov, N.N.

    2008-01-01

    In this paper we report on observations and interpretations of a new class of global MHD eigenmode solutions arising in gaps in the low frequency Alfven-acoustic continuum below the geodesic acoustic mode frequency. These modes have been just reported (Gorelenkov et al 2007 Phys. Lett. 370 70-7) where preliminary comparisons indicate qualitative agreement between theory and experiment. Here we show a more quantitative comparison emphasizing recent NSTX experiments on the observations of the global eigenmodes, referred to as beta-induced Alfven-acoustic eigenmodes (BAAEs), which exist near the extrema of the Alfven-acoustic continuum. In accordance to the linear dispersion relations, the frequency of these modes may shift as the safety factor, q, profile relaxes. We show that BAAEs can be responsible for observations in JET plasmas at relatively low beta 20%. In NSTX plasma observed magnetic activity has the same properties as predicted by theory for the mode structure and the frequency. Found numerically in NOVA simulations BAAEs are used to explain the observed properties of relatively low frequency experimental signals seen in NSTX and JET tokamaks

  7. Application of Bred Vectors To Data Assimilation

    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

  8. NCEP Global Ocean Data Assimilation System (GODAS)

    National Oceanic and Atmospheric Administration, Department of Commerce — The GODAS dataset is a real-time ocean analysis and a reanalysis. It is used for monitoring, retrospective analysis as well as for providing oceanic initial...

  9. NCEP Global Data Assimilation System GDAS

    National Oceanic and Atmospheric Administration, Department of Commerce — Data is from NMC initialized analysis (2x/day). It consists of most variables interpolated to pressure surfaces from model (sigma) surfaces.

  10. Variational Data Assimilation for the Global Ocean

    2013-01-01

    ocean includes the Geoid (a fixed gravity equipotential surface ) as well as the MDT, which is not known accurately enough relative to the centimeter...scales, including processes that control the surface mixed layer, the formation of ocean eddies, meandering ocean J.A. Cummings (E3) nography Division...variables. Examples of this in the ocean are integral quantities, such as acous^B travel time and altimeter measures of sea surface height, and direct

  11. Generation and Evaluation of a Global Land Surface Phenology Product from Suomi-NPP VIIRS Observations

    Zhang, X.; Liu, L.; Yan, D.; Moon, M.; Liu, Y.; Henebry, G. M.; Friedl, M. A.; Schaaf, C.

    2017-12-01

    Land surface phenology (LSP) datasets have been produced from a variety of coarse spatial resolution satellite observations at both regional and global scales and spanning different time periods since 1982. However, the LSP product generated from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data at a spatial resolution of 500m, which is termed Land Cover Dynamics (MCD12Q2), is the only global product operationally produced and freely accessible at annual time steps from 2001. Because MODIS instrument is aging and will be replaced by the Visible Infrared Imaging Radiometer Suite (VIIRS), this research focuses on the generation and evaluation of a global LSP product from Suomi-NPP VIIRS time series observations that provide continuity with the MCD12Q2 product. Specifically, we generate 500m VIIRS global LSP data using daily VIIRS Nadir BRDF (bidirectional reflectance distribution function)-Adjusted reflectances (NBAR) in combination with land surface temperature, snow cover, and land cover type as inputs. The product provides twelve phenological metrics (seven phenological dates and five phenological greenness magnitudes), along with six quality metrics characterizing the confidence and quality associated with phenology retrievals at each pixel. In this paper, we describe the input data and algorithms used to produce this new product, and investigate the impact of VIIRS data time series quality on phenology detections across various climate regimes and ecosystems. As part of our analysis, the VIIRS LSP is evaluated using PhenoCam imagery in North America and Asia, and using higher spatial resolution satellite observations from Landsat 8 over an agricultural area in the central USA. We also explore the impact of high frequency cloud cover on the VIIRS LSP product by comparing with phenology detected from the Advanced Himawari Imager (AHI) onboard Himawari-8. AHI is a new geostationary sensor that observes land surface every 10 minutes, which increases

  12. Tropospheric Ozone Assessment Report: Database and Metrics Data of Global Surface Ozone Observations

    Martin G. Schultz

    2017-10-01

    Full Text Available In support of the first Tropospheric Ozone Assessment Report (TOAR a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of 'in-situ' hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of 'a posteriori' data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface

  13. Global Surface Mass Variations from Continuous GPS Observations and Satellite Altimetry Data

    Xinggang Zhang

    2017-09-01

    Full Text Available The Gravity Recovery and Climate Experiment (GRACE mission is able to observe the global large-scale mass and water cycle for the first time with unprecedented spatial and temporal resolution. However, no other time-varying gravity fields validate GRACE. Furthermore, the C20 of GRACE is poor, and no GRACE data are available before 2002 and there will likely be a gap between the GRACE and GRACE-FOLLOW-ON mission. To compensate for GRACE’s shortcomings, in this paper, we provide an alternative way to invert Earth’s time-varying gravity field, using a priori degree variance as a constraint on amplitudes of Stoke’s coefficients up to degree and order 60, by combining continuous GPS coordinate time series and satellite altimetry (SA mean sea level anomaly data from January 2003 to December 2012. Analysis results show that our estimated zonal low-degree gravity coefficients agree well with those of GRACE, and large-scale mass distributions are also investigated and assessed. It was clear that our method effectively detected global large-scale mass changes, which is consistent with GRACE observations and the GLDAS model, revealing the minimums of annual water cycle in the Amazon in September and October. The global mean mass uncertainty of our solution is about two times larger than that of GRACE after applying a Gaussian spatial filter with a half wavelength at 500 km. The sensitivity analysis further shows that ground GPS observations dominate the lower-degree coefficients but fail to contribute to the higher-degree coefficients, while SA plays a complementary role at higher-degree coefficients. Consequently, a comparison in both the spherical harmonic and geographic domain confirms our global inversion for the time-varying gravity field from GPS and Satellite Altimetry.

  14. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes.

    Miloslavich, Patricia; Bax, Nicholas J; Simmons, Samantha E; Klein, Eduardo; Appeltans, Ward; Aburto-Oropeza, Octavio; Andersen Garcia, Melissa; Batten, Sonia D; Benedetti-Cecchi, Lisandro; Checkley, David M; Chiba, Sanae; Duffy, J Emmett; Dunn, Daniel C; Fischer, Albert; Gunn, John; Kudela, Raphael; Marsac, Francis; Muller-Karger, Frank E; Obura, David; Shin, Yunne-Jai

    2018-04-05

    Sustained observations of marine biodiversity and ecosystems focused on specific conservation and management problems are needed around the world to effectively mitigate or manage changes resulting from anthropogenic pressures. These observations, while complex and expensive, are required by the international scientific, governance and policy communities to provide baselines against which the effects of human pressures and climate change may be measured and reported, and resources allocated to implement solutions. To identify biological and ecological essential ocean variables (EOVs) for implementation within a global ocean observing system that is relevant for science, informs society, and technologically feasible, we used a driver-pressure-state-impact-response (DPSIR) model. We (1) examined relevant international agreements to identify societal drivers and pressures on marine resources and ecosystems, (2) evaluated the temporal and spatial scales of variables measured by 100+ observing programs, and (3) analysed the impact and scalability of these variables and how they contribute to address societal and scientific issues. EOVs were related to the status of ecosystem components (phytoplankton and zooplankton biomass and diversity, and abundance and distribution of fish, marine turtles, birds and mammals), and to the extent and health of ecosystems (cover and composition of hard coral, seagrass, mangrove and macroalgal canopy). Benthic invertebrate abundance and distribution and microbe diversity and biomass were identified as emerging EOVs to be developed based on emerging requirements and new technologies. The temporal scale at which any shifts in biological systems will be detected will vary across the EOVs, the properties being monitored and the length of the existing time-series. Global implementation to deliver useful products will require collaboration of the scientific and policy sectors and a significant commitment to improve human and infrastructure

  15. Global observation-based diagnosis of soil moisture control on land surface flux partition

    Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Veal, Karen L.; Folwell, Sonja S.

    2016-04-01

    Soil moisture plays a central role in the partition of available energy at the land surface between sensible and latent heat flux to the atmosphere. As soils dry out, evapotranspiration becomes water-limited ("stressed"), and both land surface temperature (LST) and sensible heat flux rise as a result. This change in surface behaviour during dry spells directly affects critical processes in both the land and the atmosphere. Soil water deficits are often a precursor in heat waves, and they control where feedbacks on precipitation become significant. State-of-the-art global climate model (GCM) simulations for the Coupled Model Intercomparison Project Phase 5 (CMIP5) disagree on where and how strongly the surface energy budget is limited by soil moisture. Evaluation of GCM simulations at global scale is still a major challenge owing to the scarcity and uncertainty of observational datasets of land surface fluxes and soil moisture at the appropriate scale. Earth observation offers the potential to test how well GCM land schemes simulate hydrological controls on surface fluxes. In particular, satellite observations of LST provide indirect information about the surface energy partition at 1km resolution globally. Here, we present a potentially powerful methodology to evaluate soil moisture stress on surface fluxes within GCMs. Our diagnostic, Relative Warming Rate (RWR), is a measure of how rapidly the land warms relative to the overlying atmosphere during dry spells lasting at least 10 days. Under clear skies, this is a proxy for the change in sensible heat flux as soil dries out. We derived RWR from MODIS Terra and Aqua LST observations, meteorological re-analyses and satellite rainfall datasets. Globally we found that on average, the land warmed up during dry spells for 97% of the observed surface between 60S and 60N. For 73% of the area, the land warmed faster than the atmosphere (positive RWR), indicating water stressed conditions and increases in sensible heat flux

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

    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.

  17. Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise

    Brown, Patrick T.; Li, Wenhong; Cordero, Eugene C.; Mauget, Steven A.

    2015-01-01

    The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal. PMID:25898351

  18. Global long-term ozone trends derived from different observed and modelled data sets

    Coldewey-Egbers, M.; Loyola, D.; Zimmer, W.; van Roozendael, M.; Lerot, C.; Dameris, M.; Garny, H.; Braesicke, P.; Koukouli, M.; Balis, D.

    2012-04-01

    The long-term behaviour of stratospheric ozone amounts during the past three decades is investigated on a global scale using different observed and modelled data sets. Three European satellite sensors GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/METOP are combined and a merged global monthly mean total ozone product has been prepared using an inter-satellite calibration approach. The data set covers the 16-years period from June 1995 to June 2011 and it exhibits an excellent long-term stability, which is required for such trend studies. A multiple linear least-squares regression algorithm using different explanatory variables is applied to the time series and statistically significant positive trends are detected in the northern mid latitudes and subtropics. Global trends are also estimated using a second satellite-based Merged Ozone Data set (MOD) provided by NASA. For few selected geographical regions ozone trends are additionally calculated using well-maintained measurements of individual Dobson/Brewer ground-based instruments. A reasonable agreement in the spatial patterns of the trends is found amongst the European satellite, the NASA satellite, and the ground-based observations. Furthermore, two long-term simulations obtained with the Chemistry-Climate Models E39C-A provided by German Aerospace Center and UMUKCA-UCAM provided by University of Cambridge are analysed.

  19. Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise.

    Brown, Patrick T; Li, Wenhong; Cordero, Eugene C; Mauget, Steven A

    2015-04-21

    The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.

  20. Global observation of Omori-law decay in the rate of triggered earthquakes

    Parsons, T.

    2001-12-01

    Triggered earthquakes can be large, damaging, and lethal as evidenced by the 1999 shocks in Turkey and the 2001 events in El Salvador. In this study, earthquakes with M greater than 7.0 from the Harvard CMT catalog are modeled as dislocations to calculate shear stress changes on subsequent earthquake rupture planes near enough to be affected. About 61% of earthquakes that occurred near the main shocks are associated with calculated shear stress increases, while ~39% are associated with shear stress decreases. If earthquakes associated with calculated shear stress increases are interpreted as triggered, then such events make up at least 8% of the CMT catalog. Globally, triggered earthquakes obey an Omori-law rate decay that lasts between ~7-11 years after the main shock. Earthquakes associated with calculated shear stress increases occur at higher rates than background up to 240 km away from the main-shock centroid. Earthquakes triggered by smaller quakes (foreshocks) also obey Omori's law, which is one of the few time-predictable patterns evident in the global occurrence of earthquakes. These observations indicate that earthquake probability calculations which include interactions from previous shocks should incorporate a transient Omori-law decay with time. In addition, a very simple model using the observed global rate change with time and spatial distribution of triggered earthquakes can be applied to immediately assess the likelihood of triggered earthquakes following large events, and can be in place until more sophisticated analyses are conducted.

  1. How well will the Surface Water and Ocean Topography (SWOT) mission observe global reservoirs?

    Solander, Kurt C.; Reager, John T.; Famiglietti, James S.

    2016-03-01

    Accurate observations of global reservoir storage are critical to understand the availability of managed water resources. By enabling estimates of surface water area and height for reservoir sizes exceeding 250 m2 at a maximum repeat orbit of up to 21 days, the NASA Surface Water and Ocean Topography (SWOT) satellite mission (anticipated launch date 2020) is expected to greatly improve upon existing reservoir monitoring capabilities. It is thus essential that spatial and temporal measurement uncertainty for water bodies is known a priori to maximize the utility of SWOT observations as the data are acquired. In this study, we evaluate SWOT reservoir observations using a three-pronged approach that assesses temporal aliasing, errors due to specific reservoir spatial properties, and SWOT performance over actual reservoirs using a combination of in situ and simulated reservoir observations from the SWOTsim instrument simulator. Results indicate temporal errors to be less than 5% for the smallest reservoir sizes (100 km2). Surface area and height errors were found to be minimal (area SWOT, this study will be have important implications for future applications of SWOT reservoir measurements in global monitoring systems and models.

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

    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

  3. Building a Global Earth Observation System of Systems (GEOSS) and Its Interoperability Challenges

    Ryan, B. J.

    2015-12-01

    Launched in 2005 by industrialized nations, the Group on Earth Observations (GEO) began building the Global Earth Observation System of Systems (GEOSS). Consisting of both a policy framework, and an information infrastructure, GEOSS, was intended to link and/or integrate the multitude of Earth observation systems, primarily operated by its Member Countries and Participating Organizations, so that users could more readily benefit from global information assets for a number of society's key environmental issues. It was recognized that having ready access to observations from multiple systems was a prerequisite for both environmental decision-making, as well as economic development. From the very start, it was also recognized that the shear complexity of the Earth's system cannot be captured by any single observation system, and that a federated, interoperable approach was necessary. While this international effort has met with much success, primarily in advancing broad, open data policies and practices, challenges remain. In 2014 (Geneva, Switzerland) and 2015 (Mexico City, Mexico), Ministers from GEO's Member Countries, including the European Commission, came together to assess progress made during the first decade (2005 to 2015), and approve implementation strategies and mechanisms for the second decade (2016 to 2025), respectively. The approved implementation strategies and mechanisms are intended to advance GEOSS development thereby facilitating the increased uptake of Earth observations for informed decision-making. Clearly there are interoperability challenges that are technological in nature, and several will be discussed in this presentation. There are, however, interoperability challenges that can be better characterized as economic, governmental and/or political in nature, and these will be discussed as well. With the emergence of the Sustainable Development Goals (SDGs), the World Conference on Disaster Risk Reduction (WCDRR), and the United Nations

  4. Research of Compound Control for DC Motor System Based on Global Sliding Mode Disturbance Observer

    He Zhang

    2014-01-01

    Full Text Available Aiming at the problems of modeling errors, parameter variations, and load moment disturbances in DC motor control system, one global sliding mode disturbance observer (GSMDO is proposed based on the global sliding mode (GSM control theory. The output of GSMDO is used as the disturbance compensation in control system, which can improve the robust performance of DC motor control system. Based on the designed GSMDO in inner loop, one compound controller, composed of a feedback controller and a feedforward controller, is proposed in order to realize the position tracking of DC motor system. The gains of feedback controller are obtained by means of linear quadratic regulator (LQR optimal control theory. Simulation results present that the proposed control scheme possesses better tracking properties and stronger robustness against modeling errors, parameter variations, and friction moment disturbances. Moreover, its structure is simple; therefore it is easy to be implemented in engineering.

  5. Estimating Global Impervious Surface based on Social-economic Data and Satellite Observations

    Zeng, Z.; Zhang, K.; Xue, X.; Hong, Y.

    2016-12-01

    Impervious surface areas around the globe are expanding and significantly altering the surface energy balance, hydrology cycle and ecosystem services. Many studies have underlined the importance of impervious surface, r from hydrological modeling to contaminant transport monitoring and urban development estimation. Therefore accurate estimation of the global impervious surface is important for both physical and social sciences. Given the limited coverage of high spatial resolution imagery and ground survey, using satellite remote sensing and geospatial data to estimate global impervious areas is a practical approach. Based on the previous work of area-weighted imperviousness for north branch of the Chicago River provided by HDR, this study developed a method to determine the percentage of impervious surface using latest global land cover categories from multi-source satellite observations, population density and gross domestic product (GDP) data. Percent impervious surface at 30-meter resolution were mapped. We found that 1.33% of the CONUS (105,814 km2) and 0.475% of the land surface (640,370km2) are impervious surfaces. To test the utility and practicality of the proposed method, National Land Cover Database (NLCD) 2011 percent developed imperviousness for the conterminous United States was used to evaluate our results. The average difference between the derived imperviousness from our method and the NLCD data across CONUS is 1.14%, while difference between our results and the NLCD data are within ±1% over 81.63% of the CONUS. The distribution of global impervious surface map indicates that impervious surfaces are primarily concentrated in China, India, Japan, USA and Europe where are highly populated and/or developed. This study proposes a straightforward way of mapping global imperviousness, which can provide useful information for hydrologic modeling and other applications.

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

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

    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

  8. 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.

    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.

  9. Nonlinear data assimilation

    Van Leeuwen, Peter Jan; Reich, Sebastian

    2015-01-01

    This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.

  10. On the signatures of flare-induced global waves in the Sun: GOLF and VIRGO observations

    Kumar, Brajesh; Mathur, Savita; García, Rafael A.; Jiménez, Antonio

    2017-11-01

    Recently, several efforts have been made to identify the seismic signatures of flares and magnetic activity in the Sun and Sun-like stars. In this work, we have analysed the disc-integrated velocity and intensity observations of the Sun obtained from the Global Oscillations at Low Frequencies (GOLF) and Variability of solar IRradiance and Gravity Oscillations/Sun photometers (VIRGO/SPM) instruments, respectively, on board the Solar and Heliospheric Observatory space mission covering several successive flare events, for the period from 2011 February 11 to 2011 February 17, of which 2011 February 11 remained a relatively quiet day and served as a `null test' for the investigation. Application of the spectral analysis to these disc-integrated Sun-as-a-star velocity and intensity signals indicates that there is enhanced power of the global modes of oscillations in the Sun during the flares, as compared to the quiet day. The GOLF instrument obtains velocity observations using the Na I D lines which are formed in the upper solar photosphere, while the intensity data used in our analysis are obtained by VIRGO/SPM instrument at 862 nm, which is formed within the solar photosphere. Despite the fact that the two instruments sample different layers of the solar atmosphere using two different parameters (velocity versus intensity), we have found that both these observations show the signatures of flare-induced global waves in the Sun. These results could suffice in identifying the asteroseismic signatures of stellar flares and magnetic activity in the Sun-like stars.

  11. Constraints on global oceanic emissions of N2O from observations and models

    E. T. Buitenhuis

    2018-04-01

    Full Text Available We estimate the global ocean N2O flux to the atmosphere and its confidence interval using a statistical method based on model perturbation simulations and their fit to a database of ΔpN2O (n =  6136. We evaluate two submodels of N2O production. The first submodel splits N2O production into oxic and hypoxic pathways following previous publications. The second submodel explicitly represents the redox transformations of N that lead to N2O production (nitrification and hypoxic denitrification and N2O consumption (suboxic denitrification, and is presented here for the first time. We perturb both submodels by modifying the key parameters of the N2O cycling pathways (nitrification rates; NH4+ uptake; N2O yields under oxic, hypoxic and suboxic conditions and determine a set of optimal model parameters by minimisation of a cost function against four databases of N cycle observations. Our estimate of the global oceanic N2O flux resulting from this cost function minimisation derived from observed and model ΔpN2O concentrations is 2.4 ± 0.8 and 2.5 ± 0.8 Tg N yr−1 for the two N2O submodels. These estimates suggest that the currently available observational data of surface ΔpN2O constrain the global N2O flux to a narrower range relative to the large range of results presented in the latest IPCC report.

  12. Mapping Global Ocean Surface Albedo from Satellite Observations: Models, Algorithms, and Datasets

    Li, X.; Fan, X.; Yan, H.; Li, A.; Wang, M.; Qu, Y.

    2018-04-01

    Ocean surface albedo (OSA) is one of the important parameters in surface radiation budget (SRB). It is usually considered as a controlling factor of the heat exchange among the atmosphere and ocean. The temporal and spatial dynamics of OSA determine the energy absorption of upper level ocean water, and have influences on the oceanic currents, atmospheric circulations, and transportation of material and energy of hydrosphere. Therefore, various parameterizations and models have been developed for describing the dynamics of OSA. However, it has been demonstrated that the currently available OSA datasets cannot full fill the requirement of global climate change studies. In this study, we present a literature review on mapping global OSA from satellite observations. The models (parameterizations, the coupled ocean-atmosphere radiative transfer (COART), and the three component ocean water albedo (TCOWA)), algorithms (the estimation method based on reanalysis data, and the direct-estimation algorithm), and datasets (the cloud, albedo and radiation (CLARA) surface albedo product, dataset derived by the TCOWA model, and the global land surface satellite (GLASS) phase-2 surface broadband albedo product) of OSA have been discussed, separately.

  13. Can we Observe and Assess Whether the Global Hydrological Cycle is "Intensifying"?

    Wood, E. F.; Sheffield, J.

    2012-12-01

    There is controversy over whether the hydrological cycle is "intensifying" (or "accelerating"), and if so how and where? Resolving this critical question is a central goal of both national (e.g. NASA's Energy and Water cycle Study: NEWS) and international (WCRP Global Energy and Water cycle Experiment: GEWEX) programs. Its resolution has significant implications for understanding changes in hydroclimatic states and variability, and in future water security at regional to global scales. Over the last decade a number of papers have addressed trends and change in specific water cycle variables with results that can best be described as inconclusive, regardless of the conclusions of specific papers. In this presentation a number of recent studies will be reviewed for their consistency in assessing whether collectively one can make conclusions regarding how the hydrologic cycle is changing. The presentation will also demonstrate a pathway for analyzing where to observe for the detection of change based on a NASA-supported, global, 1983-2009, terrestrial water cycle Earth System Data Record project being led by the author. Initial results will be presented and a discussion presented on the extent that the proposed strategy can be used to detect change in the terrestrial hydrological cycle.

  14. The CEOS Global Observation Strategy for Disaster Risk Management: An Enterprise Architect's View

    Moe, K.; Evans, J. D.; Frye, S.

    2013-12-01

    The Committee on Earth Observation Satellites (CEOS) Working Group on Information Systems and Services (WGISS), on behalf of the Global Earth Observation System of Systems (GEOSS), is defining an enterprise architecture (known as GA.4.D) for the use of satellite observations in international disaster management. This architecture defines the scope and structure of the disaster management enterprise (based on disaster types and phases); its processes (expressed via use cases / system functions); and its core values (in particular, free and open data sharing via standard interfaces). The architecture also details how a disaster management enterprise describes, obtains, and handles earth observations and data products for decision-support; and how it draws on distributed computational services for streamlined operational capability. We have begun to apply this architecture to a new CEOS initiative, the Global Observation Strategy for Disaster Risk Management (DRM). CEOS is defining this Strategy based on the outcomes of three pilot projects focused on seismic hazards, volcanoes, and floods. These pilots offer a unique opportunity to characterize and assess the impacts (benefits / costs) of the GA.4.D architecture in practice. In particular, the DRM Floods Pilot is applying satellite-based optical and radar data to flood mitigation, warning, and response, including monitoring and modeling at regional to global scales. It is focused on serving user needs and building local institutional / technical capacity in the Caribbean, Southern Africa, and Southeast Asia. In the context of these CEOS DRM Pilots, we are characterizing where and how the GA.4D architecture helps participants to: - Understand the scope and nature of hazard events quickly and accurately - Assure timely delivery of observations into analysis, modeling, and decision-making - Streamline user access to products - Lower barriers to entry for users or suppliers - Streamline or focus field operations in

  15. Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions

    Yanxu Zhang,; Daniel J. Jacob,; Hannah M. Horowitz,; Long Chen,; Helen M. Amos,; Krabbenhoft, David P.; Franz Slemr,; Vincent L. St. Louis,; Elsie M. Sunderland,

    2015-01-01

    Observations of elemental mercury (Hg0) at sites in North America and Europe show large decreases (∼1–2% y−1) from 1990 to present. Observations in background northern hemisphere air, including Mauna Loa Observatory (Hawaii) and CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) aircraft flights, show weaker decreases (inventories indicating flat or increasing emissions over that period. However, the inventories have three major flaws: (i) they do not account for the decline in atmospheric release of Hg from commercial products; (ii) they are biased in their estimate of artisanal and small-scale gold mining emissions; and (iii) they do not properly account for the change in Hg0/HgII speciation of emissions from coal-fired utilities after implementation of emission controls targeted at SO2 and NOx. We construct an improved global emission inventory for the period 1990 to 2010 accounting for the above factors and find a 20% decrease in total Hg emissions and a 30% decrease in anthropogenic Hg0 emissions, with much larger decreases in North America and Europe offsetting the effect of increasing emissions in Asia. Implementation of our inventory in a global 3D atmospheric Hg simulation [GEOS-Chem (Goddard Earth Observing System-Chemistry)] coupled to land and ocean reservoirs reproduces the observed large-scale trends in atmospheric Hg0 concentrations and in HgII wet deposition. The large trends observed in North America and Europe reflect the phase-out of Hg from commercial products as well as the cobenefit from SO2 and NOx emission controls on coal-fired utilities.

  16. The Global Geodetic Observing System: Space Geodesy Networks for the Future

    Pearlman, Michael; Pavlis, Erricos; Ma, Chopo; Altamini, Zuheir; Noll, Carey; Stowers, David

    2011-01-01

    Ground-based networks of co-located space geodetic techniques (VLBI, SLR, GNSS. and DORIS) are the basis for the development and maintenance of the International Terrestrial Reference frame (ITRF), which is our metric of reference for measurements of global change, The Global Geodetic Observing System (GGOS) of the International Association of Geodesy (IAG) has established a task to develop a strategy to design, integrate and maintain the fundamental geodetic network and supporting infrastructure in a sustainable way to satisfy the long-term requirements for the reference frame. The GGOS goal is an origin definition at 1 mm or better and a temporal stability on the order of 0.1 mm/y, with similar numbers for the scale and orientation components. These goals are based on scientific requirements to address sea level rise with confidence, but other applications are not far behind. Recent studies including one by the US National Research Council has strongly stated the need and the urgency for the fundamental space geodesy network. Simulations are underway to examining accuracies for origin, scale and orientation of the resulting ITRF based on various network designs and system performance to determine the optimal global network to achieve this goal. To date these simulations indicate that 24 - 32 co-located stations are adequate to define the reference frame and a more dense GNSS and DORIS network will be required to distribute the reference frame to users anywhere on Earth. Stations in the new global network will require geologically stable sites with good weather, established infrastructure, and local support and personnel. GGOS wil seek groups that are interested in participation. GGOS intends to issues a Call for Participation of groups that would like to contribute in the network implementation and operation. Some examples of integrated stations currently in operation or under development will be presented. We will examine necessary conditions and challenges in

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

    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

  18. Multiple ground-based and satellite observations of global Pi 2 magnetic pulsations

    Yumoto, K.; Takahashi, K.; Sakurai, T.; Sutcliffe, P.R.; Kokubun, S.; Luehr, H.; Saito, T.; Kuwashima, M.; Sato, N.

    1990-01-01

    Four Pi 2 magnetic pulsations, observed on the ground at L = 1.2-6.9 in the interval from 2,300 UT on May 22 to 0300 UT on May 23, 1985, provide new evidence of a global nature of Pi 2 pulsations in the inner (L approx-lt 7) region of the magnetosphere bounded by the plasma sheet during quiet geomagnetic conditions. In the present study, magnetic data have been collected from stations distributed widely both in local time and in latitude, including conjugate stations, and from the AMPTE/CCE spacecraft located in the magnetotail. On the basis of high time resolution magnetic field data, the following characteristics of Pi 2 have been established: horizontal components, H and D, of the Pi 2 oscillate nearly antiphase and in-phase, respectively, between the high- and low-altitude stations in the midnight southern hemisphere. Both the H and D components of the Pi 2 have nearly in-phase relationships between the nightside and the dayside stations at low latitude. The Pi 2 amplitude is larger at the high-latitude station and decreases toward lower latitudes. The dominant periods of the Pi 2 are nearly identical at all stations. Although a direct coincidence between spacecraft-observed and ground-based global Pi 2 events does not exist for these events, the Pi 2 events are believed to be a forced field line oscillation of global scale, coupled with the magnetospheric cavity resonance wave in the inner magnetosphere during the substorm expansive phase

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

    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.

  20. Data assimilation techniques in modeling ocean processes

    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...

  1. Observations and global numerical modelling of the St. Patrick's Day 2015 geomagnetic storm event

    Foerster, M.; Prokhorov, B. E.; Doornbos, E.; Astafieva, E.; Zakharenkova, I.

    2017-12-01

    With a sudden storm commencement (SSC) at 04:45 UT on St. Patrick's day 2015 started the most severe geomagnetic storm in solar cycle 24. It appeared as a two-stage geomagnetic storm with a minimum SYM-H value of -233 nT. In the response to the storm commencement in the first activation, a short-term positive effect in the ionospheric vertical electron content (VTEC) occurred at low- and mid-latitudes on the dayside. The second phase commencing around 12:30 UT lasted longer and caused significant and complex storm-time changes around the globe with hemispherical different ionospheric storm reactions in different longitudinal ranges. Swarm-C observations of the neutral mass density variation along the orbital path as well as Langmuir probe plasma and magnetometer measurements of all three Swarm satellites and global TEC records are used for physical interpretations and modelling of the positive/negative storm scenario. These observations pose a challenge for the global numerical modelling of thermosphere-ionosphere storm processes as the storm, which occurred around spring equinox, obviously signify the existence of other impact factors than seasonal dependence for hemispheric asymmetries to occur. Numerical simulation trials using the Potsdam version of the Upper Atmosphere Model (UAM-P) are presented to explain these peculiar M-I-T storm processes.

  2. Effects of finite coverage on global polarization observables in heavy ion collisions

    Lan, Shaowei; Lin, Zi-Wei; Shi, Shusu; Sun, Xu

    2018-05-01

    In non-central relativistic heavy ion collisions, the created matter possesses a large initial orbital angular momentum. Particles produced in the collisions could be polarized globally in the direction of the orbital angular momentum due to spin-orbit coupling. Recently, the STAR experiment has presented polarization signals for Λ hyperons and possible spin alignment signals for ϕ mesons. Here we discuss the effects of finite coverage on these observables. The results from a multi-phase transport and a toy model both indicate that a pseudorapidity coverage narrower than | η | value for the extracted ϕ-meson ρ00 parameter; thus a finite coverage can lead to an artificial deviation of ρ00 from 1/3. We also show that a finite η and pT coverage affect the extracted pH parameter for Λ hyperons when the real pH value is non-zero. Therefore proper corrections are necessary to reliably quantify the global polarization with experimental observables.

  3. Global Observations of the 630-nm Nightglow and Patterns of Brightness Measured by ISUAL

    Chih-Yu Chiang

    2013-01-01

    Full Text Available This study investigates the distributions and occurrence mechanisms of the global local-midnight airglow brightness through FORMOSAT-2/ISUAL satellite imaging observations. We focus on the OI 630.0 nm nightglow emission at altitudes of ~250 km along equatorial space. The database used in this study included data from 2007 to 2008 under solar minimum conditions. The data were classified into four specified types in the statistical study. We found that the occurrence of equatorial brightness was often in the vicinity of the geographic equator and mostly at equinoxes with a tendency to move toward the summer hemisphere as the season changes. Conjugate brightness occurring simultaneously on both sides of the geomagnetic equator was observed predominantly in the northern winter. Furthermore, midnight brightness appeared to have lower luminosity from May to July. We suggest that the global midnight brightness associated with the locations and seasons was the result of several effects which include the influence of the thermospheric midnight temperature maximum (MTM, summer-to-winter neutral wind, and ionospheric anomalies.

  4. Long-term climate monitoring by the global climate observing system

    Karl, T.R.

    1995-12-01

    Is the climate warming? Is the hydrologic cycle changing? Is the atmospheric/oceanic circulation changing? Is the climate becoming more variable or extreme? Is radiative forcing of the climate changing? are complex questions not only from the standpoint of a multi-variate problem, but because of the various aspects of spatial and temporal sampling that must be considered on a global scale. The development of a Global Climate Observing System (GCOS) offers the opportunity for scientists to do something about existing observing deficiencies in light of the importance of documenting long-term climate changes that may already be affected by anthropogenic changes of atmospheric composition and land use as well as other naturally occurring changes. As an important step toward improving the present inadequacies, a workshop was held to help define the long-term monitoring requirements minimally needed to address the five questions posed above, with special emphasis on detecting anthropogenic climate change and its potential impact on managed and unmanaged systems The workshop focussed on three broad areas related to long-term climate monitoring: (a) the scientific rationale for the long-term climate products (including their accuracy, resolution, and homogeneity) required from our observing systems as related to climate monitoring and climate change detection and attribution; (b) the status of long-term climate products and the observing systems from which these data are derived; and (c) implementation strategies necessary to fulfill item (a) in light of existing systems. Item (c) was treated more in terms of feasibility rather than as a specific implementation plan. figs., tabs., refs

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

    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.

  6. Globalization

    Tulio Rosembuj

    2006-12-01

    Full Text Available There is no singular globalization, nor is the result of an individual agent. We could start by saying that global action has different angles and subjects who perform it are different, as well as its objectives. The global is an invisible invasion of materials and immediate effects.

  7. Globalization

    Tulio Rosembuj

    2006-01-01

    There is no singular globalization, nor is the result of an individual agent. We could start by saying that global action has different angles and subjects who perform it are different, as well as its objectives. The global is an invisible invasion of materials and immediate effects.

  8. Evaluation of a Soil Moisture Data Assimilation System Over the Conterminous 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

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

    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.

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

    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.

  11. The future for the Global Sea Level Observing System (GLOSS) Sea Level Data Rescue

    Bradshaw, Elizabeth; Matthews, Andrew; Rickards, Lesley; Aarup, Thorkild

    2016-04-01

    Historical sea level data are rare and unrepeatable measurements with a number of applications in climate studies (sea level rise), oceanography (ocean currents, tides, surges), geodesy (national datum), geophysics and geology (coastal land movements) and other disciplines. However, long-term time series are concentrated in the northern hemisphere and there are no records at the Permanent Service for Mean Sea Level (PSMSL) global data bank longer than 100 years in the Arctic, Africa, South America or Antarctica. Data archaeology activities will help fill in the gaps in the global dataset and improve global sea level reconstruction. The Global Sea Level Observing System (GLOSS) is an international programme conducted under the auspices of the WMO-IOC Joint Technical Commission for Oceanography and Marine Meteorology. It was set up in 1985 to collect long-term tide gauge observations and to develop systems and standards "for ocean monitoring and flood warning purposes". At the GLOSS-GE-XIV Meeting in 2015, GLOSS agreed on a number of action items to be developed in the next two years. These were: 1. To explore mareogram digitisation applications, including NUNIEAU (more information available at: http://www.mediterranee.cerema.fr/logiciel-de-numerisation-des-enregistrements-r57.html) and other recent developments in scanning/digitisation software, such as IEDRO's Weather Wizards program, to see if they could be used via a browser. 2. To publicise sea level data archaeology and rescue by: • maintaining and regularly updating the Sea Level Data Archaeology page on the GLOSS website • strengthening links to the GLOSS data centres and data rescue organisations e.g. linking to IEDRO, ACRE, RDA • restarting the sea level data rescue blog with monthly posts. 3. Investigate sources of funding for data archaeology and rescue projects. 4. Propose "Guidelines" for rescuing sea level data. These action items will aid the discovery, scanning, digitising and quality control

  12. Global observations and modeling of atmosphere-surface exchange of elemental mercury: a critical review

    Zhu, Wei; Lin, Che-Jen; Wang, Xun; Sommar, Jonas; Fu, Xuewu; Feng, Xinbin

    2016-04-01

    Reliable quantification of air-surface fluxes of elemental Hg vapor (Hg0) is crucial for understanding mercury (Hg) global biogeochemical cycles. There have been extensive measurements and modeling efforts devoted to estimating the exchange fluxes between the atmosphere and various surfaces (e.g., soil, canopies, water, snow, etc.) in the past three decades. However, large uncertainties remain due to the complexity of Hg0 bidirectional exchange, limitations of flux quantification techniques and challenges in model parameterization. In this study, we provide a critical review on the state of science in the atmosphere-surface exchange of Hg0. Specifically, the advancement of flux quantification techniques, mechanisms in driving the air-surface Hg exchange and modeling efforts are presented. Due to the semi-volatile nature of Hg0 and redox transformation of Hg in environmental media, Hg deposition and evasion are influenced by multiple environmental variables including seasonality, vegetative coverage and its life cycle, temperature, light, moisture, atmospheric turbulence and the presence of reactants (e.g., O3, radicals, etc.). However, the effects of these processes on flux have not been fundamentally and quantitatively determined, which limits the accuracy of flux modeling. We compile an up-to-date global observational flux database and discuss the implication of flux data on the global Hg budget. Mean Hg0 fluxes obtained by micrometeorological measurements do not appear to be significantly greater than the fluxes measured by dynamic flux chamber methods over unpolluted surfaces (p = 0.16, one-tailed, Mann-Whitney U test). The spatiotemporal coverage of existing Hg0 flux measurements is highly heterogeneous with large data gaps existing in multiple continents (Africa, South Asia, Middle East, South America and Australia). The magnitude of the evasion flux is strongly enhanced by human activities, particularly at contaminated sites. Hg0 flux observations in East

  13. Global observations and modeling of atmosphere–surface exchange of elemental mercury: a critical review

    W. Zhu

    2016-04-01

    Full Text Available Reliable quantification of air–surface fluxes of elemental Hg vapor (Hg0 is crucial for understanding mercury (Hg global biogeochemical cycles. There have been extensive measurements and modeling efforts devoted to estimating the exchange fluxes between the atmosphere and various surfaces (e.g., soil, canopies, water, snow, etc. in the past three decades. However, large uncertainties remain due to the complexity of Hg0 bidirectional exchange, limitations of flux quantification techniques and challenges in model parameterization. In this study, we provide a critical review on the state of science in the atmosphere–surface exchange of Hg0. Specifically, the advancement of flux quantification techniques, mechanisms in driving the air–surface Hg exchange and modeling efforts are presented. Due to the semi-volatile nature of Hg0 and redox transformation of Hg in environmental media, Hg deposition and evasion are influenced by multiple environmental variables including seasonality, vegetative coverage and its life cycle, temperature, light, moisture, atmospheric turbulence and the presence of reactants (e.g., O3, radicals, etc.. However, the effects of these processes on flux have not been fundamentally and quantitatively determined, which limits the accuracy of flux modeling. We compile an up-to-date global observational flux database and discuss the implication of flux data on the global Hg budget. Mean Hg0 fluxes obtained by micrometeorological measurements do not appear to be significantly greater than the fluxes measured by dynamic flux chamber methods over unpolluted surfaces (p = 0.16, one-tailed, Mann–Whitney U test. The spatiotemporal coverage of existing Hg0 flux measurements is highly heterogeneous with large data gaps existing in multiple continents (Africa, South Asia, Middle East, South America and Australia. The magnitude of the evasion flux is strongly enhanced by human activities, particularly at contaminated sites. Hg0

  14. Ethnicity, assimilation and harassment in the labor market

    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...

  15. Semantics-enabled knowledge management for global Earth observation system of systems

    King, Roger L.; Durbha, Surya S.; Younan, Nicolas H.

    2007-10-01

    The Global Earth Observation System of Systems (GEOSS) is a distributed system of systems built on current international cooperation efforts among existing Earth observing and processing systems. The goal is to formulate an end-to-end process that enables the collection and distribution of accurate, reliable Earth Observation data, information, products, and services to both suppliers and consumers worldwide. One of the critical components in the development of such systems is the ability to obtain seamless access of data across geopolitical boundaries. In order to gain support and willingness to participate by countries around the world in such an endeavor, it is necessary to devise mechanisms whereby the data and the intellectual capital is protected through procedures that implement the policies specific to a country. Earth Observations (EO) are obtained from a multitude of sources and requires coordination among different agencies and user groups to come to a shared understanding on a set of concepts involved in a domain. It is envisaged that the data and information in a GEOSS context will be unprecedented and the current data archiving and delivery methods need to be transformed into one that allows realization of seamless interoperability. Thus, EO data integration is dependent on the resolution of conflicts arising from a variety of areas. Modularization is inevitable in distributed environments to facilitate flexible and efficient reuse of existing ontologies. Therefore, we propose a framework for modular ontologies based knowledge management approach for GEOSS and present methods to enable efficient reasoning in such systems.

  16. Global variation in the long-term seasonal changes observed in ionospheric F region data

    C. J. Scott

    2015-04-01

    Full Text Available Long-term variability has previously been observed in the relative magnitude of annual and semi-annual variations in the critical frequency (related to the peak electron concentration of the ionospheric F2 layer (foF2. In this paper we investigate the global patterns in such variability by calculating the time varying power ratio of semi-annual to annual components seen in ionospheric foF2 data sequences from 77 ionospheric monitoring stations around the world. The temporal variation in power ratios observed at each station was then correlated with the same parameter calculated from similar epochs for the Slough/Chilton data set (for which there exists the longest continuous sequence of ionospheric data. This technique reveals strong regional variation in the data, which bears a striking similarity to the regional variation observed in long-term changes to the height of the ionospheric F2 layer. We argue that since both the height and peak density of the ionospheric F2 region are influenced by changes to thermospheric circulation and composition, the observed long-term and regional variability can be explained by such changes. In the absence of long-term measurements of thermospheric composition, detailed modelling work is required to investigate these processes.

  17. The Global-Scale Observations of the Limb and Disk (GOLD) Mission

    Eastes, R. W.; McClintock, W. E.; Burns, A. G.; Anderson, D. N.; Andersson, L.; Codrescu, M.; Correira, J. T.; Daniell, R. E.; England, S. L.; Evans, J. S.; Harvey, J.; Krywonos, A.; Lumpe, J. D.; Richmond, A. D.; Rusch, D. W.; Siegmund, O.; Solomon, S. C.; Strickland, D. J.; Woods, T. N.; Aksnes, A.; Budzien, S. A.; Dymond, K. F.; Eparvier, F. G.; Martinis, C. R.; Oberheide, J.

    2017-10-01

    The Earth's thermosphere and ionosphere constitute a dynamic system that varies daily in response to energy inputs from above and from below. This system can exhibit a significant response within an hour to changes in those inputs, as plasma and fluid processes compete to control its temperature, composition, and structure. Within this system, short wavelength solar radiation and charged particles from the magnetosphere deposit energy, and waves propagating from the lower atmosphere dissipate. Understanding the global-scale response of the thermosphere-ionosphere ( T-I) system to these drivers is essential to advancing our physical understanding of coupling between the space environment and the Earth's atmosphere. Previous missions have successfully determined how the "climate" of the T-I system responds. The Global-scale Observations of the Limb and Disk (GOLD) mission will determine how the "weather" of the T-I responds, taking the next step in understanding the coupling between the space environment and the Earth's atmosphere. Operating in geostationary orbit, the GOLD imaging spectrograph will measure the Earth's emissions from 132 to 162 nm. These measurements will be used image two critical variables—thermospheric temperature and composition, near 160 km—on the dayside disk at half-hour time scales. At night they will be used to image the evolution of the low latitude ionosphere in the same regions that were observed earlier during the day. Due to the geostationary orbit being used the mission observes the same hemisphere repeatedly, allowing the unambiguous separation of spatial and temporal variability over the Americas.

  18. A global climatology of stratospheric gravity waves from Atmospheric Infrared Sounder observations

    Hoffmann, Lars; Xue, Xianghui; Alexander, M. Joan

    2014-05-01

    We present the results of a new study that aims on the detection and classification of `hotspots' of stratospheric gravity waves on a global scale. The analysis is based on a nine-year record (2003 to 2011) of radiance measurements by the Atmospheric Infrared Sounder (AIRS) aboard NASA's Aqua satellite. We detect the presence of stratospheric gravity waves based on 4.3 micron brightness temperature variances. Our method is optimized for peak events, i.e., strong gravity wave events for which the local variance considerably exceeds background levels. We estimated the occurrence frequencies of these peak events for different seasons and time of day and used the results to find local maxima of gravity wave activity. In addition, we use AIRS radiances at 8.1 micron to simultaneously detect convective events, including deep convection in the tropics and mesoscale convective systems at mid latitudes. We classified the gravity waves according to their sources, based on seasonal occurrence frequencies for convection and by means of topographic data. Our study reproduces well-known hotspots of gravity waves, e.g., the mountain wave hotspots at the Andes and the Antarctic Peninsula or the convective hotspot during the thunderstorm season over the North American Great Plains. However, the high horizontal resolution of the AIRS observations also helped us to locate several smaller hotspots, which were partly unknown or poorly studied so far. Most of these smaller hotspots are found near orographic features like small mountain ranges, in coastal regions, in desert areas, or near isolated islands. This new study will help to select the most promising regions and seasons for future observational studies of gravity waves. Reference: Hoffmann, L., X. Xue, and M. J. Alexander, A global view of stratospheric gravity wave hotspots located with Atmospheric Infrared Sounder observations, J. Geophys. Res., 118, 416-434, doi:10.1029/2012JD018658, 2013.

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

    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.

  20. Development of a New Research Data Infrastructure for Collaboration in Earth Observation and Global Change Science

    Wagner, Wolfgang; Briese, Christian

    2017-04-01

    With the global population having surpassed 7 billion people in 2012, the impacts of human activities on the environment have started to be noticeable almost everywhere on our planet. Yet, while pressing social problems such as mass migration may be at least be partly a consequence of these impacts, many are still elusive, particularly when trying to quantify them on larger scales. Therefore, it is essential to collect verifiable observations that allow tracing environmental changes from a local to global scale over several decades. Complementing in situ networks, this task is increasingly fulfilled by earth observation satellites which have been acquiring measurements of the land, atmosphere and oceans since the beginning of the 1970s. While many multi-decadal data sets are already available, the major limitation hindering their effective exploitation in global change studies is the lack of dedicated data centres offering the high performance processing capabilities needed to process multi-year global data sets at a fine spatial resolution (Wagner, 2015). Essentially the only platform which currently offers these capabilities is Google's Earth Engine. From a scientific perspective there is undoubtedly a high need to build up independent science-driven platforms that are transparent for their users and offer a higher diversity and flexibility in terms of the data sets and algorithms used. Recognizing this need, TU Wien founded the EODC Earth Observation Data Centre for Water Resources Monitoring together with other Austrian partners in May 2014 as a public-private partnership (Wagner et al. 2014). Thanks to its integrative governance approach, EODC has succeeded of quickly developing an international cooperation consisting of scientific institutions, public organisations and several private partners. Making best use of their existing infrastructures, the EODC partners have already created the first elements of a federated IT infrastructure capable of storing and

  1. Euro-Argo: The European contribution to the global Argo ocean observations network

    Gourcuff, Claire

    2017-04-01

    The international Argo programme is a major element of the global in-situ ocean observing system. More than 3900 floats are now globally measuring temperature and salinity throughout the global oceans, down to 2,000 meters depth and delivering data both in real time for operational users and after careful scientific quality control for climate change research and monitoring. Argo is the single most important in-situ observing system for the Copernicus Marine Service. The Euro-Argo research infrastructure organizes and federates European contribution to Argo. A legal and governance framework (Euro-Argo ERIC) was set up in May 2014; it allows European countries to consolidate and improve their contribution to Argo international. We will provide an overview of the development of Euro-Argo over the past years and present the now agreed Euro-Argo long term organization. The capability of the Euro-Argo infrastructure to organize Argo floats procurement, deployment and processing at European level and to conduct R&D driven by Copernicus needs will be highlighted. During the recent years, within the H2020 E-AIMS project, Euro-Argo carried R&D activities on new Argo floats, equipped with biogeochemical sensors or able to dive up to 4000m, from the floats design up to the analysis of their measurements. European Argo data centers were adapted so that they can handle the new data. Observing System Evaluations and Simulation Experiments were also conducted to provide robust recommendations for the next phase of Argo. One of the main challenges for Euro-Argo is now to implement the next phase of Argo with an extension towards biogeochemistry (e.g. oxygen, biology), the polar oceans, the marginal seas and the deep ocean. Meeting such challenges is essential for the long term sustainability and evolution of the Copernicus Marine Service. We will present Euro-Argo strategy and provide some highlights on the implementation-plan for the years to come and the Argo extensions for the

  2. Energetic neutral atom and interstellar flow observations with IBEX: Implications for the global heliosphere

    Schwadron, N. A., E-mail: nschwadron@unh.edu [University of New Hampshire, Durham NH, 03824 (United States); Southwest Research Institute, San Antonio, TX, 78238 (United States); McComas, D. J.; Desai, M. I.; Fuselier, S. A. [Southwest Research Institute, San Antonio, TX, 78238 (United States); University of Texas, San Antonio, TX, 78249 (United States); Christian, E. R. [Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Funsten, H. O. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Moebius, E. [University of New Hampshire, Durham NH, 03824 (United States); Reno, M.; Scherrer, J.; Zirnstein, E. [Southwest Research Institute, San Antonio, TX, 78238 (United States)

    2016-03-25

    Since launch in Oct. 2008, IBEX, with its two energetic neutral atom (ENA) cameras, has provided humankind with the first-ever global images of the complex boundary separating the heliosphere from the local interstellar medium (LISM). IBEX’s energy-resolved all-sky maps, collected every six months, are yielding remarkable new insights into the heliospheres structure as it is shaped by the combined forces of the local interstellar flow, the local interstellar magnetic field (LISMF), and the evolving solar wind. IBEX has also acquired the first images of ENAs backscattered from the surface of the moon as well as global images of the magnetospheric response to solar wind disturbances. IBEX thus addresses all three Heliophysics science objectives set forth in the 2014 Science Plan for NASAs Science Mission Directorate (SMD) as well as the goals in the recent Solar and Space Physics Decadal Survey (NRC 2012). In addition, with the information it provides on the properties of the LISM and the LISMF, IBEX represents a unique bridge between heliophysics and astrophysics, and fills in critical knowledge for understanding the habitability of exoplanetary systems and the future habitability of Earth and the solar system. Because of the few-year time lag due to solar wind and ENA transport, IBEX observed the solar wind/ LISM interaction characteristic of declining phase/solar minimum conditions. In the continuing mission, IBEX captures the response of the interstellar boundaries to the changing structure of the solar wind in its transition toward the “mini” solar maximum and possibly the decline into the next solar minimum. The continuing IBEX mission affords never-to-be-repeated opportunities to coordinate global imaging of the heliospheric boundary with in-situ measurements by the Voyagers as they pass beyond the heliopause and start to directly sample the LISM.

  3. The pricing and procurement of antiretroviral drugs: an observational study of data from the Global Fund.

    Vasan, Ashwin; Hoos, David; Mukherjee, Joia S; Farmer, Paul E; Rosenfield, Allan G; Perriëns, Joseph H

    2006-05-01

    The Purchase price report released in August 2004 by the Global Fund to Fight AIDS, Tuberculosis, and Malaria (Global Fund) was the first publication of a significant amount of real transaction purchase data for antiretrovirals (ARVs). We did an observational study of the ARV transaction data in the Purchase price report to examine the procurement behaviour of principal recipients of Global Fund grants in developing countries. We found that, with a few exceptions for specific products (e.g. lamivudine) and regions (e.g. eastern Europe), prices in low-income countries were broadly consistent or lower than the lowest differential prices quoted by the research and development sector of the pharmaceutical industry. In lower middle-income countries, prices were more varied and in several instances (lopinavir/ritonavir, didanosine, and zidovudine/lamivudine) were very high compared with the per capita income of the country. In all low- and lower middle-income countries, ARV prices were still significantly high given limited local purchasing power and economic strength, thus reaffirming the need for donor support to achieve rapid scale-up of antiretroviral therapy. However, the price of ARVs will have to decrease to render scale-up financially sustainable for donors and eventually for governments themselves. An important first step in reducing prices will be to make available in the public domain as much ARV transaction data as possible to provide a factual basis for discussions on pricing. The price of ARVs has considerable implications for the sustainability of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) treatment in the developing world.

  4. Variations of global gravity waves derived from 14 years of SABER temperature observations

    Liu, Xiao; Yue, Jia; Xu, Jiyao; Garcia, Rolando R.; Russell, James M.; Mlynczak, Martin; Wu, Dong L.; Nakamura, Takuji

    2017-06-01

    The global gravity wave (GW) potential energy (PE) per unit mass is derived from SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) temperature profiles over the past 14 years (2002-2015). Since the SABER data cover longer than one solar cycle, multivariate linear regression is applied to calculate the trend (means linear trend from 2002 to 2015) of global GW PE and the responses of global GW PE to solar activity, to QBO (quasi-biennial oscillation) and to ENSO (El Niño-Southern Oscillation). We find a significant positive trend of GW PE at around 50°N during July from 2002 to 2015, in agreement with ground-based radar observations at a similar latitude but from 1990 to 2010. Both the monthly and the deseasonalized trends of GW PE are significant near 50°S. Specifically, the deseasonalized trend of GW PE has a positive peak of 12-15% per decade at 40°S-50°S and below 60 km, which suggests that eddy diffusion is increasing in some places. A significant positive trend of GW PE near 50°S could be due to the strengthening of the polar stratospheric jets, as documented from Modern Era Retrospective-analysis for Research and Applications wind data. The response of GW PE to solar activity is negative in the lower and middle latitudes. The response of GW PE to QBO (as indicated by 30 hPa zonal winds over the equator) is negative in the tropical upper stratosphere and extends to higher latitudes at higher altitudes. The response of GW PE to ENSO (as indicated by the Multivariate ENSO Index) is positive in the tropical upper stratosphere.

  5. Data assimilation in hydrological modelling

    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...

  6. Data Assimilation in Marine Models

    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...

  7. Dark matter assimilation into the baryon asymmetry

    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

  8. Intercomparison of 4 Years of Global Formaldehyde Observations from the GOME-2 and OMI Sensors

    De Smedt, Isabelle; Van Roozendael, Michel; Stravrakou, Trissevgeni; Muller, Jean-Francois; Chance, Kelly; Kurosu, Thomas

    2012-11-01

    Formaldehyde (H2CO) tropospheric columns have been retrieved since 2007 from backscattered UV radiance measurements performed by the GOME-2 instrument on the EUMETSAT METOP-A platform. This data set extends the successful time-series of global H2CO observations established with GOME/ ERS-2 (1996-2003), SCIAMACHY/ ENVISAT (2003-2012), and OMI on the NASA AURA platform (2005-now). In this work, we perform an intercomparison of the H2CO tropospheric columns retrieved from GOME-2 and OMI between 2007 and 2010, respectively at BIRA-IASB and at Harvard SAO. We first compare the global formaldehyde data products that are provided by each retrieval group. We then investigate each step of the retrieval procedure: the slant column fitting, the reference sector correction and the air mass factor calculation. New air mass factors are computed for OMI using external parameters consistent with those used for GOME-2. By doing so, the impacts of the different a priori profiles and aerosol corrections are quantified. The remaining differences are evaluated in view of the expected diurnal variations of the formaldehyde concentrations, based on ground-based measurements performed in the Beijing area.

  9. Comparison of Sequential and Variational Data Assimilation

    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.

  10. Data assimilation and model evaluation experiment datasets

    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.

  11. Recent Trends in Global Ocean Chlorophyll

    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.

  12. Observation of Global Hyperon Polarization in Ultrarelativistic Heavy-Ion Collisions

    Upsal, Isaac; STAR Collaboration

    2017-11-01

    Collisions between heavy nuclei at ultra-relativistic energies form a color-deconfined state of matter known as the quark-gluon plasma. This state is well described by hydrodynamics, and non-central collisions are expected to produce a fluid characterized by strong vorticity in the presence of strong external magnetic fields. The STAR Collaboration at Brookhaven National Laboratory's Relativistic Heavy Ion Collider (RHIC) has measured collisions between gold nuclei at center of mass energies √{sNN} = 7.7- 200 GeV. We report the first observation of globally polarized Λ and Λ bar hyperons, aligned with the angular momentum of the colliding system. These measurements provide important information on partonic spin-orbit coupling, the vorticity of the quark-gluon plasma, and the magnetic field generated in the collision.

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

    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.

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

    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.

  15. Global-scale Observations of the Limb and Disk (GOLD): Science Implementation

    Solomon, S. C.; McClintock, W. E.; Eastes, R.; Anderson, D. N.; Andersson, L.; Burns, A. G.; Codrescu, M.; Daniell, R. E.; England, S.; Eparvier, F. G.; Evans, J. S.; Krywonos, A.; Lumpe, J. D.; Richmond, A. D.; Rusch, D. W.; Siegmund, O.; Woods, T. N.

    2017-12-01

    The Global-scale Observations of the Limb and Disk (GOLD) is a NASA mission of opportunity that will image the Earth's thermosphere and ionosphere from geostationary orbit. GOLD will investigate how the thermosphere-ionosphere (T-I) system responds to geomagnetic storms, solar radiation, and upward propagating tides and how the structure of the equatorial ionosphere influences the formation and evolution of equatorial plasma density irregularities. GOLD consists of a pair of identical imaging spectrographs that will measure airglow emissions at far-ultraviolet wavelengths from 132 to 162 nm. On the disk, temperature and composition will be determined during the day using emissions from molecular nitrogen Lyman-Birge-Hopfield (LBH) band and atomic oxygen 135.6 nm, and electron density will be derived at night from 135.6 nm emission. On the limb, exospheric temperature will be derived from LBH emission profiles, and molecular oxygen density will be measured using stellar occultations. This presentation describes the GOLD mission science implementation including the as-built instrument performance and the planned observing scenario. It also describes the results of simulations performed by the GOLD team to validate that the measured instrument performance and observing plan will return adequate data to address the science objectives of the mission.

  16. Global observations of tropospheric BrO columns using GOME-2 satellite data

    Theys, N.; van Roozendael, M.; Hendrick, F.; Yang, X.; de Smedt, I.; Richter, A.; Begoin, M.; Errera, Q.; Johnston, P. V.; Kreher, K.; de Mazière, M.

    2011-02-01

    Measurements from the GOME-2 satellite instrument have been analyzed for tropospheric BrO using a residual technique that combines measured BrO columns and estimates of the stratospheric BrO content from a climatological approach driven by O3 and NO2 observations. Comparisons between the GOME-2 results and BrO vertical columns derived from correlative ground-based and SCIAMACHY nadir observations, present a good level of consistency. We show that the adopted technique enables separation of stratospheric and tropospheric fractions of the measured total BrO columns and allows quantitative study of the BrO plumes in polar regions. While some satellite observed plumes of enhanced BrO can be explained by stratospheric descending air, we show that most BrO hotspots are of tropospheric origin, although they are often associated to regions with low tropopause heights as well. Elaborating on simulations using the p-TOMCAT tropospheric chemical transport model, this result is found to be consistent with the mechanism of bromine release through sea salt aerosols production during blowing snow events. No definitive conclusion can be drawn however on the importance of blowing snow sources in comparison to other bromine release mechanisms. Outside polar regions, evidence is provided for a global tropospheric BrO background with column of 1-3 × 1013 molec cm-2, consistent with previous estimates.

  17. Global observations of tropospheric BrO columns using GOME-2 satellite data

    N. Theys

    2011-02-01

    Full Text Available Measurements from the GOME-2 satellite instrument have been analyzed for tropospheric BrO using a residual technique that combines measured BrO columns and estimates of the stratospheric BrO content from a climatological approach driven by O3 and NO2 observations. Comparisons between the GOME-2 results and BrO vertical columns derived from correlative ground-based and SCIAMACHY nadir observations, present a good level of consistency. We show that the adopted technique enables separation of stratospheric and tropospheric fractions of the measured total BrO columns and allows quantitative study of the BrO plumes in polar regions. While some satellite observed plumes of enhanced BrO can be explained by stratospheric descending air, we show that most BrO hotspots are of tropospheric origin, although they are often associated to regions with low tropopause heights as well. Elaborating on simulations using the p-TOMCAT tropospheric chemical transport model, this result is found to be consistent with the mechanism of bromine release through sea salt aerosols production during blowing snow events. No definitive conclusion can be drawn however on the importance of blowing snow sources in comparison to other bromine release mechanisms. Outside polar regions, evidence is provided for a global tropospheric BrO background with column of 1–3 × 1013 molec cm−2, consistent with previous estimates.

  18. Improving operational flood forecasting through data assimilation

    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

  19. Globalization

    Andru?cã Maria Carmen

    2013-01-01

    The field of globalization has highlighted an interdependence implied by a more harmonious understanding determined by the daily interaction between nations through the inducement of peace and the management of streamlining and the effectiveness of the global economy. For the functioning of the globalization, the developing countries that can be helped by the developed ones must be involved. The international community can contribute to the institution of the development environment of the gl...

  20. Quantifying global fossil-fuel CO2 emissions: from OCO-2 to optimal observing designs

    Ye, X.; Lauvaux, T.; Kort, E. A.; Oda, T.; Feng, S.; Lin, J. C.; Yang, E. G.; Wu, D.; Kuze, A.; Suto, H.; Eldering, A.

    2017-12-01

    Cities house more than half of the world's population and are responsible for more than 70% of the world anthropogenic CO2 emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, should allow us to monitor changes in global fossil-fuel CO2 emissions, in an independent, objective way. Satellite platforms provide favorable temporal and spatial coverage to collect urban CO2 data to quantify the anthropogenic contributions to the global carbon budget. We present here the optimal observation design for future NASA's OCO-2 and Japanese GOSAT missions, based on real-data (i.e. OCO-2) experiments and Observing System Simulation Experiments (OSSE's) to address different error components in the urban CO2 budget calculation. We identify the major sources of emission uncertainties for various types of cities with different ecosystems and geographical features, such as urban plumes over flat terrains, accumulated enhancements within basins, and complex weather regimes in coastal areas. Atmospheric transport errors were characterized under various meteorological conditions using the Weather Research and Forecasting (WRF) model at 1-km spatial resolution, coupled to the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emissions. We propose and discuss the optimized urban sampling strategies to address some difficulties from the seasonality in cloud cover and emissions, vegetation density in and around cities, and address the daytime sampling bias using prescribed diurnal cycles. These factors are combined in pseudo data experiments in which we evaluate the relative impact of uncertainties on inverse estimates of CO2 emissions for cities across latitudinal and climatological zones. We propose here several sampling strategies to minimize the uncertainties in target mode for tracking urban fossil-fuel CO2 emissions over the globe for future satellite missions, such as OCO-3 and future

  1. Community Observatories: Fostering Ideas that STEM From Ocean Sense: Local Observations. Global Connections.

    Pelz, M. S.; Ewing, N.; Hoeberechts, M.; Riddell, D. J.; McLean, M. A.; Brown, J. C. K.

    2015-12-01

    Ocean Networks Canada (ONC) uses education and communication to inspire, engage and educate via innovative "meet them where they are, and take them where they need to go" programs. ONC data are accessible via the internet allowing for the promotion of programs wherever the learners are located. We use technologies such as web portals, mobile apps and citizen science to share ocean science data with many different audiences. Here we focus specifically on one of ONC's most innovative programs: community observatories and the accompanying Ocean Sense program. The approach is based on equipping communities with the same technology enabled on ONC's large cabled observatories. ONC operates the world-leading NEPTUNE and VENUS cabled ocean observatories and they collect data on physical, chemical, biological, and geological aspects of the ocean over long time periods, supporting research on complex Earth processes in ways not previously possible. Community observatories allow for similar monitoring on a smaller scale, and support STEM efforts via a teacher-led program: Ocean Sense. This program, based on local observations and global connections improves data-rich teaching and learning via visualization tools, interactive plotting interfaces and lesson plans for teachers that focus on student inquiry and exploration. For example, students use all aspects of STEM by accessing, selecting, and interpreting data in multiple dimensions, from their local community observatories to the larger VENUS and NEPTUNE networks. The students make local observations and global connections in all STEM areas. The first year of the program with teachers and students who use this innovative technology is described. Future community observatories and their technological applications in education, communication and STEM efforts are also described.

  2. Observations of urban and suburban environments with global satellite scatterometer data

    Nghiem, S. V.; Balk, D.; Rodriguez, E.; Neumann, G.; Sorichetta, A.; Small, C.; Elvidge, C. D.

    A global and consistent characterization of land use and land change in urban and suburban environments is crucial for many fundamental social and natural science studies and applications. Presented here is a dense sampling method (DSM) that uses satellite scatterometer data to delineate urban and intraurban areas at a posting scale of about 1 km. DSM results are analyzed together with information on population and housing censuses, with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, and with Defense Meteorological Satellite Program (DMSP) night-light data. The analyses include Dallas-Fort Worth and Phoenix in the United States, Bogotá in Colombia, Dhaka in Bangladesh, Guangzhou in China, and Quito in Ecuador. Results show that scatterometer signatures correspond to buildings and infrastructures in urban and suburban environments. City extents detected by scatterometer data are significantly smaller than city light extents, but not all urban areas are detectable by the current SeaWinds scatterometer on the QuikSCAT satellite. Core commercial and industrial areas with high buildings and large factories are identified as high-backscatter centers. Data from DSM backscatter and DMSP nighttime lights have a good correlation with population density. However, the correlation relations from the two satellite datasets are different for different cities indicating that they contain complementary information. Together with night-light and census data, DSM and satellite scatterometer data provide new observations to study global urban and suburban environments and their changes. Furthermore, the capability of DSM to identify hydrological channels on the Greenland ice sheet and ecological biomes in central Africa demonstrates that DSM can be used to observe persistent structures in natural environments at a km scale, providing contemporaneous data to study human impacts beyond urban and suburban areas.