Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST

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Weijing Chen

2017-03-01

Full Text Available Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperature (TB and MODIS (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (LST products, which also corrects model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter (DEnKS. Common Land Model (CoLM and a Radiative Transfer Model (RTM are adopted as model and observation operator, respectively. The assimilation experiment was conducted in Naqu on the Tibet Plateau from 31 May to 27 September 2011. The updated soil temperature at surface obtained by assimilating MODIS LST serving as inputs of RTM is to reduce the differences between the simulated and observed TB, then AMSR-E TB is assimilated to update soil moisture and model parameters. Compared with in situ measurements, the accuracy of soil moisture estimation derived from the assimilation experiment has been tremendously improved at a variety of scales. The updated parameters effectively reduce the states bias of CoLM. The results demonstrate the potential of assimilating AMSR-E TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study indicates that the developed scheme is an effective way to retrieve downscaled soil moisture when assimilating the coarse-scale microwave TB.

Assimilation of MODIS Ice Surface Temperature and Albedo into the Snow and Ice Model CROCUS Over the Greenland Ice Sheet Along the K-transect Stations

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Navari, M.; Margulis, S. A.; Bateni, S. M.; Alexander, P. M.; Tedesco, M.

2016-12-01

Estimating the Greenland Ice Sheet (GrIS) surface mass balance (SMB) is an important component of current and future projections of sea level rise. In situ measurement provides direct estimates of the SMB, but are inherently limited by their spatial extent and representativeness. Given this limitation, physically based regional climate models (RCMs) are critical for understanding GrIS physical processes and estimating of the GrIS SMB. However, the uncertainty in estimates of SMB from RCMs is still high. Surface remote sensing (RS) has been used as a complimentary tool to characterize various aspects related to the SMB. The difficulty of using these data streams is that the links between them and the SMB terms are most often indirect and implicit. Given the lack of in situ information, imperfect models, and under-utilized RS data it is critical to merge the available data in a systematic way to better characterize the spatial and temporal variation of the GrIS SMB. This work proposes a data assimilation (DA) framework that yields temporally-continuous and physically consistent SMB estimates that benefit from state-of-the-art models and relevant remote sensing data streams. Ice surface temperature (IST) is the most important factor that regulates partitioning of the net radiation into the subsurface snow/ice, sensible and latent heat fluxes and plays a key role in runoff generation. Therefore it can be expected that a better estimate of surface temperature from a data assimilation system would contribute to a better estimate of surface mass fluxes. Albedo plays an important role in the surface energy balance of the GrIS. However, even advanced albedo modules are not adequate to simulate albedo over the GrIS. Therefore, merging remotely sensed albedo product into a physically based model has a potential to improve the estimates of the GrIS SMB. In this work a MODIS-derived IST and a 16-day albedo product are independently assimilated into the snow and ice model CROCUS

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

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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.

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

Science.gov (United States)

Susskind, Joel; Rosenberg, Bob

2016-01-01

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

Estimation of the Ocean Skin Temperature using the NASA GEOS Atmospheric Data Assimilation System

Science.gov (United States)

Koster, Randal D.; Akella, Santha; Todling, Ricardo; Suarez, Max

2016-01-01

This report documents the status of the development of a sea surface temperature (SST) analysis for the Goddard Earth Observing System (GEOS) Version-5 atmospheric data assimilation system (ADAS). Its implementation is part of the steps being taken toward the development of an integrated earth system analysis. Currently, GEOS-ADAS SST is a bulk ocean temperature (from ocean boundary conditions), and is almost identical to the skin sea surface temperature. Here we describe changes to the atmosphere-ocean interface layer of the GEOS-atmospheric general circulation model (AGCM) to include near surface diurnal warming and cool-skin effects. We also added SST relevant Advanced Very High Resolution Radiometer (AVHRR) observations to the GEOS-ADAS observing system. We provide a detailed description of our analysis of these observations, along with the modifications to the interface between the GEOS atmospheric general circulation model, gridpoint statistical interpolation-based atmospheric analysis and the community radiative transfer model. Our experiments (with and without these changes) show improved assimilation of satellite radiance observations. We obtained a closer fit to withheld, in-situ buoys measuring near-surface SST. Evaluation of forecast skill scores corroborate improvements seen in the observation fits. Along with a discussion of our results, we also include directions for future work.

Hydrologic Remote Sensing and Land Surface Data Assimilation

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Hamid Moradkhani

2008-05-01

Full Text Available Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF and Particle filter (PF, for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law and could be a strong alternative to EnKF which is subject to some

Assimilation of Sea Surface Temperature in a doubly, two-way nested primitive equation model of the Ligurian Sea

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Barth, A.; Alvera-Azcarate, A.; Rixen, M.; Beckers, J.-M.; Testut, C.-E.; Brankart, J.-M.; Brasseur, P.

2003-04-01

The GHER 3D primitive equation model is implemented with three different resolutions: a low resolution model (1/4^o) covering the whole Mediterranean Sea, an intermediate resolution model (1/20^o) of the Liguro-Provençal basin and a high resolution model (1/60^o) simulating the fine mesoscale structures in the Ligurian Sea. Boundary conditions and the averaged fields (feedback) are exchanged between two successive nesting levels. The model of the Ligurian Sea is also coupled with the assimilation package SESAM. It allows to assimilate satellite data and in situ observations using the local adaptative SEEK (Singular Evolutive Extended Kalman) filter. Instead of evolving the error space by the numerically expensive Lyapunov equation, a simplified algebraic equation depending on the misfit between observation and model forecast is used. Starting from the 1st January 1998 the low and intermediate resolution models are spun up for 18 months. The initial conditions for the Ligurian Sea are interpolated from the intermediate resolution model. The three models are then integrated until August 1999. During this period AVHRR Sea Surface Temperature of the Ligurian Sea is assimilated. The results are validated by using CTD and XBT profiles of the SIRENA cruise from the SACLANT Center. The overall objective of this study is pre-operational. It should help to identify limitations and weaknesses of forecasting methods and to suggest improvements of existing operational models.

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

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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.

Preliminary investigation into the impacts of assimilating SST and SLA on the surface velocities in a HYCOM of the Agulhas Current

CSIR Research Space (South Africa)

Rapeti, T

2016-10-01

Full Text Available Data assimilative ocean models play crucial roles in furthering the understanding, and providing forecasts of the Agulhas Current system. This study investigates the impact that assimilating sea surface temperatures (SST) combined with sea level...

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

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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.

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

Science.gov (United States)

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

2015-01-01

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

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

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Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

2014-01-01

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

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

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C. L. Keppenne

2005-01-01

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

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

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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.

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

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

2003-01-01

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

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

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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.

Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

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Tao Wang

Full Text Available Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique improve the simulation accuracy of mean seasonal (October throughout May snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the

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

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

2011-06-01

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

Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data

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Lei Ren

2017-09-01

Full Text Available In order to improve the forecasting ability of numerical models, a sequential data assimilation scheme, nudging, was applied to blend remotely sensing high-frequency (HF radar surface currents with results from a three-dimensional numerical, EFDC (Environmental Fluid Dynamics Code model. For the first time, this research presents the most appropriate nudging parameters, which were determined from sensitivity experiments. To examine the influence of data assimilation cycle lengths on forecasts and to extend forecasting improvements, the duration of data assimilation cycles was studied through assimilating linearly interpolated temporal radar data. Data assimilation nudging parameters have not been previously analyzed. Assimilation of HF radar measurements at each model computational timestep outperformed those assimilation models using longer data assimilation cycle lengths; root-mean-square error (RMSE values of both surface velocity components during a 12 h model forecasting period indicated that surface flow fields were significantly improved when implementing nudging assimilation at each model computational timestep. The Data Assimilation Skill Score (DASS technique was used to quantitatively evaluate forecast improvements. The averaged values of DASS over the data assimilation domain were 26% and 33% for east–west and north–south velocity components, respectively, over the half-day forecasting period. Correlation of Averaged Kinetic Energy (AKE was improved by more than 10% in the best data assimilation model. Time series of velocity components and surface flow fields were presented to illustrate the improvement resulting from data assimilation application over time.

Improving Forecast Skill by Assimilation of Quality Controlled AIRS Version 5 Temperature Soundings

Science.gov (United States)

Susskind, Joel; Reale, Oreste

2009-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of cloud cleared radiances R(sub i). This approach allows for the generation of accurate values of R(sub i) and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for R(sub i). These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

Science.gov (United States)

Blakenship, Clay; Zavodsky, Bradley; Blackwell, William

2014-01-01

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. Forecasts are against ERA reanalyses.

Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

Science.gov (United States)

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

2011-12-01

This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

Impact of satellite-based lake surface observations on the initial state of HIRLAM. Part II: Analysis of lake surface temperature and ice cover

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Homa Kheyrollah Pour

2014-09-01

Full Text Available This paper presents results from a study on the impact of remote-sensing Lake Surface Water Temperature (LSWT observations in the analysis of lake surface state of a numerical weather prediction (NWP model. Data assimilation experiments were performed with the High Resolution Limited Area Model (HIRLAM, a three-dimensional operational NWP model. Selected thermal remote-sensing LSWT observations provided by the Moderate Resolution Imaging Spectroradiometer (MODIS and Advanced Along-Track Scanning Radiometer (AATSR sensors onboard the Terra/Aqua and ENVISAT satellites, respectively, were included into the assimilation. The domain of our experiments, which focussed on two winters (2010–2011 and 2011–2012, covered northern Europe. Validation of the resulting objective analyses against independent observations demonstrated that the description of the lake surface state can be improved by the introduction of space-borne LSWT observations, compared to the result of pure prognostic parameterisations or assimilation of the available limited number of in-situ lake temperature observations. Further development of the data assimilation methods and solving of several practical issues are necessary in order to fully benefit from the space-borne observations of lake surface state for the improvement of the operational weather forecast. This paper is the second part of a series of two papers aimed at improving the objective analysis of lake temperature and ice conditions in HIRLAM.

Snow multivariable data assimilation for hydrological predictions in mountain areas

Science.gov (United States)

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

2016-04-01

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

Results from Assimilating AMSR-E Soil Moisture Estimates into a Land Surface Model Using an Ensemble Kalman Filter in the Land Information System

Science.gov (United States)

Blankenship, Clay B.; Crosson, William L.; Case, Jonathan L.; Hale, Robert

2010-01-01

Improve simulations of soil moisture/temperature, and consequently boundary layer states and processes, by assimilating AMSR-E soil moisture estimates into a coupled land surface-mesoscale model Provide a new land surface model as an option in the Land Information System (LIS)

Impact of data assimilation on ocean current forecasts in the Angola Basin

Science.gov (United States)

Phillipson, Luke; Toumi, Ralf

2017-06-01

The ocean current predictability in the data limited Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with four-dimensional variational data assimilation. Six experiments were undertaken comprising a baseline case of the assimilation of salinity/temperature profiles and satellite sea surface temperature, with the subsequent addition of altimetry, OSCAR (satellite-derived sea surface currents), drifters, altimetry and drifters combined, and OSCAR and drifters combined. The addition of drifters significantly improves Lagrangian predictability in comparison to the baseline case as well as the addition of either altimetry or OSCAR. OSCAR assimilation only improves Lagrangian predictability as much as altimetry assimilation. On average the assimilation of either altimetry or OSCAR with drifter velocities does not significantly improve Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. When the forecast current speed is large, it is more likely that the combination improves trajectory forecasts. Conversely, when the currents are weaker, it is more likely that the combination degrades the trajectory forecast.

Downscaling Meteosat Land Surface Temperature over a Heterogeneous Landscape Using a Data Assimilation Approach

Directory of Open Access Journals (Sweden)

Rihab Mechri

2016-07-01

Full Text Available A wide range of environmental applications require the monitoring of land surface temperature (LST at frequent intervals and fine spatial resolutions, but these conditions are not offered nowadays by the available space sensors. To overcome these shortcomings, LST downscaling methods have been developed to derive higher resolution LST from the available satellite data. This research concerns the application of a data assimilation (DA downscaling approach, the genetic particle smoother (GPS, to disaggregate Meteosat 8 LST time series (3 km × 5 km at finer spatial resolutions. The methodology was applied over the Crau-Camargue region in Southeastern France for seven months in 2009. The evaluation of the downscaled LSTs has been performed at a moderate resolution using a set of coincident clear-sky MODIS LST images from Aqua and Terra platforms (1 km × 1 km and at a higher resolution using Landsat 7 data (60 m × 60 m. The performance of the downscaling has been assessed in terms of reduction of the biases and the root mean square errors (RMSE compared to prior model-simulated LSTs. The results showed that GPS allows downscaling the Meteosat LST product from 3 × 5 km2 to 1 × 1 km2 scales with a RMSE less than 2.7 K. Finer scale downscaling at Landsat 7 resolution showed larger errors (RMSE around 5 K explained by land cover errors and inter-calibration issues between sensors. Further methodology improvements are finally suggested.

Coupled assimilation for an intermediated coupled ENSO prediction model

Science.gov (United States)

Zheng, Fei; Zhu, Jiang

2010-10-01

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

A balanced Kalman filter ocean data assimilation system with application to the South Australian Sea

Science.gov (United States)

Li, Yi; Toumi, Ralf

2017-08-01

In this paper, an Ensemble Kalman Filter (EnKF) based regional ocean data assimilation system has been developed and applied to the South Australian Sea. This system consists of the data assimilation algorithm provided by the NCAR Data Assimilation Research Testbed (DART) and the Regional Ocean Modelling System (ROMS). We describe the first implementation of the physical balance operator (temperature-salinity, hydrostatic and geostrophic balance) to DART, to reduce the spurious waves which may be introduced during the data assimilation process. The effect of the balance operator is validated in both an idealised shallow water model and the ROMS model real case study. In the shallow water model, the geostrophic balance operator eliminates spurious ageostrophic waves and produces a better sea surface height (SSH) and velocity analysis and forecast. Its impact increases as the sea surface height and wind stress increase. In the real case, satellite-observed sea surface temperature (SST) and SSH are assimilated in the South Australian Sea with 50 ensembles using the Ensemble Adjustment Kalman Filter (EAKF). Assimilating SSH and SST enhances the estimation of SSH and SST in the entire domain, respectively. Assimilation with the balance operator produces a more realistic simulation of surface currents and subsurface temperature profile. The best improvement is obtained when only SSH is assimilated with the balance operator. A case study with a storm suggests that the benefit of the balance operator is of particular importance under high wind stress conditions. Implementing the balance operator could be a general benefit to ocean data assimilation systems.

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

Science.gov (United States)

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

2018-03-01

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

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

Science.gov (United States)

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

2016-12-01

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

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

Directory of Open Access Journals (Sweden)

D. R. Allen

2018-03-01

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

Variational data assimilative modeling of the Gulf of Maine in spring and summer 2010

Science.gov (United States)

Li, Yizhen; He, Ruoying; Chen, Ke; McGillicuddy, Dennis J.

2015-05-01

A data assimilative ocean circulation model is used to hindcast the Gulf of Maine [GOM) circulation in spring and summer 2010. Using the recently developed incremental strong constraint 4D Variational data assimilation algorithm, the model assimilates satellite sea surface temperature and in situ temperature and salinity profiles measured by expendable bathythermograph, Argo floats, and shipboard CTD casts. Validation against independent observations shows that the model skill is significantly improved after data assimilation. The data-assimilative model hindcast reproduces the temporal and spatial evolution of the ocean state, showing that a sea level depression southwest of the Scotian Shelf played a critical role in shaping the gulf-wide circulation. Heat budget analysis further demonstrates that both advection and surface heat flux contribute to temperature variability. The estimated time scale for coastal water to travel from the Scotian Shelf to the Jordan Basin is around 60 days, which is consistent with previous estimates based on in situ observations. Our study highlights the importance of resolving upstream and offshore forcing conditions in predicting the coastal circulation in the GOM.

Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

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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.

Variational Data Assimilative Modeling of the Gulf of Maine Circulation in Spring and Summer 2010

OpenAIRE

Li, Yizhen; He, Ruoying; Chen, Ke; McGillicuddy, Dennis J.

2015-01-01

A data assimilative ocean circulation model is used to hindcast the Gulf of Maine (GOM) circulation in spring and summer 2010. Using the recently developed incremental strong constraint 4D Variational data assimilation algorithm, the model assimilates satellite sea surface temperature and in situ temperature and salinity profiles measured by expendable bathythermograph, Argo floats, and shipboard CTD casts. Validation against independent observations shows that the model skill is significantl...

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

Science.gov (United States)

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

2016-01-01

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

An operational analysis of Lake Surface Water Temperature

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Emma K. Fiedler

2014-07-01

Full Text Available Operational analyses of Lake Surface Water Temperature (LSWT have many potential uses including improvement of numerical weather prediction (NWP models on regional scales. In November 2011, LSWT was included in the Met Office Operational Sea Surface Temperature and Ice Analysis (OSTIA product, for 248 lakes globally. The OSTIA analysis procedure, which has been optimised for oceans, has also been used for the lakes in this first version of the product. Infra-red satellite observations of lakes and in situ measurements are assimilated. The satellite observations are based on retrievals optimised for Sea Surface Temperature (SST which, although they may introduce inaccuracies into the LSWT data, are currently the only near-real-time information available. The LSWT analysis has a global root mean square difference of 1.31 K and a mean difference of 0.65 K (including a cool skin effect of 0.2 K compared to independent data from the ESA ARC-Lake project for a 3-month period (June to August 2009. It is demonstrated that the OSTIA LSWT is an improvement over the use of climatology to capture the day-to-day variation in global lake surface temperatures.

On the effectiveness of surface assimilation in probabilistic nowcasts of planetary boundary layer profiles

Science.gov (United States)

Rostkier-Edelstein, Dorita; Hacker, Joshua

2013-04-01

Surface observations comprise a wide, non-expensive and reliable source of information about the state of the near-surface planetary boundary layer (PBL). Operational data assimilation systems have encountered several difficulties in effectively assimilating them, among others due to their local-scale representativeness, the transient coupling between the surface and the atmosphere aloft and the balance constraints usually used. A long-term goal of this work is to find an efficient system for probabilistic PBL nowcasting that can be employed wherever surface observations are present. Earlier work showed that surface observations can be an important source of information with a single column model (SCM) and an ensemble filter (EF). Here we extend that work to quantify the probabilistic skill of ensemble SCM predictions with a model including added complexity. We adopt a factor separation analysis to quantify the contribution of surface assimilation relative to that of selected model components (parameterized radiation and externally imposed horizontal advection) to the probabilistic skill of the system, and of any beneficial or detrimental interactions between them. To assess the real utility of the flow-dependent covariances estimated with the EF and of the SCM of the PBL we compare the skill of the SCM/EF system to that of a reference one based on climatological covariances and a 30-min persistence model. It consists of a dressing technique, whereby a deterministic 3D mesoscale forecast (e.g. from WRF model) is adjusted and dressed with uncertainty using a seasonal sample of mesoscale forecasts and surface forecast errors. Results show that assimilation of surface observations can improve deterministic and probabilistic profile predictions more significantly than major model improvements. Flow-dependent covariances estimated with the SCM/EF show clear advantage over the use of climatological covariances when the flow is characterized by wide variability, when

Diverging temperature responses of CO2 assimilation and plant development explain the overall effect of temperature on biomass accumulation in wheat leaves and grains.

Science.gov (United States)

Collins, Nicholas C; Parent, Boris

2017-01-09

There is a growing consensus in the literature that rising temperatures influence the rate of biomass accumulation by shortening the development of plant organs and the whole plant and by altering rates of respiration and photosynthesis. A model describing the net effects of these processes on biomass would be useful, but would need to reconcile reported differences in the effects of night and day temperature on plant productivity. In this study, the working hypothesis was that the temperature responses of CO 2 assimilation and plant development rates were divergent, and that their net effects could explain observed differences in biomass accumulation. In wheat (Triticum aestivum) plants, we followed the temperature responses of photosynthesis, respiration and leaf elongation, and confirmed that their responses diverged. We measured the amount of carbon assimilated per "unit of plant development" in each scenario and compared it to the biomass that accumulated in growing leaves and grains. Our results suggested that, up to a temperature optimum, the rate of any developmental process increased with temperature more rapidly than that of CO 2 assimilation and that this discrepancy, summarised by the CO 2 assimilation rate per unit of plant development, could explain the observed reductions in biomass accumulation in plant organs under high temperatures. The model described the effects of night and day temperature equally well, and offers a simple framework for describing the effects of temperature on plant growth. Published by Oxford University Press on behalf of the Annals of Botany Company.

Effects of gamma radiation and temperature on the biological assimilation and retention of /sup 137/Cs by Acheta domesticus (L. )

Energy Technology Data Exchange (ETDEWEB)

Van Hook, Jr, R I; Herbert, E T

1971-12-01

Cesium-137 retention was determined for brown crickets, Acheta domesticus, which had been irradiated with 0, 1000, 2500 and 5000 rad gamma radiation and maintained at 20, 25 and 30 degrees C. Parameters examined for temperature and dose effects were (1) per cent /sup 137/Cs assimilated into body tissues (p2), (2) rate of isotope passage through the gut (k1) and (3) rate of elimination of assimilated /sup 137/Cs (ks). Increases in temperature and gamma dose resulted in a general decrease in per cent /sup 137/Cs assimilated pe day (p2). The first-component elimination coefficient (k1) was not significantly affected (P less than or equal to 0.05) by either temperature or dose changes. Biological elimination coefficients for assimilated /sup 137/Cs (k2) increased with increasing temperature between doses of 0 and 2500 rad. Above 2500 rads however, increases in temperature had no noticeable effects on the rate of assimilated /sup 137/Cs excretion. At higher dose levels, radiation was the dominant factor influencing the parameter k2.

Ensemble perturbation smoother for optimizing tidal boundary conditions by assimilation of High-Frequency radar surface currents – application to the German Bight

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A. Barth

2010-02-01

Full Text Available High-Frequency (HF radars measure the ocean surface currents at various spatial and temporal scales. These include tidal currents, wind-driven circulation, density-driven circulation and Stokes drift. Sequential assimilation methods updating the model state have been proven successful to correct the density-driven currents by assimilation of observations such as sea surface height, sea surface temperature and in-situ profiles. However, the situation is different for tides in coastal models since these are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. For improving the modeled tidal variability it is therefore not sufficient to update the model state via data assimilation without updating the boundary conditions. The optimization of boundary conditions to match observations inside the domain is traditionally achieved through variational assimilation methods. In this work we present an ensemble smoother to improve the tidal boundary values so that the model represents more closely the observed currents. To create an ensemble of dynamically realistic boundary conditions, a cost function is formulated which is directly related to the probability of each boundary condition perturbation. This cost function ensures that the boundary condition perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimensional General Estuarine Ocean Model (GETM. Optimized boundary values are obtained by assimilating all observations using the covariances of the ensemble simulation.

4DVAR data Assimilation with the Regional Ocean Modeling System (ROMS): Impact on the Water Mass Distributions in the Yellow Sea

Science.gov (United States)

Lee, Joon-Ho; Kim, Taekyun; Pang, Ig-Chan; Moon, Jae-Hong

2018-04-01

In this study, we evaluate the performance of the recently developed incremental strong constraint 4-dimensional variational (4DVAR) data assimilation applied to the Yellow Sea (YS) using the Regional Ocean Modeling System (ROMS). Two assimilation experiments are compared: assimilating remote-sensed sea surface temperature (SST) and both the SST and in-situ profiles measured by shipboard CTD casts into a regional ocean modeling from January to December of 2011. By comparing the two assimilation experiments against a free-run without data assimilation, we investigate how the assimilation affects the hydrographic structures in the YS. Results indicate that the SST assimilation notably improves the model behavior at the surface when compared to the nonassimilative free-run. The SST assimilation also has an impact on the subsurface water structure in the eastern YS; however, the improvement is seasonally dependent, that is, the correction becomes more effective in winter than in summer. This is due to a strong stratification in summer that prevents the assimilation of SST from affecting the subsurface temperature. A significant improvement to the subsurface temperature is made when the in-situ profiles of temperature and salinity are assimilated, forming a tongue-shaped YS bottom cold water from the YS toward the southwestern seas of Jeju Island.

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

Science.gov (United States)

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

2007-12-01

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

Estimating surface turbulent heat fluxes from land surface temperature and soil moisture using the particle batch smoother

Science.gov (United States)

Lu, Yang; Dong, Jianzhi; Steele-Dunne, Susan; van de Giesen, Nick

2016-04-01

This study is focused on estimating surface sensible and latent heat fluxes from land surface temperature (LST) time series and soil moisture observations. Surface turbulent heat fluxes interact with the overlying atmosphere and play a crucial role in meteorology, hydrology and other climate-related fields, but in-situ measurements are costly and difficult. It has been demonstrated that the time series of LST contains information of energy partitioning and that surface turbulent heat fluxes can be determined from assimilation of LST. These studies are mainly based on two assumptions: (1) a monthly value of bulk heat transfer coefficient under neutral conditions (CHN) which scales the sum of the fluxes, and (2) an evaporation fraction (EF) which stays constant during the near-peak hours of the day. Previous studies have applied variational and ensemble approaches to this problem. Here the newly developed particle batch smoother (PBS) algorithm is adopted to test its capability in this application. The PBS can be seen as an extension of the standard particle filter (PF) in which the states and parameters within a fix window are updated in a batch using all observations in the window. The aim of this study is two-fold. First, the PBS is used to assimilate only LST time series into the force-restore model to estimate fluxes. Second, a simple soil water transfer scheme is introduced to evaluate the benefit of assimilating soil moisture observations simultaneously. The experiments are implemented using the First ISLSCP (International Satellite Land Surface Climatology Project) (FIFE) data. It is shown that the restored LST time series using PBS agrees very well with observations, and that assimilating LST significantly improved the flux estimation at both daily and half-hourly time scales. When soil moisture is introduced to further constrain EF, the accuracy of estimated EF is greatly improved. Furthermore, the RMSEs of retrieved fluxes are effectively reduced at both

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

Science.gov (United States)

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

2012-12-01

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

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

Science.gov (United States)

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

2011-01-01

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

Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data

Science.gov (United States)

Abdolghafoorian, A.; Farhadi, L.

2017-12-01

Accurate estimation of land surface heat and moisture fluxes, as well as root zone soil moisture, is crucial in various hydrological, meteorological, and agricultural applications. Field measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state observations that are widely available from remote sensing across a range of scale. In this work, we applies the variational data assimilation approach to estimate land surface fluxes and soil moisture profile from the implicit information contained Land Surface Temperature (LST) and Soil Moisture (SM) (hereafter the VDA model). The VDA model is focused on the estimation of three key parameters: 1- neutral bulk heat transfer coefficient (CHN), 2- evaporative fraction from soil and canopy (EF), and 3- saturated hydraulic conductivity (Ksat). CHN and EF regulate the partitioning of available energy between sensible and latent heat fluxes. Ksat is one of the main parameters used in determining infiltration, runoff, groundwater recharge, and in simulating hydrological processes. In this study, a system of coupled parsimonious energy and water model will constrain the estimation of three unknown parameters in the VDA model. The profile of SM (LST) at multiple depths is estimated using moisture diffusion (heat diffusion) equation. In this study, the uncertainties of retrieved unknown parameters and fluxes are estimated from the inverse of Hesian matrix of cost function which is computed using the Lagrangian methodology. Analysis of uncertainty provides valuable information about the accuracy of estimated parameters and their correlation and guide the formulation of a well-posed estimation problem. The results of proposed algorithm are validated with a series of experiments using a synthetic data set generated by the simultaneous heat and

A virtual reality catchment for data assimilation experiments

Science.gov (United States)

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

2016-04-01

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

Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study

Directory of Open Access Journals (Sweden)

A. L. Barbu

2011-07-01

Full Text Available The performance of the joint assimilation in a land surface model of a Soil Wetness Index (SWI product provided by an exponential filter together with Leaf Area Index (LAI is investigated. The data assimilation is evaluated with different setups using the SURFEX modeling platform, for a period of seven years (2001–2007, at the SMOSREX grassland site in southwestern France. The results obtained with a Simplified Extended Kalman Filter demonstrate the effectiveness of a joint data assimilation scheme when both SWI and Leaf Area Index are merged into the ISBA-A-gs land surface model. The assimilation of a retrieved Soil Wetness Index product presents several challenges that are investigated in this study. A significant improvement of around 13 % of the root-zone soil water content is obtained by assimilating dimensionless root-zone SWI data. For comparison, the assimilation of in situ surface soil moisture is considered as well. A lower impact on the root zone is noticed. Under specific conditions, the transfer of the information from the surface to the root zone was found not accurate. Also, our results indicate that the assimilation of in situ LAI data may correct a number of deficiencies in the model, such as low LAI values in the senescence phase by using a seasonal-dependent error definition for background and observations. In order to verify the specification of the errors for SWI and LAI products, a posteriori diagnostics are employed. This approach highlights the importance of the assimilation design on the quality of the analysis. The impact of data assimilation scheme on CO₂ fluxes is also quantified by using measurements of net CO₂ fluxes gathered at the SMOSREX site from 2005 to 2007. An improvement of about 5 % in terms of rms error is obtained.

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

Directory of Open Access Journals (Sweden)

Jana Kolassa

2017-11-01

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

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

Science.gov (United States)

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

2018-01-01

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

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

Science.gov (United States)

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

2018-01-01

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

Water flux characterization through hydraulic head and temperature data assimilation: Numerical modeling and sandbox experiments

Science.gov (United States)

Ju, Lei; Zhang, Jiangjiang; Chen, Cheng; Wu, Laosheng; Zeng, Lingzao

2018-03-01

Spatial distribution of groundwater recharge/discharge fluxes has an important impact on mass and energy exchanges in shallow streambeds. During the last two decades, extensive studies have been devoted to the quantification of one-dimensional (1-D) vertical exchange fluxes. Nevertheless, few studies were conducted to characterize two-dimensional (2-D) heterogeneous flux fields that commonly exist in real-world cases. In this study, we used an iterative ensemble smoother (IES) to quantify the spatial distribution of 2-D exchange fluxes by assimilating hydraulic head and temperature measurements. Four assimilation scenarios corresponding to different potential field applications were tested. In the first three scenarios, the heterogeneous hydraulic conductivity fields were first inferred from hydraulic head and/or temperature measurements, and then the flux fields were derived through Darcy's law using the estimated conductivity fields. In the fourth scenario, the flux fields were estimated directly from the temperature measurements, which is more efficient and especially suitable for the situation that a complete knowledge of flow boundary conditions is unavailable. We concluded that, the best estimation could be achieved through jointly assimilating hydraulic head and temperature measurements, and temperature data were superior to the head data when they were used independently. Overall, the IES method provided more robust and accurate vertical flux estimations than those given by the widely used analytical solution-based methods. Furthermore, IES gave reasonable uncertainty estimations, which were unavailable in traditional methods. Since temperature can be accurately monitored with high spatial and temporal resolutions, the coupling of heat tracing techniques and IES provides promising potential in quantifying complex exchange fluxes under field conditions.

Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO2 surface flux inversions

NARCIS (Netherlands)

Babenhauserheide, A.; Basu, S.; Peters, W.

2015-01-01

Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on assimilation of more than one

Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO2 surface flux inversions

NARCIS (Netherlands)

Babenhauserheide, A.; Basu, S.; Houweling, S.; Peters, W.; Butz, A.

2015-01-01

Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on the assimilation of more than

Steady-state room temperature fluorescence and CO/sub 2/ assimilation rates in intact leaves. [Phaseolus vulgaris; Xanthium strumarium

Energy Technology Data Exchange (ETDEWEB)

Sharkey, T D

1985-01-01

Steady-state room temperature variable fluorescence from leaves was measured as a function of CO/sub 2/ pressure in Xanthium strumarium L. and Phaseolus vulgaris L. Measurements were made in a range of light intensities, at normal and low O/sub 2/ partial pressure and over a range of temperatures. At low CO/sub 2/ pressure fluorescence increased with increasing CO/sub 2/. At higher CO/sub 2/ pressure fluorescence usually decreased with increasing CO/sub 2/ but occasionally increased slightly. The transition CO/sub 2/ pressure between the responses could be changed by changing light, O/sub 2/ pressure, or temperature. This breakpoint in the fluorescence-CO/sub 2/ curve was a reliable indicator of the transition between ribulose 1,5-bisphosphate (RuBP) saturated assimilation and RuBP regeneration limited assimilation. The fluorescence signal was not a reliable indicator of O/sub 2/-insensitive assimilation in these C/sub 3/ species. 21 references, 8 figures.

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

Science.gov (United States)

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

2015-01-01

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

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

Science.gov (United States)

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

2018-01-01

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

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Science.gov (United States)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

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

Science.gov (United States)

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

1995-01-01

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

Data Assimilation in Hydrodynamic Models of Continental Shelf Seas

DEFF Research Database (Denmark)

Sørensen, Jacob Viborg Tornfeldt

2004-01-01

. Assimilation of sea surface temperature and parameter estimation in hydrodynamic models are also considered. The main focus has been on the development of robust and efficient techniques applicable in real operational settings. The applied assimilation techniques all use a Kalman filter approach. They consist....... The assimilation schemes used in this work are primarily based on two ensemble based schemes, the Ensemble Kalman Filter and the Reduced Rank Square Root Kalman Filter. In order to investigate the applicability of these and derived schemes, the sensitivity to filter parameters, nonlinearity and bias is examined...... in artificial tests. Approximate schemes, which are theoretically presented as using regularised Kalman gains, are introduced and successfully applied in artificial as well real case scenarios. Particularly, distant dependent and slowly time varying or constant Kalman gains are shown to possess good hindcast...

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

Directory of Open Access Journals (Sweden)

Yongjing Zhao

2015-05-01

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

Statistical techniques to extract information during SMAP soil moisture assimilation

Science.gov (United States)

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

2017-12-01

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

Effects of temperature and light intensity on the uptake and assimilation of 15N-labeled ammonium and nitrate in Indica and Japonica rice plants

International Nuclear Information System (INIS)

Ta, T.C.; Ohira, Koji

1982-01-01

The effects of various environmental condition such as temperature and light intensity on the uptake and assimilation of ammonium and nitrate in 4-week-old Indica and Japonica rice plants were studied using the 15 N tracer technique. Both temperature and light intensity profoundly affected the uptake and assimilation of ammonium and nitrate, and the effects were more apparent in the utilization of nitrate by both Indica and Japonica rice plants. The uptake as well as assimilation of the two forms of nitrogen were greatly inhibited at low temperature and low light intensity. On the other hand, although no significant difference in the effects of environmental conditions on the utilization of ammonium was observed between the Indica and Japonica rice plants, the former were more sensitive than the latter in the utilization of nitrate. At high temperature and high light intensity, the Indica rice plants absorbed, reduced, and assimilated nitrate more effectively than the Japonica, and this effect disappeared when the temperature or light intensity was reduced. (author)

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

Science.gov (United States)

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

2017-12-01

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

Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site

Directory of Open Access Journals (Sweden)

Lei Ren

2017-12-01

Full Text Available A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR data into models of complex inshore waters strongly influenced by both tides and wind dynamics, such as Galway Bay. This research entailed implementing four different data assimilation algorithms: Direct Insertion (DI, Optimal Interpolation (OI, Nudging and indirect data assimilation via correcting model forcing into a three-dimensional hydrodynamic model and carrying out detailed comparisons of model performances. This work will allow researchers to directly compare four of the most common data assimilation algorithms being used in operational coastal hydrodynamics. The suitability of practical data assimilation algorithms for hindcasting and forecasting in shallow coastal waters subjected to alternate wetting and drying using data collected from radars was assessed. Results indicated that a forecasting system of surface currents based on the three-dimensional model EFDC (Environmental Fluid Dynamics Code and the HFR data using a Nudging or DI algorithm was considered the most appropriate for Galway Bay. The largest averaged Data Assimilation Skill Score (DASS over the ≥6 h forecasting period from the best model NDA attained 26% and 31% for east–west and north–south surface velocity components respectively. Because of its ease of implementation and its accuracy, this data assimilation system can provide timely and useful information for various practical coastal hindcast and forecast operations.

Development of airborne remote sensing data assimilation system

International Nuclear Information System (INIS)

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

2014-01-01

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

Do Assimilated Drifter Velocities Improve Lagrangian Predictability in an Operational Ocean Model?

Science.gov (United States)

2015-05-01

extended Kalman filter . Molcard et al. (2005) used a statistical method to cor- relate model and drifter velocities. Taillandier et al. (2006) describe the... temperature and salinity observations. Trajectory angular differ- ences are also reduced. 1. Introduction The importance of Lagrangian forecasts was seen... Temperature , salinity, and sea surface height (SSH, measured along-track by satellite altimeters) observa- tions are typically assimilated in

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Directory of Open Access Journals (Sweden)

P. Sakov

2012-08-01

Full Text Available We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.

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

Science.gov (United States)

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

Assimilation of Feng-Yun-3B satellite microwave humidity sounder data over land

Science.gov (United States)

Chen, Keyi; Bormann, Niels; English, Stephen; Zhu, Jiang

2018-03-01

The ECMWF has been assimilating Feng-Yun-3B (FY-3B) satellite microwave humidity sounder (MWHS) data over ocean in an operational forecasting system since 24 September 2014. It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel 1 [150 GHz (vertical polarization)] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS (Advanced Technology Microwave Sounder) humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.

Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China

Science.gov (United States)

Pang, Jiongming; Liu, Zhiquan; Wang, Xuemei; Bresch, Jamie; Ban, Junmei; Chen, Dan; Kim, Jhoon

2018-04-01

In this study, Geostationary Ocean Color Imager (GOCI) AOD and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data were assimilated to forecast surface PM2.5 concentrations over Eastern China, by using the three-dimensional variational (3DAVR) data assimilation (DA) system, to compare DA impacts by assimilating AOD retrievals from these two types of satellites. Three experiments were conducted, including a CONTROL without the AOD assimilation, and GOCIDA and VIIRSDA with the assimilation of AOD retrievals from GOCI and VIIRS, respectively. By utilizing the Weather Research and Forecasting with Chemistry (WRF/Chem) model, 48-h forecasts were initialized at each 06 UTC from 19 November to 06 December 2013. These forecasts were evaluated with 248 ground-based measurements from the air quality monitoring network across 67 China cities. The results show that overall the CONTROL underestimated surface PM2.5 concentrations, especially over Jing-Jin-Ji (JJJ) region and Yangtze River Delta (YRD) region. Both the GOCIDA and VIIRSDA produced higher surface PM2.5 concentrations mainly over Eastern China, which fits well with the PM2.5 measurements at these eastern sites, with more than 8% error reductions (ER). Moreover, compared to CONTROL, GOCIDA reduced 14.0% and 6.4% error on JJJ region and YRD region, respectively, while VIIRSDA reduced respectively 2.0% and 13.4% error over the corresponding areas. During the heavy polluted period, VIIRSDA improved all sites within YRD region, and GOCIDA enhanced 84% sites. Meanwhile, GOCIDA improved 84% sites on JJJ region, while VIIRSDA did not affect that region. These geographic distinctions might result from spatial dissimilarity between GOCI AOD and VIIRS AOD at time intervals. Moreover, the larger increment produced by AOD DA under stable meteorological conditions could lead to a longer duration (e.g., 1-2 days, > 2 days) of AOD DA impacts. Even though with AOD DA, surface PM2.5 concentrations were still underestimated

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

Science.gov (United States)

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

2011-01-01

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

Assimilation scheme of the Mediterranean Forecasting System: operational implementation

Directory of Open Access Journals (Sweden)

E. Demirov

Full Text Available This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP. The assimilation scheme, System for Ocean Forecast and Analysis (SOFA, is a reduced order Optimal Interpolation (OI scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF. The data assimilated are Sea Level Anomaly (SLA and temperature profiles from Expandable Bathy Termographs (XBT. The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT. The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS between the model forecast and the analysis (the forecast RMS is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements.

Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction

Assimilation scheme of the Mediterranean Forecasting System: operational implementation

Directory of Open Access Journals (Sweden)

E. Demirov

2003-01-01

Full Text Available This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP. The assimilation scheme, System for Ocean Forecast and Analysis (SOFA, is a reduced order Optimal Interpolation (OI scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF. The data assimilated are Sea Level Anomaly (SLA and temperature profiles from Expandable Bathy Termographs (XBT. The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT. The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS between the model forecast and the analysis (the forecast RMS is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements. Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction

Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

Directory of Open Access Journals (Sweden)

S. Naha

2016-06-01

Full Text Available The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006. Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I and Ensemble Kalman Filter (EnKF that uses observations of snow covered area (SCA to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU, rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated

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

Science.gov (United States)

2016-09-26

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

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

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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).

Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China

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Zhu, X.; Wen, X.; Zheng, Z.

2017-12-01

For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global LandData Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We found that the satellite-derived GVF from MODIS increased over southeast China compared with the default model over the whole year. The simulated results of soil temperature, net radiation and surface energy flux from the HRADC are improved compared with the control simulation and are close to GLDAS outputs. The values of net radiation from HRADC are higher than the GLDAS outputs, and the differences in the simulations are large in the east region but are smaller in northwest China and on the Qinghai-Tibet Plateau. The spatial distribution of the sensible heat flux and the ground

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

Science.gov (United States)

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

2018-05-01

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

Satellite Data Assimilation within KIAPS-LETKF system

Science.gov (United States)

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

2016-12-01

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

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

Science.gov (United States)

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

2009-09-01

The COSMO model has been developed by the COnsortium for Small-scale MOdelling, an over-national consortium coordinating the cooperation of the national and regional weather services of Germany, Italy, Switzerland, Greece, Poland and Romania. Its operational version does not make use of the 2m temperature, since it has been shown to have potentially adverse effects on the stability of the planetary boundary layer. Moreover, in pre-operational tests, it has been showed to degrade the low-tropospheric thermal structure of the model. The 2m temperature is at the moment only used in the soil moisture analysis, where it has the potential to modify the surface fluxes and to improve the prediction of 2m temperature during the forecast time. Despite these facts, there is an option in the model for the inclusion of 2m temperature in the assimilation cycle. For this reason, considering the great number of non-GTS stations in the ARPA Piemonte ground network, it has been decided to try the assimilation of 2m temperature in the COSMO-I2 version of the model, which has a horizontal resolution of about 3 km more similar to the average resolution of the thermometers. Two different test periods have been considered, from 1 to 15 September 2008 (summer-like weather) and from 3 to 17 January 2009 (winter-like weather). Every day we have run two simulations up to +24h, starting at 00UTC and 12UTC in order to investigate also the dependence on the initial state of the PBL. The aim of the work is to investigate the assimilation of the non-GTS data in the first 12h of the simulations in order to create an operational very high-resolution analysis, but also to test the option of running in the future a very short-range forecast (+12h to +18h) starting from these analyses. The results, in terms of RMSE, Mean Error (ME) and diurnal cycle of some surface variables such as 2m temperature, 2m relative humidity and 10m wind intensity, and in terms of vertical profile of temperature, show in

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

KAUST Repository

Toye, Habib

2017-05-26

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

Evaluation of a Soil Moisture Data Assimilation System Over West Africa

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

2009-05-01

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

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

Science.gov (United States)

Kim, Jong; Akella, Santha; da Silva, Arlindo M.; Todling, Ricardo; McCarty, William

2018-01-01

This document reports on preliminary results obtained when studying the impact of aerosols on the calculation of brightness temperature (BT) for satellite infrared (IR) instruments that are currently assimilated in a 3DVAR configuration of Goddard Earth Observing System (GEOS)-atmospheric data assimilation system (ADAS). A set of fifteen aerosol species simulated by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model is used to evaluate the influence of the aerosol fields on the Community Radiative Transfer Model (CRTM) calculations taking place in the observation operators of the Gridpoint Statistical Interpolation (GSI) analysis system of GEOSADAS. Results indicate that taking aerosols into account in the BT calculation improves the fit to observations over regions with significant amounts of dust. The cooling effect obtained with the aerosol-affected BT leads to a slight warming of the analyzed surface temperature (by about 0:5oK) in the tropical Atlantic ocean (off northwest Africa), whereas the effect on the air temperature aloft is negligible. In addition, this study identifies a few technical issues to be addressed in future work if aerosol-affected BT are to be implemented in reanalysis and operational settings. The computational cost of applying CRTM aerosol absorption and scattering options is too high to justify their use, given the size of the benefits obtained. Furthermore, the differentiation between clouds and aerosols in GSI cloud detection procedures needs satisfactory revision.

Benefits and Pitfalls of GRACE Terrestrial Water Storage Data Assimilation

Science.gov (United States)

Girotto, Manuela

2018-01-01

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

Screen-level non-GTS data assimilation in a limited-area mesoscale model

Directory of Open Access Journals (Sweden)

M. Milelli

2010-06-01

Full Text Available The forecast in areas of very complex topography, as for instance the Alpine region, is still a challenge even for the new generation of numerical weather prediction models which aim at reaching the km-scale. The problem is enhanced by a general lack of standard observations, which is even more evident over the southern side of the Alps. For this reason, it would be useful to increase the performance of the mathematical models by locally assimilating non-conventional data. Since in ARPA Piemonte there is the availability of a great number of non-GTS stations, it has been decided to assimilate the 2 m temperature, coming from this dataset, in the very-high resolution version of the COSMO model, which has a horizontal resolution of about 3 km, more similar to the average resolution of the thermometers. Four different weather situations have been considered, ranging from spring to winter, from cloudy to clear sky. The aim of the work is to investigate the effects of the assimilation of non-GTS data in order to create an operational very high-resolution analysis, but also to test the option of running in the future a very short-range forecast starting from these analyses (RUC or Rapid Update Cycle. The results, in terms of Root Mean Square Error, Mean Error and diurnal cycle of some surface variables such as 2 m temperature, 2 m relative humidity and 10 m wind intensity show a positive impact during the assimilation cycle which tends to dissipate a few hours after the end of it. Moreover, the 2 m temperature assimilation has a slightly positive or neutral impact on the vertical profiles of temperature, eventhough some calibration is needed for the precipitation field which is too much perturbed during the assimilation cycle, while it is unaffected in the forecast period. So the stability of the planetary boundary layer, on the one hand, has not been particularly improved by the new-data assimilation, but, on the other hand, it has not been destroyed

Ecohydrological drought monitoring and prediction using a land data assimilation system

Science.gov (United States)

Sawada, Y.; Koike, T.

2017-12-01

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

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

Science.gov (United States)

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

2015-01-01

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

ENVISAT Land Surface Processes. Phase 2

Science.gov (United States)

vandenHurk, B. J. J. M.; Su, Z.; Verhoef, W.; Menenti, M.; Li, Z.-L.; Wan, Z.; Moene, A. F.; Roerink, G.; Jia, I.

2002-01-01

This is a progress report of the 2nd phase of the project ENVISAT- Land Surface Processes, which has a 3-year scope. In this project, preparative research is carried out aiming at the retrieval of land surface characteristics from the ENVISAT sensors MERIS and AATSR, for assimilation into a system for Numerical Weather Prediction (NWP). Where in the 1st phase a number of first shot experiments were carried out (aiming at gaining experience with the retrievals and data assimilation procedures), the current 2nd phase has put more emphasis on the assessment and improvement of the quality of the retrieved products. The forthcoming phase will be devoted mainly to the data assimilation experiments and the assessment of the added value of the future ENVISAT products for NWP forecast skill. Referring to the retrieval of albedo, leaf area index and atmospheric corrections, preliminary radiative transfer calculations have been carried out that should enable the retrieval of these parameters once AATSR and MERIS data become available. However, much of this work is still to be carried out. An essential part of work in this area is the design and implementation of software that enables an efficient use of MODTRAN(sub 4) radiative transfer code, and during the current project phase familiarization with these new components has been achieved. Significant progress has been made with the retrieval of component temperatures from directional ATSR-images, and the calculation of surface turbulent heat fluxes from these data. The impact of vegetation cover on the retrieved component temperatures appears manageable, and preliminary comparison of foliage temperature to air temperatures were encouraging. The calculation of surface fluxes using the SEBI concept,which includes a detailed model of the surface roughness ratio, appeared to give results that were in reasonable agreement with local measurements with scintillometer devices. The specification of the atmospheric boundary conditions

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

Science.gov (United States)

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

2018-01-01

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

Collaborative Research: Atlantic Ocean Tropical/Subtropical Processes from Seasonal to Decadal Time Scales: Model/Data, Model/Model Comparison and Model/Data Synthesis Through Assimilation

Science.gov (United States)

Malannotte-Rizzoli, Paola

2003-01-01

The effort of this first year of research has been focused on the assimilation of TOPEX/Poseidon altimetric data into a primitive equation model of the Atlantic tropical/subtropical circulation. A reduced-rank, stationary Kalman filter has been constructed to assimilate the altimetric sea surface height anomaly (SHA) into the model. The goal is to assess how the inter-hemispheric transports between the Atlantic subtropics and tropics are affected by the assimilation and how the subsurface thermocline structure , and its variability ,is dynamically constrained by the SHA. The model is a reduced-gravity primitive equation GCM of the upper Atlantic Ocean between 30 S and 30 N. The assimilation scheme is an approximation to the extended Kalman filter in which the error covariances of the state estimates are calculated only in a reduced- dimension subspace. The subspace is defined by the leading empirical orthogonal functions calculated from an unconstrained model calculation. Both an identical twin experiment using simulated SHA observations and assimilation of the real TOPEX data were performed. Results from the twin experiments demonstrate the ability of the method to constrain the ocean circulation and the subsurface temperature structure. The impact on the subsurface temperature structure of TOPEX assimilation was assessed using data from expandable bathythermographs. This showed a substantial improvement in the estimated temperature variability only within 13 degrees in latitude around the equator. The impact of TOPEX SHA assimilation on zonally integrated meridional transport across different latitudes was also estimated. Again within 13 degrees from the equator both the mean amplitude and interannual variability of the surface and subsurface transports were significantly enhanced, while the transports were insensitive to the assimilation in the subtropics.

Impact of the assimilation of satellite soil moisture and LST on the hydrological cycle

Science.gov (United States)

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

2014-05-01

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 ground based data. The aim of this work is to investigate the impacts on the performances of a distributed hydrological model (Continuum) of the assimilation of satellite-derived soil moisture products and Land Surface (LST). In this work three different soil moisture (SM) products, derived by ASCAT sensor, are used. These data are provided by the EUMETSAT's H-SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) program. The considered soil moisture products are: large scale surface soil moisture (SM OBS 1 - H07), small scale surface soil moisture (SM OBS 2 - H08) and profile index in the roots region (SM DAS 2 - H14). These data are compared with soil moisture estimated by Continuum model on the Orba catchment (800 km2), in the northern part of Italy, for the period July 2012-June 2013. Different assimilation experiments have been performed. The first experiment consists in the assimilation of the SM products by using a simple Nudging technique; the second one is the assimilation of only LST data, derived from MSG satellite, and the third is the assimilation of both SM products and LST. The benefits on the model predictions of discharge, LST and soil moisture dynamics were tested.

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

KAUST Repository

Viswanadhapalli, Yesubabu

2013-12-08

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

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

NARCIS (Netherlands)

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

2016-01-01

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

Assimilation efficiencies of Cd and Zn in the common carp (Cyprinus carpio): Effects of metal concentration, temperature and prey type

Energy Technology Data Exchange (ETDEWEB)

Campenhout, K. van [Ecophysiology, Biochemistry and Toxicology Group, Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium); Bervoets, L. [Ecophysiology, Biochemistry and Toxicology Group, Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium)]. E-mail: lieven.bervoets@ua.ac.be; Blust, R. [Ecophysiology, Biochemistry and Toxicology Group, Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium)

2007-02-15

The impact of several factors on the assimilation efficiency (AE) of Cd and Zn from food in the common carp (Cyprinus carpio) was studied. Tested prey species were midge larvae (Chironomus riparius), zebra mussels (Dreissena polymorpha) and oligochaetes (Tubifex tubifex). The Cd load of the larvae did not affect the Cd AE in the carp. The Zn AE however, was negatively related to the Zn load of the prey. Food quantity and starvation of the carp did not significantly affect the Cd AE. For Zn, a significant decrease in AE was found when carp were fed ad libitum. Decreasing the temperature from 25 {sup o}C to 15 {sup o}C did not influence the Cd AE, while for Zn a significant decrease of the AE was measured. Carp assimilated Cd from both zebra mussels and oligochaetes with a significantly lower efficiency in comparison to the midge larvae, although Zn AEs was prey independent. - Assimilation efficiency of Cd and Zn in food of carp is affected by metal load, prey type and temperature.

Assimilation efficiencies of Cd and Zn in the common carp (Cyprinus carpio): Effects of metal concentration, temperature and prey type

International Nuclear Information System (INIS)

Campenhout, K. van; Bervoets, L.; Blust, R.

2007-01-01

The impact of several factors on the assimilation efficiency (AE) of Cd and Zn from food in the common carp (Cyprinus carpio) was studied. Tested prey species were midge larvae (Chironomus riparius), zebra mussels (Dreissena polymorpha) and oligochaetes (Tubifex tubifex). The Cd load of the larvae did not affect the Cd AE in the carp. The Zn AE however, was negatively related to the Zn load of the prey. Food quantity and starvation of the carp did not significantly affect the Cd AE. For Zn, a significant decrease in AE was found when carp were fed ad libitum. Decreasing the temperature from 25 o C to 15 o C did not influence the Cd AE, while for Zn a significant decrease of the AE was measured. Carp assimilated Cd from both zebra mussels and oligochaetes with a significantly lower efficiency in comparison to the midge larvae, although Zn AEs was prey independent. - Assimilation efficiency of Cd and Zn in food of carp is affected by metal load, prey type and temperature

Adjoint assimilation of altimetric, surface drifter, and hydrographic data in a quasi-geostrophic model of the Azores Current

Science.gov (United States)

Morrow, Rosemary; de Mey, Pierre

1995-12-01

The flow characteristics in the region of the Azores Current are investigated by assimilating TOPEX/POSEIDON and ERS 1 altimeter data into the multilevel Harvard quasigeostrophic (QG) model with open boundaries (Miller et al., 1983) using an adjoint variational scheme (Moore, 1991). The study site lies in the path of the Azores Current, where a branch retroflects to the south in the vicinity of the Madeira Rise. The region was the site of an intensive field program in 1993, SEMAPHORE. We had two main aims in this adjoint assimilation project. The first was to see whether the adjoint method could be applied locally to optimize an initial guess field, derived from the continous assimilation of altimetry data using optimal interpolation (OI). The second aim was to assimilate a variety of different data sets and evaluate their importance in constraining our QG model. The adjoint assimilation of surface data was effective in optimizing the initial conditions from OI. After 20 iterations the cost function was generally reduced by 50-80%, depending on the chosen data constraints. The primary adjustment process was via the barotropic mode. Altimetry proved to be a good constraint on the variable flow field, in particular, for constraining the barotropic field. The excellent data quality of the TOPEX/POSEIDON (T/P) altimeter data provided smooth and reliable forcing; but for our mesoscale study in a region of long decorrelation times O(30 days), the spatial coverage from the combined T/P and ERS 1 data sets was more important for constraining the solution and providing stable flow at all levels. Surface drifters provided an excellent constraint on both the barotropic and baroclinic model fields. More importantly, the drifters provided a reliable measure of the mean field. Hydrographic data were also applied as a constraint; in general, hydrography provided a weak but effective constraint on the vertical Rossby modes in the model. Finally, forecasts run over a 2-month period

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

Science.gov (United States)

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

2016-04-01

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

A simple lightning assimilation technique for improving ...

Science.gov (United States)

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

Atmospheric boundary layer response to sea surface temperatures during the SEMAPHORE experiment

Science.gov (United States)

Giordani, Hervé; Planton, Serge; Benech, Bruno; Kwon, Byung-Hyuk

1998-10-01

The sensitivity of the marine atmospheric boundary layer (MABL) subjected to sea surface temperatures (SST) during the Structure des Echanges Mer-Atmosphere, Proprietes des Heterogeneites Oceaniques: Recherche Experimentale (SEMAPHORE) experiment in 1993 has been studied. Atmospheric analyses produced by the Action de Recherche, Petite Echelle, Grande Echelle (ARPEGE) operational model at the French meteorological weather service assimilated data sets collected between October 7 and November 17, 1993, merged with the Global Telecommunication System (GTS) data. Analyses were validated against independent data from aircraft instruments collected along a section crossing the Azores oceanic front, not assimilated into the model. The responses of the mean MABL in the aircraft cross section to changes in SST gradients of about 1°C/100 km were the presence of an atmospheric front with horizontal gradients of 1°C/100 km and an increase of the wind intensity from the cold to the warm side during an anticyclonic synoptic situation. The study of the spatiotemporal characteristics of the MABL shows that during 3 days of an anticyclonic synoptic situation the SST is remarkably stationary because it is principally controlled by the Azores ocean current, which has a timescale of about 10 days. However, the temperature and the wind in the MABL are influenced by the prevailing atmospheric conditions. The ocean does not appear to react to the surface atmospheric forcing on the timescale of 3 days, whereas the atmospheric structures are modified by local and synoptic-scale advection. The MABL response appears to be much quicker than that of the SSTs. The correlation between the wind and the thermal structure in the MABL is dominated by the ageostrophic and not by the geostrophic component. In particular, the enhancement of the wind on either side of the SST front is mainly due to the ageostrophic component. Although the surface heat fluxes are not the only cause of ageostrophy, the

Development of KIAPS Observation Processing Package for Data Assimilation System

Science.gov (United States)

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

2015-04-01

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

Assimilation of Leaf Area Index and Soil Wetness Index into the ISBA-A-gs land surface model over France

Science.gov (United States)

Barbu, A. L.; Calvet, J.-C.; Lafont, S.

2012-04-01

The development of a Land Data Assimilation System (LDAS) dedicated to carbon and water cycles is considered as a key aspect for monitoring activities of terrestrial carbon fluxes. It allows the assimilation of biophysical products in order to reduce the bias between the model simulations and the observations and have a positive impact on carbon and water fluxes. This work shows the benefits of data assimilation of Earth observations for the monitoring of vegetation status and carbon fluxes, in the framework of the GEOLAND2 project, co-funded by the European Commission within the GMES initiative in FP7. In this study, the SURFEX modelling platform developed at Meteo-France is used for describing the continental vegetation state, surface fluxes and soil moisture. It consists of the land surface model ISBA-A-gs that simulates photosynthesis and plant growth. The vegetation biomass and Leaf Area Index (LAI) evolve dynamically in response to weather and climate conditions. The ECOCLIMAP database provides detailed information about the land cover at a resolution of 1 km. Over the France domain, the most present ecosystem types are grasslands (32%), C3 crop lands (24%), deciduous forest (20%), bare soil (11%), and C4 crop lands (8%).The model also includes a representation of the soil moisture stress with two different types of drought responses for herbaceous vegetation and forests. A version of the Extended Kalman Filter (EKF) scheme is developed for the joint assimilation of satellite-derived surface soil moisture from ASCAT-25 km product, namely Soil Wetness Index (SWI-01) developed by TU-Wien, and remote sensing LAI product provided by GEOLAND2. The GEOLAND2 LAI product is derived from CYCLOPES V3.1 and MODIS collection 5 data. It is more consistent with an effective LAI for low LAI and close to the actual LAI for high values. The assimilation experiment was conducted across France at a spatial resolution of 8 km. The study period ranges from July 2007 to December

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

Directory of Open Access Journals (Sweden)

Thomas Milewski

2013-02-01

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

ELEVATED TEMPERATURE, SOIL MOISTURE AND SEASONALITY BUT NOT CO2 AFFECT CANOPY ASSIMILATION AND SYSTEM RESPIRATION IN SEEDLING DOUGLAS-FIR ECOSYSTEMS

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We investigated the effects of elevated atmospheric CO2 and air temperature on C cycling in trees and associated soil system, focusing on canopy CO2 assimilation (Asys) and system CO2 loss through respiration (Rsys). We hypothesized that both elevated CO2 and elevated temperature...

Variational data assimilation problem for the thermodynamics model with displaced pole

Science.gov (United States)

Parmuzin, Eugene; Agosgkov, Valery; Zakharova, Natalia

2017-04-01

The most versatile and promising technology for solving problems of monitoring and analysis of the natural environment is a four-dimensional variational data assimilation of observation data. The development of computational algorithms for the solution of data assimilation problems in geophysical hydrodynamics is important in the contemporary computation and informational science to improve the quality of long-term prediction by using the hydrodynamics sea model. These problems are applied to close and solve in practice the appropriate inverse problems of the geophysical hydrodynamics. In this work the variational data assimilation problems in the Baltic Sea water area with displaced pole were formulated and studied [1]. We assume, that the unique function which is obtained by observation data processing is the function and we permit that the function is known only on a part of considering area (for example, on a part of the Baltic Sea). Numerical experiments on restoring the ocean heat flux and obtaining solution of the system (temperature, salinity, velocity, and sea surface height) in the Baltic Sea primitive equation hydrodynamics model [2] with assimilation procedure were carried out. In the calculations we used daily sea surface temperature observation from Danish meteorological Institute, prepared on the basis of measurements of the radiometer (AVHRR, AATSR and AMSRE) and spectroradiometer (SEVIRI and MODIS). The spatial resolution of the model grid with respect to the horizontal variables is uniform on latitude (0.2 degree) and varies on longitude from 0.04 to 0.0004 degree . The results of the numerical experiments are presented. This study was supported by the Russian Foundation for Basic Research (project №16-01-00548) and project №14-11-00609 by the Russian Science Foundation. References: [1] Agoshkov V.I., Parmuzin E.I., Zakharova N.B., Zalesny V.B., Shutyaev V.P., Gusev A.V. Variational assimilation of observation data in the mathematical model of

Assimilation of NUCAPS Retrieved Profiles in GSI for Unique Forecasting Applications

Science.gov (United States)

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

V. Shutyaev

2018-06-01

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

Data assimilation and model evaluation experiment datasets

Science.gov (United States)

Lai, Chung-Cheng A.; Qian, Wen; Glenn, Scott M.

1994-01-01

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Open source data assimilation framework for hydrological modeling

Science.gov (United States)

Ridler, Marc; Hummel, Stef; van Velzen, Nils; Katrine Falk, Anne; Madsen, Henrik

2013-04-01

An open-source data assimilation framework is proposed for hydrological modeling. Data assimilation (DA) in hydrodynamic and hydrological forecasting systems has great potential to improve predictions and improve model result. The basic principle is to incorporate measurement information into a model with the aim to improve model results by error minimization. Great strides have been made to assimilate traditional in-situ measurements such as discharge, soil moisture, hydraulic head and snowpack into hydrologic models. More recently, remotely sensed data retrievals of soil moisture, snow water equivalent or snow cover area, surface water elevation, terrestrial water storage and land surface temperature have been successfully assimilated in hydrological models. The assimilation algorithms have become increasingly sophisticated to manage measurement and model bias, non-linear systems, data sparsity (time & space) and undetermined system uncertainty. It is therefore useful to use a pre-existing DA toolbox such as OpenDA. OpenDA is an open interface standard for (and free implementation of) a set of tools to quickly implement DA and calibration for arbitrary numerical models. The basic design philosophy of OpenDA is to breakdown DA into a set of building blocks programmed in object oriented languages. To implement DA, a model must interact with OpenDA to create model instances, propagate the model, get/set variables (or parameters) and free the model once DA is completed. An open-source interface for hydrological models exists capable of all these tasks: OpenMI. OpenMI is an open source standard interface already adopted by key hydrological model providers. It defines a universal approach to interact with hydrological models during simulation to exchange data during runtime, thus facilitating the interactions between models and data sources. The interface is flexible enough so that models can interact even if the model is coded in a different language, represent

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

Science.gov (United States)

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

2017-08-01

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

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

Science.gov (United States)

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

2013-01-01

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

The leading mode of observed and CMIP5 ENSO-residual sea surface temperatures and associated changes in Indo-Pacific climate

Science.gov (United States)

Funk, Christopher C.; Hoell. Andrew,

2015-01-01

SSTs in the western Pacific Ocean have tracked closely with CMIP5 simulations despite recent hiatus cooling in the eastern Pacific. This paper quantifies these similarities and associated circulation and precipitation variations using the first global 1900–2012 ENSO-residual empirical orthogonal functions (EOFs) of 35 variables: observed SSTs; 28 CMIP5 SST simulations; Simple Ocean Data Assimilation (SODA) 25-, 70-, and 171-m ocean temperatures and sea surface heights (SSHs); and Twentieth Century Reanalysis, version 2 (20CRv2), surface winds and precipitation.

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

Science.gov (United States)

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

2018-01-01

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

Inverse analysis of inner surface temperature history from outer surface temperature measurement of a pipe

International Nuclear Information System (INIS)

Kubo, S; Ioka, S; Onchi, S; Matsumoto, Y

2010-01-01

When slug flow runs through a pipe, nonuniform and time-varying thermal stresses develop and there is a possibility that thermal fatigue occurs. Therefore it is necessary to know the temperature distributions and the stress distributions in the pipe for the integrity assessment of the pipe. It is, however, difficult to measure the inner surface temperature directly. Therefore establishment of the estimation method of the temperature history on inner surface of pipe is needed. As a basic study on the estimation method of the temperature history on the inner surface of a pipe with slug flow, this paper presents an estimation method of the temperature on the inner surface of a plate from the temperature on the outer surface. The relationship between the temperature history on the outer surface and the inner surface is obtained analytically. Using the results of the mathematical analysis, the inverse analysis method of the inner surface temperature history estimation from the outer surface temperature history is proposed. It is found that the inner surface temperature history can be estimated from the outer surface temperature history by applying the inverse analysis method, even when it is expressed by the multiple frequency components.

Weighted ensemble transform Kalman filter for image assimilation

Directory of Open Access Journals (Sweden)

Sebastien Beyou

2013-01-01

Full Text Available This study proposes an extension of the Weighted Ensemble Kalman filter (WEnKF proposed by Papadakis et al. (2010 for the assimilation of image observations. The main focus of this study is on a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF, incorporating directly as a measurement model a non-linear image reconstruction criterion. This technique has been compared to the original WEnKF on numerical and real world data of 2-D turbulence observed through the transport of a passive scalar. In particular, it has been applied for the reconstruction of oceanic surface current vorticity fields from sea surface temperature (SST satellite data. This latter technique enables a consistent recovery along time of oceanic surface currents and vorticity maps in presence of large missing data areas and strong noise.

Spatial dependence of color assimilation by the watercolor effect.

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Devinck, Frédéric; Delahunt, Peter B; Hardy, Joseph L; Spillmann, Lothar; Werner, John S

2006-01-01

Color assimilation with bichromatic contours was quantified for spatial extents ranging from von Bezold-type color assimilation to the watercolor effect. The magnitude and direction of assimilative hue change was measured as a function of the width of a rectangular stimulus. Assimilation was quantified by hue cancellation. Large hue shifts were required to null the color of stimuli < or = 9.3 min of arc in width, with an exponential decrease for stimuli increasing up to 7.4 deg. When stimuli were viewed through an achromatizing lens, the magnitude of the assimilation effect was reduced for narrow stimuli, but not for wide ones. These results demonstrate that chromatic aberration may account, in part, for color assimilation over small, but not large, surface areas.

Evaluation of Surface Fatigue Strength Based on Surface Temperature

Science.gov (United States)

Deng, Gang; Nakanishi, Tsutomu

Surface temperature is considered to be an integrated index that is dependent on not only the load and the dimensions at the contact point but also the sliding velocity, rolling velocity, surface roughness, and lubrication conditions. Therefore, the surface durability of rollers and gears can be evaluated more exactly and simply by the use of surface temperature rather than Hertzian stress. In this research, surface temperatures of rollers under different rolling and sliding conditions are measured using a thermocouple. The effects of load P, mean velocity Vm and sliding velocity Vs on surface temperature are clarified. An experimental formula, which expresses the linear relationship between surface temperature and the P0.86Vs1.31Vm-0.83 value, is used to determine surface temperature. By comparing calculated and measured temperature on the tooth surface of a gear, this formula is confirmed to be applicable for gear tooth surface temperature calculation.

Assimilation of GRACE Terrestrial Water Storage into a Land Surface Model: Evaluation 1 and Potential Value for Drought Monitoring in Western and Central Europe

Science.gov (United States)

Li, Bailing; Rodell, Matthew; Zaitchik, Benjamin F.; Reichle, Rolf H.; Koster, Randal D.; van Dam, Tonie M.

2012-01-01

A land surface model s ability to simulate states (e.g., soil moisture) and fluxes (e.g., runoff) is limited by uncertainties in meteorological forcing and parameter inputs as well as inadequacies in model physics. In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother. GRACE data assimilation led to improved runoff correlations with gauge data in 17 out of 18 hydrological basins, even in basins smaller than the effective resolution of GRACE. Improvements in root zone soil moisture were less conclusive, partly due to the shortness of the in situ data record. In addition to improving temporal correlations, GRACE data assimilation also reduced increasing trends in simulated monthly TWS and runoff associated with increasing rates of precipitation. GRACE assimilated root zone soil moisture and TWS fields exhibited significant changes in their dryness rankings relative to those without data assimilation, suggesting that GRACE data assimilation could have a substantial impact on drought monitoring. Signals of drought in GRACE TWS correlated well with MODIS Normalized Difference Vegetation Index (NDVI) data in most areas. Although they detected the same droughts during warm seasons, drought signatures in GRACE derived TWS exhibited greater persistence than those in NDVI throughout all seasons, in part due to limitations associated with the seasonality of vegetation.

Assessing the Regional/Diurnal Bias between Satellite Retrievals and GEOS-5/MERRA Model Estimates of Land Surface Temperature

Science.gov (United States)

Scarino, B. R.; Smith, W. L., Jr.; Minnis, P.; Bedka, K. M.

2017-12-01

Atmospheric models rely on high-accuracy, high-resolution initial radiometric and surface conditions for better short-term meteorological forecasts, as well as improved evaluation of global climate models. Continuous remote sensing of the Earth's energy budget, as conducted by the Clouds and Earth's Radiant Energy System (CERES) project, allows for near-realtime evaluation of cloud and surface radiation properties. It is unfortunately common for there to be bias between atmospheric/surface radiation models and Earth-observations. For example, satellite-observed surface skin temperature (Ts), an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface, can be biased due to atmospheric adjustment assumptions and anisotropy effects. Similarly, models are potentially biased by errors in initial conditions and regional forcing assumptions, which can be mitigated through assimilation with true measurements. As such, when frequent, broad-coverage, and accurate retrievals of satellite Ts are available, important insights into model estimates of Ts can be gained. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared method to produce anisotropy-corrected Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) satellite imagers. Regional and diurnal changes in model land surface temperature (LST) performance can be assessed owing to the somewhat continuous measurements of the LST offered by GEO satellites - measurements which are accurate to within 0.2 K. A seasonal, hourly comparison of satellite-observed LST with the NASA Goddard Earth Observing System Version 5 (GEOS-5) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) LST estimates is conducted to reveal regional and diurnal biases. This assessment is an important first step for evaluating the effectiveness of Ts assimilation, as well for determining the

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

Science.gov (United States)

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

2008-01-01

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

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

Science.gov (United States)

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

2017-12-01

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

Impact of SLA assimilation in the Sicily Channel Regional Model: model skills and mesoscale features

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A. Olita

2012-07-01

Full Text Available The impact of the assimilation of MyOcean sea level anomalies along-track data on the analyses of the Sicily Channel Regional Model was studied. The numerical model has a resolution of 1/32° degrees and is capable to reproduce mesoscale and sub-mesoscale features. The impact of the SLA assimilation is studied by comparing a simulation (SIM, which does not assimilate data with an analysis (AN assimilating SLA along-track multi-mission data produced in the framework of MyOcean project. The quality of the analysis was evaluated by computing RMSE of the misfits between analysis background and observations (sea level before assimilation. A qualitative evaluation of the ability of the analyses to reproduce mesoscale structures is accomplished by comparing model results with ocean colour and SST satellite data, able to detect such features on the ocean surface. CTD profiles allowed to evaluate the impact of the SLA assimilation along the water column. We found a significant improvement for AN solution in terms of SLA RMSE with respect to SIM (the averaged RMSE of AN SLA misfits over 2 years is about 0.5 cm smaller than SIM. Comparison with CTD data shows a questionable improvement produced by the assimilation process in terms of vertical features: AN is better in temperature while for salinity it gets worse than SIM at the surface. This suggests that a better a-priori description of the vertical error covariances would be desirable. The qualitative comparison of simulation and analyses with synoptic satellite independent data proves that SLA assimilation allows to correctly reproduce some dynamical features (above all the circulation in the Ionian portion of the domain and mesoscale structures otherwise misplaced or neglected by SIM. Such mesoscale changes also infer that the eddy momentum fluxes (i.e. Reynolds stresses show major changes in the Ionian area. Changes in Reynolds stresses reflect a different pumping of eastward momentum from the eddy to

Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

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A. L. Barbu

2014-01-01

Full Text Available The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM and leaf area index (LAI. This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs land surface model within the the externalised surface model (SURFEX modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function matching technique. A multivariate multi-scale land data assimilation system (LDAS based on the extended Kalman Filter (EKF is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011. The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil

Development Of A Data Assimilation Capability For RAPID

Science.gov (United States)

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

2017-12-01

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

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

Science.gov (United States)

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

2015-04-01

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

Reanalysis of the Indian summer monsoon: four dimensional data assimilation of AIRS retrievals in a regional data assimilation and modeling framework

Science.gov (United States)

Attada, Raju; Parekh, Anant; Chowdary, J. S.; Gnanaseelan, C.

2018-04-01

This work is the first attempt to produce a multi-year downscaled regional reanalysis of the Indian summer monsoon (ISM) using the National Centers for Environmental Prediction (NCEP) operational analyses and Atmospheric Infrared Sounder (AIRS) version 5 temperature and moisture retrievals in a regional model. Reanalysis of nine monsoon seasons (2003-2011) are produced in two parallel setups. The first set of experiments simply downscale the original NCEP operational analyses, whilst the second one assimilates the AIRS temperature and moisture profiles. The results show better representation of the key monsoon features such as low level jet, tropical easterly jet, subtropical westerly jet, monsoon trough and the spatial pattern of precipitation when AIRS profiles are assimilated (compared to those without AIRS data assimilation). The distribution of temperature, moisture and meridional gradients of dynamical and thermodynamical fields over the monsoon region are better represented in the reanalysis that assimilates AIRS profiles. The change induced by AIRS data on the moist and thermodynamic conditions results in more realistic rendering of the vertical shear associated with the monsoon, which in turn leads to a proper moisture transport and the moist convective feedback. This feedback benefits the representation of the regional monsoon characteristics, the monsoon dynamics and the moist convective processes on the seasonal time scale. This study emphasizes the use of AIRS soundings for downscaling of ISM representation in a regional reanalysis.

Reanalysis of the Indian summer monsoon: four dimensional data assimilation of AIRS retrievals in a regional data assimilation and modeling framework

KAUST Repository

Attada, Raju

2017-07-04

This work is the first attempt to produce a multi-year downscaled regional reanalysis of the Indian summer monsoon (ISM) using the National Centers for Environmental Prediction (NCEP) operational analyses and Atmospheric Infrared Sounder (AIRS) version 5 temperature and moisture retrievals in a regional model. Reanalysis of nine monsoon seasons (2003–2011) are produced in two parallel setups. The first set of experiments simply downscale the original NCEP operational analyses, whilst the second one assimilates the AIRS temperature and moisture profiles. The results show better representation of the key monsoon features such as low level jet, tropical easterly jet, subtropical westerly jet, monsoon trough and the spatial pattern of precipitation when AIRS profiles are assimilated (compared to those without AIRS data assimilation). The distribution of temperature, moisture and meridional gradients of dynamical and thermodynamical fields over the monsoon region are better represented in the reanalysis that assimilates AIRS profiles. The change induced by AIRS data on the moist and thermodynamic conditions results in more realistic rendering of the vertical shear associated with the monsoon, which in turn leads to a proper moisture transport and the moist convective feedback. This feedback benefits the representation of the regional monsoon characteristics, the monsoon dynamics and the moist convective processes on the seasonal time scale. This study emphasizes the use of AIRS soundings for downscaling of ISM representation in a regional reanalysis.

Assimilation of temperature and hydraulic gradients for quantifying the spatial variability of streambed hydraulics

Science.gov (United States)

Huang, Xiang; Andrews, Charles B.; Liu, Jie; Yao, Yingying; Liu, Chuankun; Tyler, Scott W.; Selker, John S.; Zheng, Chunmiao

2016-08-01

Understanding the spatial and temporal characteristics of water flux into or out of shallow aquifers is imperative for water resources management and eco-environmental conservation. In this study, the spatial variability in the vertical specific fluxes and hydraulic conductivities in a streambed were evaluated by integrating distributed temperature sensing (DTS) data and vertical hydraulic gradients into an ensemble Kalman filter (EnKF) and smoother (EnKS) and an empirical thermal-mixing model. The formulation of the EnKF/EnKS assimilation scheme is based on a discretized 1D advection-conduction equation of heat transfer in the streambed. We first systematically tested a synthetic case and performed quantitative and statistical analyses to evaluate the performance of the assimilation schemes. Then a real-world case was evaluated to calculate assimilated specific flux. An initial estimate of the spatial distributions of the vertical hydraulic gradients was obtained from an empirical thermal-mixing model under steady-state conditions using a constant vertical hydraulic conductivity. Then, this initial estimate was updated by repeatedly dividing the assimilated specific flux by estimates of the vertical hydraulic gradients to obtain a refined spatial distribution of vertical hydraulic gradients and vertical hydraulic conductivities. Our results indicate that optimal parameters can be derived with fewer iterations but greater simulation effort using the EnKS compared with the EnKF. For the field application in a stream segment of the Heihe River Basin in northwest China, the average vertical hydraulic conductivities in the streambed varied over three orders of magnitude (5 × 10-1 to 5 × 102 m/d). The specific fluxes ranged from near zero (qz < ±0.05 m/d) to ±1.0 m/d, while the vertical hydraulic gradients were within the range of -0.2 to 0.15 m/m. The highest and most variable fluxes occurred adjacent to a debris-dam and bridge pier. This phenomenon is very likely

The influence of rolled erosion control systems on soil temperature and surface albedo: part I. A greenhouse experiment

International Nuclear Information System (INIS)

Sutherland, R.A.; Menard, T.; Perry, J.L.; Penn, D.C.

1998-01-01

A greenhouse study examined the influences of various surface covers (a bare control soil and seven rolled erosion control systems—RECS) on surface radiative properties, and soil temperature. In our companion paper we examine relationships with soil moisture, biomass production, and nutrient assimilation. Randomization and replication were key components to our study of microclimate under tropical radiation conditions. The bare Oxisol control soil exhibited the most extreme microclimatic conditions with the lowest albedo (not significantly different from that of P300© North American Green, a dark green polypropylene system), and the highest mean and maximum hourly temperatures recorded at depths of 5 and 8 cm. This hostile climatic environment was not conducive to biomass production or moisture storage and it is likely that the observed soil surface crusts impeded plant emergence. Rolled erosion control systems, on the other hand, generally moderated soil temperatures by reflecting more shortwave radiation, implying less heat energy at the surface for conduction to the soil. The result was that RECS exhibited lower mean soil temperatures, higher minimum temperatures and lower maximum soil temperatures. An aspen excelsior system (Curlex I© Excelsior) had the highest albedo and the soil beneath this system exhibited the greatest temperature modulation. Open-weave systems composed of jute (Geojute© Price & Pictures) and coconut fibers (BioD-Mat 70© RoLanka) were the RECS most similar in temperature response to the bare control soil. Other systems examined were intermediate in their temperature response and surface albedo (i.e., SC150BN© North American Green, C125© North American Green and Futerra© Conwed Fibers). (author)

Effective Assimilation of Global Precipitation

Science.gov (United States)

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

2012-12-01

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

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

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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.

Real-data tests of a single-Doppler radar assimilation system

Science.gov (United States)

Nehrkorn, Thomas; Hegarty, James; Hamill, Thomas M.

1994-06-01

Real data tests of a single-Doppler radar data assimilation and forecast system have been conducted for a Florida sea breeze case. The system consists of a hydrostatic mesoscale model used for prediction of the preconvective boundary layer, an objective analysis that combines model first guess fields with radar derived horizontal winds, a thermodynamic retrieval scheme that obtains temperature information from the three-dimensional wind field and its temporal evolution, and a Newtonian nudging scheme for forcing the model forecast to closer agreement with the analysis. As was found in earlier experiments with simulated data, assimilation using Newtonian nudging benefits from temperature data in addition to wind data. The thermodynamic retrieval technique was successful in retrieving a horizontal temperature gradient from the radar-derived wind fields that, when assimilated into the model, led to a significantly improved forecast of the seabreeze strength and position.

Impact of data assimilation of physical variables on the spring bloom from TOPAZ operational runs in the North Atlantic

Directory of Open Access Journals (Sweden)

A. Samuelsen

2009-12-01

Full Text Available A reanalysis of the North Atlantic spring bloom in 2007 was produced using the real-time analysis from the TOPAZ North Atlantic and Arctic forecasting system. The TOPAZ system uses a hybrid coordinate general circulation ocean model and assimilates physical observations: sea surface anomalies, sea surface temperatures, and sea-ice concentrations using the Ensemble Kalman Filter. This ocean model was coupled to an ecosystem model, NORWECOM (Norwegian Ecological Model System, and the TOPAZ-NORWECOM coupled model was run throughout the spring and summer of 2007. The ecosystem model was run online, restarting from analyzed physical fields (result after data assimilation every 7 days. Biological variables were not assimilated in the model. The main purpose of the study was to investigate the impact of physical data assimilation on the ecosystem model. This was determined by comparing the results to those from a model without assimilation of physical data. The regions of focus are the North Atlantic and the Arctic Ocean. Assimilation of physical variables does not affect the results from the ecosystem model significantly. The differences between the weekly mean values of chlorophyll are normally within 5–10% during the summer months, and the maximum difference of ~20% occurs in the Arctic, also during summer. Special attention was paid to the nutrient input from the North Atlantic to the Nordic Seas and the impact of ice-assimilation on the ecosystem. The ice-assimilation increased the phytoplankton concentration: because there was less ice in the assimilation run, this increased both the mixing of nutrients during winter and the area where production could occur during summer. The forecast was also compared to remotely sensed chlorophyll, climatological nutrients, and in-situ data. The results show that the model reproduces a realistic annual cycle, but the chlorophyll concentrations tend to be between 0.1 and 1.0 mg chla/m³ too

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

Science.gov (United States)

Peters-Lidard, Christa D.

2011-01-01

Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

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

Science.gov (United States)

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

2016-04-01

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

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

Science.gov (United States)

Zakeri, Zeinab; Azadi, Majid; Ghader, Sarmad

2018-01-01

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

Assimilative and non-assimilative color spreading in the watercolor configuration

Directory of Open Access Journals (Sweden)

Eiji eKimura

2014-09-01

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

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

Science.gov (United States)

Kimura, Eiji; Kuroki, Mikako

2014-01-01

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

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

Science.gov (United States)

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

2017-12-01

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

The role of ocean-atmosphere interaction in Typhoon Sinlaku (2008) using a regional coupled data assimilation system

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Wada, Akiyoshi; Kunii, Masaru

2017-05-01

For improving analyses of tropical cyclone (TC) and sea surface temperature (SST) and thereby TC simulations, a regional mesoscale strongly coupled atmosphere-ocean data assimilation system was developed with the local ensemble transform Kalman filter (LETKF) implemented with the Japan Meteorological Agency's nonhydrostatic model (NHM) coupled with a multilayer ocean model and the third-generation ocean wave model. The NHM-LETKF coupled data assimilation system was applied to Typhoon Sinlaku (2008) along with the original NHM-LETKF system to investigate the sensitivity of Sinlaku to SST assimilation with the Level 2 Pre-processed (L2P) standard product of satellite SST. SST calculated in the coupled-assimilation experiment with the coupled data assimilation system and the satellite SST (CPL) showed a better correlation with Optimally Interpolated SST than SST used in the control experiment with the original NHM-LETKF (CNTL) and SST calculated in the succession experiment with the coupled system without satellite SST (SUCC). The time series in the CPL experiment well captured the variation in the SST observed at the Kuroshio Extension Observation buoy site. In addition, TC-induced sea surface cooling was analyzed more realistically in the CPL experiment than that in the CNTL and SUCC experiments. However, the central pressure analyzed in each three experiments was overestimated compared with the Regional Specialized Meteorological Center Tokyo best-track central pressure, mainly due to the coarse horizontal resolution of 15 km. The 96 h TC simulations indicated that the CPL experiment provided more favorable initial and boundary conditions than the CNTL experiment to simulate TC tracks more accurately.

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

Science.gov (United States)

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

2014-01-01

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

Joint Center for Satellite Data Assimilation Overview and Research Activities

Science.gov (United States)

Auligne, T.

2017-12-01

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

Estimation of bare soil surface temperature from air temperature and ...

African Journals Online (AJOL)

Soil surface temperature has critical influence on climate, agricultural and hydrological activities since it serves as a good indicator of the energy budget of the earth's surface. Two empirical models for estimating soil surface temperature from air temperature and soil depth temperature were developed. The coefficient of ...

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

Science.gov (United States)

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

2012-01-01

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

Estimation of the drag coefficient from the upper ocean response to a hurricane: A variational data assimilation approach

KAUST Repository

Zedler, Sarah

2013-08-01

We seek to determine whether a small number of measurements of upper ocean temperature and currents can be used to make estimates of the drag coefficient that have a smaller range of uncertainty than previously found. We adopt a numerical approach in an inverse problem setup using an ocean model and its adjoint, to assimilate data and to adjust the drag coefficient parameterization (here the free parameter) with wind speed that corresponds to the minimum of a model minus data misfit or cost function. Pseudo data are generated from a reference forward simulation, and are perturbed with different levels of Gaussian distributed noise. It is found that it is necessary to assimilate both surface current speed and temperature data to obtain improvement over previous estimates of the drag coefficient. When data is assimilated without any smoothing or constraints on the solution, the drag coefficient is overestimated at low wind speeds and there are unrealistic, high frequency oscillations in the adjusted drag coefficient curve. When second derivatives of the drag coefficient curve are penalized and the solution is constrained to experimental values at low wind speeds, the adjusted drag coefficient is within 10% of its target value. This result is robust to the addition of realistic random noise meant to represent turbulence due to the presence of mesoscale background features in the assimilated data, or to the wind speed time series to model its unsteady and gusty character. When an eddy is added to the background flow field in both the initial condition and the assimilated data time series, the target and adjusted drag coefficient are within 10% of one another, regardless of whether random noise is added to the assimilated data. However, when the eddy is present in the assimilated data but is not in the initial conditions, the drag coefficient is overestimated by as much as 30%. This carries the implication that when real data is assimilated, care needs to be taken in

Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

Science.gov (United States)

Adolph, Alden C.; Albert, Mary R.; Hall, Dorothy K.

2018-03-01

As rapid warming of the Arctic occurs, it is imperative that climate indicators such as temperature be monitored over large areas to understand and predict the effects of climate changes. Temperatures are traditionally tracked using in situ 2 m air temperatures and can also be assessed using remote sensing techniques. Remote sensing is especially valuable over the Greenland Ice Sheet, where few ground-based air temperature measurements exist. Because of the presence of surface-based temperature inversions in ice-covered areas, differences between 2 m air temperature and the temperature of the actual snow surface (referred to as skin temperature) can be significant and are particularly relevant when considering validation and application of remote sensing temperature data. We present results from a field campaign extending from 8 June to 18 July 2015, near Summit Station in Greenland, to study surface temperature using the following measurements: skin temperature measured by an infrared (IR) sensor, 2 m air temperature measured by a National Oceanic and Atmospheric Administration (NOAA) meteorological station, and a Moderate Resolution Imaging Spectroradiometer (MODIS) surface temperature product. Our data indicate that 2 m air temperature is often significantly higher than snow skin temperature measured in situ, and this finding may account for apparent biases in previous studies of MODIS products that used 2 m air temperature for validation. This inversion is present during our study period when incoming solar radiation and wind speed are both low. As compared to our in situ IR skin temperature measurements, after additional cloud masking, the MOD/MYD11 Collection 6 surface temperature standard product has an RMSE of 1.0 °C and a mean bias of -0.4 °C, spanning a range of temperatures from -35 to -5 °C (RMSE = 1.6 °C and mean bias = -0.7 °C prior to cloud masking). For our study area and time series, MODIS surface temperature products agree with skin surface

A 3-step framework for understanding the added value of surface soil moisture measurements for large-scale runoff prediction via data assimilation - a synthetic study in the Arkansas-Red River basin

Science.gov (United States)

Mao, Y.; Crow, W. T.; Nijssen, B.

2017-12-01

Soil moisture (SM) plays an important role in runoff generation both by partitioning infiltration and surface runoff during rainfall events and by controlling the rate of subsurface flow during inter-storm periods. Therefore, more accurate SM state estimation in hydrologic models is potentially beneficial for streamflow prediction. Various previous studies have explored the potential of assimilating SM data into hydrologic models for streamflow improvement. These studies have drawn inconsistent conclusions, ranging from significantly improved runoff via SM data assimilation (DA) to limited or degraded runoff. These studies commonly treat the whole assimilation procedure as a black box without separating the contribution of each step in the procedure, making it difficult to attribute the underlying causes of runoff improvement (or the lack thereof). In this study, we decompose the overall DA process into three steps by answering the following questions (3-step framework): 1) how much can assimilation of surface SM measurements improve surface SM state in a hydrologic model? 2) how much does surface SM improvement propagate to deeper layers? 3) How much does (surface and deeper-layer) SM improvement propagate into runoff improvement? A synthetic twin experiment is carried out in the Arkansas-Red River basin ( 600,000 km2) where a synthetic "truth" run, an open-loop run (without DA) and a DA run (where synthetic surface SM measurements are assimilated) are generated. All model runs are performed at 1/8 degree resolution and over a 10-year period using the Variable Infiltration Capacity (VIC) hydrologic model at a 3-hourly time step. For the DA run, the ensemble Kalman filter (EnKF) method is applied. The updated surface and deeper-layer SM states with DA are compared to the open-loop SM to quantitatively evaluate the first two steps in the framework. To quantify the third step, a set of perfect-state runs are generated where the "true" SM states are directly inserted

Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season

Science.gov (United States)

Robock, Alan; Luo, Lifeng; Wood, Eric F.; Wen, Fenghua; Mitchell, Kenneth E.; Houser, Paul R.; Schaake, John C.; Lohmann, Dag; Cosgrove, Brian; Sheffield, Justin; Duan, Qingyun; Higgins, R. Wayne; Pinker, Rachel T.; Tarpley, J. Dan; Basara, Jeffery B.; Crawford, Kenneth C.

2003-11-01

North American Land Data Assimilation System (NLDAS) land surface models have been run for a retrospective period forced by atmospheric observations from the Eta analysis and actual precipitation and downward solar radiation to calculate land hydrology. We evaluated these simulations using in situ observations over the southern Great Plains for the periods of May-September of 1998 and 1999 by comparing the model outputs with surface latent, sensible, and ground heat fluxes at 24 Atmospheric Radiation Measurement/Cloud and Radiation Testbed stations and with soil temperature and soil moisture observations at 72 Oklahoma Mesonet stations. The standard NLDAS models do a fairly good job but with differences in the surface energy partition and in soil moisture between models and observations and among models during the summer, while they agree quite well on the soil temperature simulations. To investigate why, we performed a series of experiments accounting for differences between model-specified soil types and vegetation and those observed at the stations, and differences in model treatment of different soil types, vegetation properties, canopy resistance, soil column depth, rooting depth, root density, snow-free albedo, infiltration, aerodynamic resistance, and soil thermal diffusivity. The diagnosis and model enhancements demonstrate how the models can be improved so that they can be used in actual data assimilation mode.

Snow Radiance Data Assimilation over High Mountain Asia Using the NASA Land Information System and a Well-Trained Support Vector Machine

Science.gov (United States)

Kwon, Y.; Forman, B. A.; Yoon, Y.; Kumar, S.

2017-12-01

High Mountain Asia (HMA) has been progressively losing ice and snow in recent decades, which could negatively impact regional water supply and native ecosystems. One goal of this study is to characterize the spatiotemporal variability of snow (and ice) across the HMA region. In addition, modeled snow water equivalent (SWE) estimates will be enhanced through the assimilation of passive microwave brightness temperatures (TB) collected by the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) as part of a radiance assimilation system. The radiance assimilation framework includes the NASA Land Information System (LIS) in conjunction with a well-trained support vector machine (SVM) that acts as the observation operator. The Noah Land Surface Model with multi-parameterization options (Noah-MP) is used as the prior model for simulating snow dynamics. Noah-MP is forced by meteorological fields from the NASA Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis for the periods 01 Sep. 2002 to 01 Sep. 2011. The radiance assimilation system requires two separate phases: 1) training and 2) assimilation. During the training phase, a nonlinear SVM is generated for three different AMSR-E frequencies - 10.65, 18.7, and 36.5 GHz - at both vertical and horizontal polarization. The trained SVM is then used to predict TB during the assimilation phase. An ensemble Kalman filter will be used to condition the model on AMSR-E brightness temperatures not used during SVM training. The performance of the Noah-MP (with and without radiance assimilation) will be assessed via comparison to in-situ measurements, remotely-sensing geophysical retrievals, and other reanalysis products.

Evaluation of Oceanic Surface Observation for Reproducing the Upper Ocean Structure in ECHAM5/MPI-OM

Science.gov (United States)

Luo, Hao; Zheng, Fei; Zhu, Jiang

2017-12-01

Better constraints of initial conditions from data assimilation are necessary for climate simulations and predictions, and they are particularly important for the ocean due to its long climate memory; as such, ocean data assimilation (ODA) is regarded as an effective tool for seasonal to decadal predictions. In this work, an ODA system is established for a coupled climate model (ECHAM5/MPI-OM), which can assimilate all available oceanic observations using an ensemble optimal interpolation approach. To validate and isolate the performance of different surface observations in reproducing air-sea climate variations in the model, a set of observing system simulation experiments (OSSEs) was performed over 150 model years. Generally, assimilating sea surface temperature, sea surface salinity, and sea surface height (SSH) can reasonably reproduce the climate variability and vertical structure of the upper ocean, and assimilating SSH achieves the best results compared to the true states. For the El Niño-Southern Oscillation (ENSO), assimilating different surface observations captures true aspects of ENSO well, but assimilating SSH can further enhance the accuracy of ENSO-related feedback processes in the coupled model, leading to a more reasonable ENSO evolution and air-sea interaction over the tropical Pacific. For ocean heat content, there are still limitations in reproducing the long time-scale variability in the North Atlantic, even if SSH has been taken into consideration. These results demonstrate the effectiveness of assimilating surface observations in capturing the interannual signal and, to some extent, the decadal signal but still highlight the necessity of assimilating profile data to reproduce specific decadal variability.

Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

Directory of Open Access Journals (Sweden)

A. C. Adolph

2018-03-01

Full Text Available As rapid warming of the Arctic occurs, it is imperative that climate indicators such as temperature be monitored over large areas to understand and predict the effects of climate changes. Temperatures are traditionally tracked using in situ 2 m air temperatures and can also be assessed using remote sensing techniques. Remote sensing is especially valuable over the Greenland Ice Sheet, where few ground-based air temperature measurements exist. Because of the presence of surface-based temperature inversions in ice-covered areas, differences between 2 m air temperature and the temperature of the actual snow surface (referred to as skin temperature can be significant and are particularly relevant when considering validation and application of remote sensing temperature data. We present results from a field campaign extending from 8 June to 18 July 2015, near Summit Station in Greenland, to study surface temperature using the following measurements: skin temperature measured by an infrared (IR sensor, 2 m air temperature measured by a National Oceanic and Atmospheric Administration (NOAA meteorological station, and a Moderate Resolution Imaging Spectroradiometer (MODIS surface temperature product. Our data indicate that 2 m air temperature is often significantly higher than snow skin temperature measured in situ, and this finding may account for apparent biases in previous studies of MODIS products that used 2 m air temperature for validation. This inversion is present during our study period when incoming solar radiation and wind speed are both low. As compared to our in situ IR skin temperature measurements, after additional cloud masking, the MOD/MYD11 Collection 6 surface temperature standard product has an RMSE of 1.0 °C and a mean bias of −0.4 °C, spanning a range of temperatures from −35 to −5 °C (RMSE  =  1.6 °C and mean bias  =  −0.7 °C prior to cloud masking. For our study area and time series

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

Science.gov (United States)

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

2018-03-01

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

Calibration and combination of monthly near-surface temperature and precipitation predictions over Europe

Science.gov (United States)

Rodrigues, Luis R. L.; Doblas-Reyes, Francisco J.; Coelho, Caio A. S.

2018-02-01

A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often.

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

Science.gov (United States)

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

2017-12-01

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

Hydrological land surface modelling

DEFF Research Database (Denmark)

Ridler, Marc-Etienne Francois

Recent advances in integrated hydrological and soil-vegetation-atmosphere transfer (SVAT) modelling have led to improved water resource management practices, greater crop production, and better flood forecasting systems. However, uncertainty is inherent in all numerical models ultimately leading...... temperature are explored in a multi-objective calibration experiment to optimize the parameters in a SVAT model in the Sahel. The two satellite derived variables were effective at constraining most land-surface and soil parameters. A data assimilation framework is developed and implemented with an integrated...... and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface...

Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS

Directory of Open Access Journals (Sweden)

Yongxin Zhang

2016-01-01

Full Text Available An analysis of the impacts of assimilating the Tropospheric Airborne Meteorological Data Report (TAMDAR data with the Weather Research and Forecasting- (WRF- real-time four-dimensional data assimilation (RTFDDA and forecasting system over the Contiguous US (CONUS is presented. The impacts of the horizontal resolution increase from 12 km to 4 km on the WRF-RTFDDA simulations are also examined in conjunction with the TAMDAR data impacts. The assimilation of the TAMDAR data reduces the root mean squared error of the moisture field predictions and increases the correlation between the predictions and the observations for both domains with 12 km and 4 km grid spacings. The TAMDAR data reduce the model dry biases in the middle and lower levels by adding moisture at those levels. Assimilating the TAMDAR data improves temperature predictions at middle to high levels and wind speed predictions at all levels especially for the 12 km domain. Increasing the horizontal resolution from 12 km to 4 km results in significantly larger impacts on surface variables than assimilating the TAMDAR data.

Data Assimilation: Making Sense of Earth Observation

Directory of Open Access Journals (Sweden)

William Albert Lahoz

2014-05-01

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

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

Science.gov (United States)

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

2018-02-01

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

Evaluation of Flat Surface Temperature Probes

Science.gov (United States)

Beges, G.; Rudman, M.; Drnovsek, J.

2011-01-01

The objective of this paper is elaboration of elements related to metrological analysis in the field of surface temperature measurement. Surface temperature measurements are applicable in many fields. As examples, safety testing of electrical appliances and a pharmaceutical production line represent case studies for surface temperature measurements. In both cases correctness of the result of the surface temperature has an influence on final product safety and quality and thus conformity with specifications. This paper deals with the differences of flat surface temperature probes in measuring the surface temperature. For the purpose of safety testing of electrical appliances, surface temperature measurements are very important for safety of the user. General requirements are presented in European standards, which support requirements in European directives, e.g., European Low Voltage Directive 2006/95/EC and pharmaceutical requirements, which are introduced in official state legislation. This paper introduces a comparison of temperature measurements of an attached thermocouple on the measured surface and measurement with flat surface temperature probes. As a heat generator, a so called temperature artifact is used. It consists of an aluminum plate with an incorporated electrical heating element with very good temperature stability in the central part. The probes and thermocouple were applied with different forces to the surface in horizontal and vertical positions. The reference temperature was measured by a J-type fine-wire (0.2 mm) thermocouple. Two probes were homemade according to requirements in the European standard EN 60335-2-9/A12, one with a fine-wire (0.2 mm) thermocouple and one with 0.5mm of thermocouple wire diameter. Additional commercially available probes were compared. Differences between probes due to thermal conditions caused by application of the probe were found. Therefore, it can happen that measurements are performed with improper equipment or

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

Science.gov (United States)

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

2010-01-01

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

Downscaling the 2D Bénard convection equations using continuous data assimilation

KAUST Repository

Altaf, Muhammad; Titi, E. S.; Gebrael, T.; Knio, Omar; Zhao, L.; McCabe, Matthew; Hoteit, Ibrahim

2017-01-01

We consider a recently introduced continuous data assimilation (CDA) approach for downscaling a coarse resolution configuration of the 2D Bénard convection equations into a finer grid. In this CDA, a nudging term, estimated as the misfit between some interpolants of the assimilated coarse-grid measurements and the fine-grid model solution, is added to the model equations to constrain the model. The main contribution of this study is a performance analysis of CDA for downscaling measurements of temperature and velocity. These measurements are assimilated either separately or simultaneously, and the results are compared against those resulting from the standard point-to-point nudging approach (NA). Our numerical results suggest that the CDA solution outperforms that of NA, always converging to the true solution when the velocity is assimilated as has been theoretically proven. Assimilation of temperature measurements only may not always recover the true state as demonstrated in the case study. Various runs are conducted to evaluate the sensitivity of CDA to noise in the measurements, the size, and the time frequency of the measured grid, suggesting a more robust behavior of CDA compared to that of NA.

Downscaling the 2D Bénard convection equations using continuous data assimilation

KAUST Repository

Altaf, Muhammad

2017-02-27

We consider a recently introduced continuous data assimilation (CDA) approach for downscaling a coarse resolution configuration of the 2D Bénard convection equations into a finer grid. In this CDA, a nudging term, estimated as the misfit between some interpolants of the assimilated coarse-grid measurements and the fine-grid model solution, is added to the model equations to constrain the model. The main contribution of this study is a performance analysis of CDA for downscaling measurements of temperature and velocity. These measurements are assimilated either separately or simultaneously, and the results are compared against those resulting from the standard point-to-point nudging approach (NA). Our numerical results suggest that the CDA solution outperforms that of NA, always converging to the true solution when the velocity is assimilated as has been theoretically proven. Assimilation of temperature measurements only may not always recover the true state as demonstrated in the case study. Various runs are conducted to evaluate the sensitivity of CDA to noise in the measurements, the size, and the time frequency of the measured grid, suggesting a more robust behavior of CDA compared to that of NA.

Data assimilation for air quality models

DEFF Research Database (Denmark)

Silver, Jeremy David

2014-01-01

-dimensional optimal interpolation procedure (OI), an Ensemble Kalman Filter (EnKF), and a three-dimensional variational scheme (3D-var). The three assimilation procedures are described and tested. A multi-faceted approach is taken for the verification, using independent measurements from surface air-quality...

Temperature dependence of nuclear surface properties

International Nuclear Information System (INIS)

Campi, X.; Stringari, S.

1982-01-01

Thermal properties of nuclear surface are investigated in a semi-infinite medium. Explicit analytical expression are given for the temperature dependence of surface thickness, surface energy and surface free energy. In this model the temperature effects depend critically on the nuclear incompressibility and on the shape of the effective mass at the surface. To illustrate the relevance of these effects we made an estimate of the temperature dependence of the fission barrier height. (orig.)

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

International Nuclear Information System (INIS)

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

1994-01-01

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

O the Development and Use of Four-Dimensional Data Assimilation in Limited-Area Mesoscale Models Used for Meteorological Analysis.

Science.gov (United States)

Stauffer, David R.

1990-01-01

The application of dynamic relationships to the analysis problem for the atmosphere is extended to use a full-physics limited-area mesoscale model as the dynamic constraint. A four-dimensional data assimilation (FDDA) scheme based on Newtonian relaxation or "nudging" is developed and evaluated in the Penn State/National Center for Atmospheric Research (PSU/NCAR) mesoscale model, which is used here as a dynamic-analysis tool. The thesis is to determine what assimilation strategies and what meterological fields (mass, wind or both) have the greatest positive impact on the 72-h numerical simulations (dynamic analyses) of two mid-latitude, real-data cases. The basic FDDA methodology is tested in a 10-layer version of the model with a bulk-aerodynamic (single-layer) representation of the planetary boundary layer (PBL), and refined in a 15-layer version of the model by considering the effects of data assimilation within a multi-layer PBL scheme. As designed, the model solution can be relaxed toward either gridded analyses ("analysis nudging"), or toward the actual observations ("obs nudging"). The data used for assimilation include standard 12-hourly rawinsonde data, and also 3-hourly mesoalpha-scale surface data which are applied within the model's multi-layer PBL. Continuous assimilation of standard-resolution rawinsonde data into the 10-layer model successfully reduced large-scale amplitude and phase errors while the model realistically simulated mesoscale structures poorly defined or absent in the rawinsonde analyses and in the model simulations without FDDA. Nudging the model fields directly toward the rawinsonde observations generally produced results comparable to nudging toward gridded analyses. This obs -nudging technique is especially attractive for the assimilation of high-frequency, asynoptic data. Assimilation of 3-hourly surface wind and moisture data into the 15-layer FDDA system was most effective for improving the simulated precipitation fields because a

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

Science.gov (United States)

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

2018-02-01

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

SMAP Data Assimilation at NASA SPoRT

Science.gov (United States)

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

2016-01-01

The NASA Short-Term Prediction Research and Transition (SPoRT) Center maintains a near-real- time run of the Noah Land Surface Model within the Land Information System (LIS) at 3-km resolution. Soil moisture products from this model are used by several NOAA/National Weather Service Weather Forecast Offices for flood and drought situational awareness. We have implemented assimilation of soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active/ Passive (SMAP) satellites, and are now evaluating the SMAP assimilation. The SMAP-enhanced LIS product is planned for public release by October 2016.

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

Science.gov (United States)

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

2009-01-01

NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins,". As described in Kumar et al., 2007, and demonstrated in Case et al., 2008, and Santanello et al., 2009, LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling the enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation as described in Peters-Lidard et al. (2008) and Santanello et al. (2007), who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs. LIS has also recently been demonstrated for multi-model data assimilation (Kumar et al., 2008) using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature. Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation. Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeoroogical modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems.

Assimilation of SMOS Retrievals in the Land Information System

Science.gov (United States)

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

2016-01-01

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

Effects of entrainment through Oconee Nuclear Station on carbon-14 assimilation rates of phytoplankton

International Nuclear Information System (INIS)

Kreh, T.V.; Derwort, J.E.

1976-01-01

Carbon assimilation rates of phytoplankton communities entrained through Oconee Nuclear Station were measured on six dates during 1974. Thermal, mechanical, condenser, and multiple entrainment effects on uptake rates were compared by incubating samples in vitro in controlled-temperature water baths. Duplicate light and dark bottles containing water from four cooling-system locations were exposed to temperatures approximating intake and discharge temperatures. The relationships were variable, but exposure of the hypolimnetic intake water at near-discharge temperatures (thermal effect) stimulated primary productivity in four of six experiments. Multiple entrainment and mechanical effects caused no consistent change in assimilation rates

Assimilation of satellite altimeter data into an open ocean model

Science.gov (United States)

Vogeler, Armin; SchröTer, Jens

1995-08-01

Geosat sea surface height data are assimilated into an eddy-resolving quasi-geostrophic open ocean model using the adjoint technique. The method adjusts the initial conditions for all layers and is successful on the timescale of a few weeks. Time-varying values for the open boundaries are prescribed by a much larger quasi-geostrophic model of the Antarctic Circumpolar Current (ACC). Both models have the same resolution of approximately 20×20 km (1/3°×1/6°), have three layers, and include realistic bottom topography and coastlines. The open model box is embedded in the African sector of the ACC. For continuous assimilation of satellite data into the larger model the nudging technique is applied. These results are used for the adjoint optimization procedure as boundary conditions and as a first guess for the initial condition. For the open model box the difference between model and satellite sea surface height that remains after the nudging experiment amounts to a 19-cm root-mean-square error (rmse). By assimilation into the regional model this value can be reduced to a 6-cm rmse for an assimilation period of 20 days. Several experiments which attempt to improve the convergence of the iterative optimization method are reported. Scaling and regularization by smoothing have to be applied carefully. Especially during the first 10 iterations, the convergence can be improved considerably by low-pass filtering of the cost function gradient. The result of a perturbation experiment shows that for longer assimilation periods the influence of the boundary values becomes dominant and they should be determined inversely by data assimilation into the open ocean model.

An integrated GIS application system for soil moisture data assimilation

Science.gov (United States)

Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

2014-11-01

The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

Estimation of soil hydraulic information through the assimilation of values of the surface moisture: extended approximations (unscented)

International Nuclear Information System (INIS)

Medina, Hanoi; Hernández, Yunay; Batista, Giovanni Chirico; Romano, Nunzio

2008-01-01

Effective estimation of soil hydraulic information through the assimilation of surface moisture values, demand the use of approximations necessarily related to highly nonlinear models. The Kalman Filter 'Unscented' ( UKF ) has emerged in the literature as a safe and easy technique to implement than the most rudimentary, but more widely used, Kalman Filter 'Linear' (EKF ), for these purposes. However, the efficiency of these techniques depends not only on the approach itself, but also the numerical scheme that supports it. This work is aimed to demonstrate the advantages and disadvantages encountered during implementation of the UKF and EKF in the scheme of numerical solution of the Richards equation to obtain statements and soil parameters by assimilating surface moisture values. Numerical solutions evaluated were implemented using a finite difference scheme. The results demonstrate that a Crack -Nicolson linearized scheme is much more efficient in terms of security and time that based on an explicit scheme and safer than a UKF based on a traditional implicit numerical scheme for estimating profile soil moisture. The latter approach leads to a systematic bias in the solution 'unscented' when the central state is close to saturation. In the dual estimate (state- parameter), certain physical and mathematical parameter constraints, coupled with the bias in the estimates, resulted in substantial difficulties in the practical implementation of this technique using the UKF, or a solution that combines elements of both techniques Kalman filter

International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 3 Monthly

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Land Surface Temperature Databank contains monthly timescale mean, maximum, and minimum temperature for approximately 40,000 stations globally. It was...

International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 2 Daily

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 2 Monthly

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 1 Monthly

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 1 Daily

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

Science.gov (United States)

Borovikov, Anna; Rienecker, Michele M.; Keppenne, Christian; Johnson, Gregory C.

2004-01-01

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the

Acclimation to higher VPD and temperature minimized negative effects on assimilation and grain yield of wheat

DEFF Research Database (Denmark)

Rashid, Muhammad Adil; Andersen, Mathias Neumann; Wollenweber, Bernd

2018-01-01

Adapting to climate change and minimizing its negative impact on crop production requires detailed understanding of the direct and indirect effects of different climate variables (i.e. temperature, VPD). We investigated the direct (via heat stress) and indirect effects (through increased VPD....... Treatments included hot humid (HH: 36° C; 1.96 kPa VPD), hot dry (HD: 36° C; 3.92 kPa VPD) and normal (NC: 24° C; 1.49 kPa VPD). Difference between HH and HD was considered as the indirect effect of temperature through increased VPD. HD increased transpiration by 2–22% and decreased photosynthetic water......-use efficiency (WUEp) by 24–64% over HH during stress but whole-plant WUE at final harvest was not affected. HD reduced grainfilling duration (3 days), resulted in relatively lower green leaf area (GLA) after the stress and showed a tendency of lower net assimilation rate during the stress compared to HH...

Extreme Maximum Land Surface Temperatures.

Science.gov (United States)

Garratt, J. R.

1992-09-01

There are numerous reports in the literature of observations of land surface temperatures. Some of these, almost all made in situ, reveal maximum values in the 50°-70°C range, with a few, made in desert regions, near 80°C. Consideration of a simplified form of the surface energy balance equation, utilizing likely upper values of absorbed shortwave flux (1000 W m2) and screen air temperature (55°C), that surface temperatures in the vicinity of 90°-100°C may occur for dry, darkish soils of low thermal conductivity (0.1-0.2 W m1 K1). Numerical simulations confirm this and suggest that temperature gradients in the first few centimeters of soil may reach 0.5°-1°C mm1 under these extreme conditions. The study bears upon the intrinsic interest of identifying extreme maximum temperatures and yields interesting information regarding the comfort zone of animals (including man).

Midday depression of CO/sub 2/ assimilation in leaves of Arbutus unedo L. : diurnal changes in photosynthetic capacity related to changes in temperature and humidity

Energy Technology Data Exchange (ETDEWEB)

Raschke, K.; Resemann, A.

1986-01-01

Parts of the attached leaves of the sclerophyllous shrub Arbutus unedo were subjected to simulated mediterranean days. Gas exchange was recorded in order to recognize the causes of the midday depression in CO/sub 2/ assimilation. Depressions could be induced in part of a leaf: they were local responses. The CO/sub 2/-saturation curves of photosynthesis, determined during the morning and afternoon maxima of CO/sub 2/ assimilation and during the minimum at midday, established that depressions in CO/sub 2/ assimilation were in one-half of the investigated cases totally caused by reversible reductions in the photosynthetic capacity of the leaves, and in other half almost totally caused by such reductions. There was no correlation between the water loss with the degree of reduction of the photosynthetic capacity. However, depressions occurred if an apparent threshold in the water-vapor pressure difference between leaf and air was exceeded. In another set of experiments, leaves were subjected to variations in temperature and humidity independent of the time of the day, under otherwise constant conditions. Photosynthetic capacity and stomatal conductance proved to be almost insensitive to changes in temperature (in a range extending from 20 to 37/sup 0/C) as long as the water vapor-pressure difference was held constant. If it was not, the rate of photosynthesis began to decline with increasing temperature after a threshold water-vapor pressure difference was exceeded. The position of the resulting apparent temperature optimum of photosynthesis depended on the humidity of the air. The authors suggest that the ability of A. unedo to respond to a dry atmosphere with a reversible reduction of its photosynthetic capacity (by a still unknown mechanism) is the result of a co-evolution with the development of a strong stomatal sensitivity to changes in humidity. 26 references, 14 figures.

Augmenting an operational forecasting system for the North and Baltic Seas by in situ T and S data assimilation

Science.gov (United States)

Losa, Svetlana; Danilov, Sergey; Schröter, Jens; Nerger, Lars; Maßmann, Silvia; Janssen, Frank

2014-05-01

In order to improve the hydrography forecast of the North and Baltic Seas, the operational circulation model of the German Federal Maritime and Hydrographic Agency (BSH) has been augmented by a data assimilation (DA) system. The DA system has been developed based on the Singular Evolution Interpolated Kalman (SEIK) filter algorithm (Pham, 1998) coded within the Parallel Data Assimilation Framework (Nerger et al., 2004, Nerger and Hiller, 2012). Previously the only data assimilated were sea surface temperature (SST) measurements obtained with the Advanced Very High Resolution Radiometer (AVHRR) aboard NOAA's polar orbiting satellites. While the quality of the forecast has been significantly improved by assimilating the satellite data (Losa et al., 2012, Losa et al., 2014), assimilation of in situ observational temperature (T) and salinity (S) profiles has allowed for further improvement. Assimilating MARNET time series and CTD and Scanfish measurements, however, required a careful calibration of the DA system with respect to local analysis. The study addresses the problem of the local SEIK analysis accounting for the data within a certain radius. The localisation radius is considered spatially variable and dependent on the system local dynamics. As such, we define the radius of the data influence based on the energy ratio of the baroclinic and barotropic flows. D. T. Pham, J. Verron, L. Gourdeau, 1998. Singular evolutive Kalman filters for data assimilation in oceanography, C. R. Acad. Sci. Paris, Earth and Planetary Sciences, 326, 255-260. L. Nerger, W. Hiller, J. Schröter, 2004. PDAF - The Parallel Data Assimilation Framework: Experiences with Kalman Filtering, In: Zwieflhofer, W., Mozdzynski, G. (Eds.), Use of high performance computing in meteorology: proceedings of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology. Singapore: World Scientific, Reading, UK, 63-83. L. Nerger, W. Hiller, 2012. Software for Ensemble-based Data

A prototype data assimilation framework for generating spatiotemporally continuous SWOT data products

Science.gov (United States)

Andreadis, K.; Margulis, S. A.; Li, D.; Lettenmaier, D. P.

2017-12-01

The Surface Water and Ocean Topography (SWOT) satellite will provide critical surface water observations for the hydrologic community. However, production of key SWOT variables, such as river discharge and surface inundation, as well as lake, reservoir, and wetland storage change will be complicated by the discontinuity of the observations in space and time. A methodology that generates products with spatially and temporally continuous fields based on SWOT observables would be highly desirable. Data assimilation provides a mechanism for merging observations from SWOT with model predictions in order to produce estimates of quantities such as river discharge, storage change, and water heights for locations and times when there is no satellite overpass or other constraints (such as layover) render the measurement unusable. We describe here a prototype assimilation system with application to the Upper Mississippi basin, implemented using synthetic SWOT observations. We use a hydrologic model (VIC) coupled with a hydrodynamic model (LISFLOOD-FP) which generates "true" fields of surface water variables. The true fields are then used to generate synthetic SWOT observations using the SWOT Instrument Simulator. We also perform a "first-guess" (or open-loop) simulation with the coupled model using a configuration that contains errors representative of the imperfect knowledge of parameters and input data, including channel topography, bankfull widths and depths, and inflows, to create an ensemble of 20 model trajectories. Subsequently we assimilate the synthetic SWOT observations into the open-loop model results to estimate water surface elevation, discharge, and storage change. Our preliminary results using three data assimilation strategies show that all improve the water surface elevation estimate accuracy by 25% - 35% for a river reach of the upper Mississippi River. Ongoing work is examining whether the improved water surface elevation estimates propagate to improvements

Extended Reconstructed Sea Surface Temperature (ERSST)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global monthly sea surface temperature analysis derived from the International Comprehensive...

NOAA HRD's HEDAS Data Assimilation System's performance for the 2010 Atlantic Hurricane Season

Science.gov (United States)

Sellwood, K.; Aksoy, A.; Vukicevic, T.; Lorsolo, S.

2010-12-01

The Hurricane Ensemble Data Assimilation System (HEDAS) was developed at the Hurricane Research Division (HRD) of NOAA, in conjunction with an experimental version of the Hurricane Weather and Research Forecast model (HWRFx), in an effort to improve the initial representation of the hurricane vortex by utilizing high resolution in-situ data collected during NOAA’s Hurricane Field Program. HEDAS implements the “ensemble square root “ filter of Whitaker and Hamill (2002) using a 30 member ensemble obtained from NOAA/ESRL’s ensemble Kalman filter (EnKF) system and the assimilation is performed on a 3-km nest centered on the hurricane vortex. As part of NOAA’s Hurricane Forecast Improvement Program (HFIP), HEDAS will be run in a semi-operational mode for the first time during the 2010 Atlantic hurricane season and will assimilate airborne Doppler radar winds, dropwindsonde and flight level wind, temperature, pressure and relative humidity, and Stepped Frequency Microwave Radiometer surface wind observations as they become available. HEDAS has been implemented in an experimental mode for the cases of Hurricane Bill, 2009 and Paloma, 2008 to confirm functionality and determine the optimal configuration of the system. This test case demonstrates the importance of assimilating thermodynamic data in addition to wind observations and the benefit of increasing the quantity and distribution of observations. Applying HEDAS to a larger sample of storm forecasts would provide further insight into the behavior of the model when inner core aircraft observations are assimilated. The main focus of this talk will be to present a summary of HEDAS performance in the HWRFx model for the inaugural season. The HEDAS analyses and the resulting HWRFx forecasts will be compared with HWRFx analyses and forecasts produced concurrently using the HRD modeling group’s vortex initialization which does not employ data assimilation. The initial vortex and subsequent forecasts will be

Estimation of state and material properties during heat-curing molding of composite materials using data assimilation: A numerical study

Directory of Open Access Journals (Sweden)

Ryosuke Matsuzaki

2018-03-01

Full Text Available Accurate simulations of carbon fiber-reinforced plastic (CFRP molding are vital for the development of high-quality products. However, such simulations are challenging and previous attempts to improve the accuracy of simulations by incorporating the data acquired from mold monitoring have not been completely successful. Therefore, in the present study, we developed a method to accurately predict various CFRP thermoset molding characteristics based on data assimilation, a process that combines theoretical and experimental values. The degree of cure as well as temperature and thermal conductivity distributions during the molding process were estimated using both temperature data and numerical simulations. An initial numerical experiment demonstrated that the internal mold state could be determined solely from the surface temperature values. A subsequent numerical experiment to validate this method showed that estimations based on surface temperatures were highly accurate in the case of degree of cure and internal temperature, although predictions of thermal conductivity were more difficult. Keywords: Engineering, Materials science, Applied mathematics

Leaching of assimilable silicon species from fly ash

International Nuclear Information System (INIS)

Piekos, R.; Paslawska, S.

1998-01-01

The objective of this study was to investigate the leaching of assimilable silicon species from coal fly ash with distilled water, sea waterand synthetic sea water at various fly ash/water ratios, pHs and temperatures. At the 1 g/100 ml fly ash/water ratio, less than 1 mg Si was found in 11 of aqueous slurries over the pH range 4-8 after 2 h at ambient temperature. The leaching was most effective at pH 10.5. At the fly ash/waterratio indicated, the pH of the suspensions decreased from 10.4 to 8.4 after 5days. The pH of fly ash slurries in sea water varied only slightly over time as compared with that in distilled water. Generally, the leaching of assimilable silicon species with distilled water was more intense than that with the sea water. 27 refs., 6 figs., 3 tabs

NOAA Global Surface Temperature (NOAAGlobalTemp)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a merged land–ocean surface temperature analysis (formerly known as MLOST) (link is external). It is...

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

Science.gov (United States)

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

2018-01-01

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

Variability of emissivity and surface temperature over a sparsely vegetated surface

International Nuclear Information System (INIS)

Humes, K.S.; Kustas, W.P.; Moran, M.S.; Nichols, W.D.; Weltz, M.A.

1994-01-01

Radiometric surface temperatures obtained from remote sensing measurements are a function of both the physical surface temperature and the effective emissivity of the surface within the band pass of the radiometric measurement. For sparsely vegetated areas, however, a sensor views significant fractions of both bare soil and various vegetation types. In this case the radiometric response of a sensor is a function of the emissivities and kinetic temperatures of various surface elements, the proportion of those surface elements within the field of view of the sensor, and the interaction of radiation emitted from the various surface components. In order to effectively utilize thermal remote sensing data to quantify energy balance components for a sparsely vegetated area, it is important to examine the typical magnitude and degree of variability of emissivity and surface temperature for such surfaces. Surface emissivity measurements and ground and low-altitude-aircraft-based surface temperature measurements (8-13 micrometer band pass) made in conjunction with the Monsoon '90 field experiment were used to evaluate the typical variability of those quantities during the summer rainy season in a semiarid watershed. The average value for thermal band emissivity of the exposed bare soil portions of the surface was found to be approximately 0.96; the average value measured for most of the varieties of desert shrubs present was approximately 0.99. Surface composite emissivity was estimated to be approximately 0.98 for both the grass-dominated and shrub-dominated portions of the watershed. The spatial variability of surface temperature was found to be highly dependent on the spatial scale of integration for the instantaneous field of view (IFOV) of the instrument, the spatial scale of the total area under evaluation, and the time of day

High-temperature morphology of stepped gold surfaces

International Nuclear Information System (INIS)

Bilalbegovic, G.; Tosatti, E.; Ercolessi, F.

1992-04-01

Molecular dynamics simulations with a classical many-body potential are used to study the high-temperature stability of stepped non-melting metal surfaces. We have studied in particular the Au(111) vicinal surfaces in the (M+1, M-1, M) family and the Au(100) vicinals in the (M, 1, 1) family. Some vicinal orientations close to the non-melting Au(111) surface become unstable close to the bulk melting temperature and facet into a mixture of crystalline (111) regions and localized surface-melted regions. On the contrary, we do not find high-temperature faceting for vicinals close to Au(100), also a non-melting surface. These (100) vicinal surfaces gradually disorder with disappearance of individual steps well below the bulk melting temperature. We have also studied the high-temperature stability of ledges formed by pairs of monoatomic steps of opposite sign on the Au(111) surface. It is found that these ledges attract each other, so that several of them merge into one larger ledge, whose edge steps then act as a nucleation site for surface melting. (author). 43 refs, 8 figs

A Fault-Tolerant HPC Scheduler Extension for Large and Operational Ensemble Data Assimilation:Application to the Red Sea

KAUST Repository

Toye, Habib

2018-04-26

of the system with numerical experiments assimilating real satellites sea surface height and temperature observations in the Red Sea.

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

Science.gov (United States)

Jung, J.; Choi, Y.; Souri, A.; Jeon, W.

2017-12-01

Particle matter(PM) has played a significantly deleterious role in affecting human health and climate. Recently, continuous high concentrations of PM in Korea attracted public attention to this critical issue, and the Korea-United States Air Quality Study(KORUS-AQ) campaign in 2016 was conducted to investigate the causes. For this study, we adjusted the initial conditions in the chemical transport model(CTM) to improve its performance over Korean Peninsula during KORUS-AQ period, using the campaign data to evaluate our model performance. We used the Optimal Interpolation(OI) approach and used hourly surface air quality measurement data from the Air Quality Monitoring Station(AQMS) by NIER and the aerosol optical depth(AOD) measured by a GOCI sensor from the geostationary orbit onboard the Communication Ocean and Meteorological Satellite(COMS). The AOD at 550nm has a 6km spatial resolution and broad coverage over East Asia. After assimilating the surface air quality observation data, the model accuracy significantly improved compared to base model result (without assimilation). It reported very high correlation value (0.98) and considerably decreased mean bias. Especially, it well captured some high peaks which was underpredicted by the base model. To assimilate satellite data, we applied AOD scaling factors to quantify each specie's contribution to total PM concentration and find-mode fraction(FMF) to define vertical distribution. Finally, the improvement showed fairly good agreement.

Dioxin and phthalate uptake and assimilation by the green mussel Perna viridis

International Nuclear Information System (INIS)

Wang, Wen-Xiong; Zhang, Qiong

2013-01-01

In this study, the aqueous uptake and dietary assimilation (trophic transfer) of two endocrine disrupting compounds (dioxin and phathalic acid) in the green mussel Perna viridis were quantified. During short-term exposure period, dioxin rapidly sorbed onto phytoplankton and its accumulation was much higher than that of phthalate. The uptake of these two compounds by the mussels increased with increasing temperature and salinity (for dioxin only). The dietary assimilation of the two contaminants was rather modest (10–64% for dioxin and 20–47% for phthalate), and was greatly dependent on the food species and concentration. Interestingly, dietary assimilation increased with increasing diatom food concentration. Gut passage time was partially responsible for the variable dietary assimilation. Given the high dissolved uptake rate and the modest dietary assimilation, aqueous exposure was predicted to be the dominant bioaccumulation source for both dioxin and phthalate in the green mussels under most conditions. -- Capsule: Aqueous uptake was the predominant pathway for dioxin and phthalate accumulation in marine green mussels

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

Science.gov (United States)

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

2013-04-01

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

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

Science.gov (United States)

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

2014-01-01

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

Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

Science.gov (United States)

Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

2012-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

SEASONAL CHANGES IN TITAN'S SURFACE TEMPERATURES

International Nuclear Information System (INIS)

Jennings, D. E.; Cottini, V.; Nixon, C. A.; Flasar, F. M.; Kunde, V. G.; Samuelson, R. E.; Romani, P. N.; Hesman, B. E.; Carlson, R. C.; Gorius, N. J. P.; Coustenis, A.; Tokano, T.

2011-01-01

Seasonal changes in Titan's surface brightness temperatures have been observed by Cassini in the thermal infrared. The Composite Infrared Spectrometer measured surface radiances at 19 μm in two time periods: one in late northern winter (LNW; L s = 335 deg.) and another centered on northern spring equinox (NSE; L s = 0 deg.). In both periods we constructed pole-to-pole maps of zonally averaged brightness temperatures corrected for effects of the atmosphere. Between LNW and NSE a shift occurred in the temperature distribution, characterized by a warming of ∼0.5 K in the north and a cooling by about the same amount in the south. At equinox the polar surface temperatures were both near 91 K and the equator was at 93.4 K. We measured a seasonal lag of ΔL S ∼ 9 0 in the meridional surface temperature distribution, consistent with the post-equinox results of Voyager 1 as well as with predictions from general circulation modeling. A slightly elevated temperature is observed at 65 0 S in the relatively cloud-free zone between the mid-latitude and southern cloud regions.

COBE Sea Surface Temperature

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The COBE-SST is a gridded 1x1 resolution SST monitoring dataset. It is used as input for the JMA Climate Data Assimilation System (JCDAS) and the Japanese 25-year...

Data assimilation of depth-distributed satellite chlorophyll-α in two Mediterranean contrasting sites

KAUST Repository

Kalaroni, S.

2016-04-12

A new approach for processing the remote sensing chlorophyll-α (Chl-α) before assimilating into an ecosystem model is applied in two contrasting, regarding productivity and nutrients availability, Mediterranean sites: the DYFAMED and POSEIDON E1-M3A fixed point open ocean observatories. The new approach derives optically weighted depth-distributed Chl-α profiles from satellite data based on the model simulated Chl-α vertical distribution and light attenuation coefficient. We use the 1D version of the operational ecological 3D POSEIDON model, based on the European Regional Seas Ecosystem Model (ERSEM). The required hydrodynamic properties are obtained (off-line) from the POSEIDON operational 3D hydrodynamic Mediterranean basin scale model. The data assimilation scheme is the Singular Evolutive Interpolated Kalman (SEIK) filter, the ensemble variant of the Singular Evolutive Extended Kalman (SEEK) filter. The performance of the proposed assimilation approach was evaluated against the Chl-α satellite data and the seasonal averages of available in-situ data for nitrate, phosphate and Chl-α. An improvement of the model simulated near-surface and subsurface maximum Chl-α concentrations is obtained, especially at the DYFAMED site. Model nitrate is improved with assimilation, particularly with the new approach assimilating depth-distributed Chl-α, while model phosphate is slightly worse after assimilation. Additional sensitivity experiments were performed, showing a better performance of the new approach under different scenarios of model Chl-α deviation from pseudo-observations of surface Chl-α.

Data assimilation of depth-distributed satellite chlorophyll-α in two Mediterranean contrasting sites

KAUST Repository

Kalaroni, S.; Tsiaras, K.; Petihakis, G.; Hoteit, Ibrahim; Economou-Amilli, A.; G.Triantafyllou

2016-01-01

A new approach for processing the remote sensing chlorophyll-α (Chl-α) before assimilating into an ecosystem model is applied in two contrasting, regarding productivity and nutrients availability, Mediterranean sites: the DYFAMED and POSEIDON E1-M3A fixed point open ocean observatories. The new approach derives optically weighted depth-distributed Chl-α profiles from satellite data based on the model simulated Chl-α vertical distribution and light attenuation coefficient. We use the 1D version of the operational ecological 3D POSEIDON model, based on the European Regional Seas Ecosystem Model (ERSEM). The required hydrodynamic properties are obtained (off-line) from the POSEIDON operational 3D hydrodynamic Mediterranean basin scale model. The data assimilation scheme is the Singular Evolutive Interpolated Kalman (SEIK) filter, the ensemble variant of the Singular Evolutive Extended Kalman (SEEK) filter. The performance of the proposed assimilation approach was evaluated against the Chl-α satellite data and the seasonal averages of available in-situ data for nitrate, phosphate and Chl-α. An improvement of the model simulated near-surface and subsurface maximum Chl-α concentrations is obtained, especially at the DYFAMED site. Model nitrate is improved with assimilation, particularly with the new approach assimilating depth-distributed Chl-α, while model phosphate is slightly worse after assimilation. Additional sensitivity experiments were performed, showing a better performance of the new approach under different scenarios of model Chl-α deviation from pseudo-observations of surface Chl-α.

Marine ARM GPCI Investigation of Clouds Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) Field Campaign Report

Energy Technology Data Exchange (ETDEWEB)

Reynolds, R. Michael [Remote Measurements & Research Company, Seattle, WA (United States); Long, Charles N. [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab.

2016-01-10

Sea surface temperature (SST) is one of the most appropriate and important climate parameters: a widespread increase is an indicator of global warming and modifications of the geographical distribution of SST are an extremely sensitive indicator of climate change. There is high demand for accurate, reliable, high-spatial-and-temporal-resolution SST measurements for the parameterization of ocean-atmosphere heat, momentum, and gas (SST is therefore critical to understanding the processes controlling the global carbon dioxide budget) fluxes, for detailed diagnostic and process-orientated studies to better understand the behavior of the climate system, as model boundary conditions, for assimilation into climate models, and for the rigorous validation of climate model output. In order to achieve an overall net flux uncertainty < 10 W/m² (Bradley and Fairall, 2006), the sea surface (skin) temperature (SSST) must be measured to an error < 0.1 C and a precision of 0.05 C. Anyone experienced in shipboard meteorological measurements will recognize this is a tough specification. These demands require complete confidence in the content, interpretation, accuracy, reliability, and continuity of observational SST data—criteria that can only be fulfilled by the successful implementation of an ongoing data product validation strategy.

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

Science.gov (United States)

Jessup, A.; Holman, R. A.; Chickadel, C.; Elgar, S.; Farquharson, G.; Haller, M. C.; Kurapov, A. L.; Özkan-Haller, H. T.; Raubenheimer, B.; Thomson, J. M.

2012-12-01

the Field Research Facility at Duck, NC in September 2010 focused on assimilation of tower-based electo-optical, infrared, and radar measurements in predictions of longshore currents. Here we provide an overview of our contribution to the RIVET I experiment at New River Inlet, NC in May 2012. During the course of the 3-week measurement period, continuous tower-based remote sensing measurements were made using electro-optical, infrared, and radar techniques covering the nearshore zone and the inlet mouth. A total of 50 hours of airborne measurements were made using high-resolution infrared imagers and a customized along track interferometric synthetic aperture radar (ATI SAR). The airborne IR imagery provides kilometer-scale mapping of frontal features that evolve as the inlet flow interacts with the oceanic wave and current fields. The ATI SAR provides maps of the two-dimensional surface currents. Near-surface measurements of turbulent velocities and surface waves using SWIFT drifters, designed to measures near-surface properties relevant to remote sensing, complimented the extensive in situ measurements by RIVET investigators.

Data assimilation optimization for the evaluation of inverse mixing and convection flows

Energy Technology Data Exchange (ETDEWEB)

Rossmann, T [Department of Mechanical Engineering, Lafayette College, Easton, PA 18042 (United States); Knight, D D; Jaluria, Y, E-mail: rossmant@lafayette.edu [Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854 (United States)

2015-10-15

A data assimilation approach is developed for evaluation of fluid-thermal systems wherein a complete specification of the boundary conditions is not known a priori and experimental diagnostics are restricted to a limited region of the flowfield. The methodology is applied to the configuration of a heated jet injected into a laminar boundary layer where the jet temperature and velocity are unknowns. The closed loop method selects the initial locations for the experimental measurements of mean temperature within the flowfield using diode laser absorbance. Diode laser absorption measurements near 761 nm of molecular oxygen are recorded to characterize the time varying temperature of the two dimensional jet. The Response Surface Models built using two-dimensional unsteady Navier–Stokes simulations are used to predict the jet exit temperature and velocity, and select the second set of locations for the experimental measurements. The two sets of experimental data are used to generate the final prediction for the jet exit temperature and velocity. The jet exit velocity is correctly predicted to within the experimental uncertainty; however, the jet exit temperature is over-estimated by 9% to 23%. (paper)

Temperature dependence of surface nanobubbles

NARCIS (Netherlands)

Berkelaar, R.P.; Seddon, James Richard Thorley; Zandvliet, Henricus J.W.; Lohse, Detlef

2012-01-01

The temperature dependence of nanobubbles was investigated experimentally using atomic force microscopy. By scanning the same area of the surface at temperatures from 51 °C to 25 °C it was possible to track geometrical changes of individual nanobubbles as the temperature was decreased.

Outdoor surface temperature measurement: ground truth or lie?

Science.gov (United States)

Skauli, Torbjorn

2004-08-01

Contact surface temperature measurement in the field is essential in trials of thermal imaging systems and camouflage, as well as for scene modeling studies. The accuracy of such measurements is challenged by environmental factors such as sun and wind, which induce temperature gradients around a surface sensor and lead to incorrect temperature readings. In this work, a simple method is used to test temperature sensors under conditions representative of a surface whose temperature is determined by heat exchange with the environment. The tested sensors are different types of thermocouples and platinum thermistors typically used in field trials, as well as digital temperature sensors. The results illustrate that the actual measurement errors can be much larger than the specified accuracy of the sensors. The measurement error typically scales with the difference between surface temperature and ambient air temperature. Unless proper care is taken, systematic errors can easily reach 10% of this temperature difference, which is often unacceptable. Reasonably accurate readings are obtained using a miniature platinum thermistor. Thermocouples can perform well on bare metal surfaces if the connection to the surface is highly conductive. It is pointed out that digital temperature sensors have many advantages for field trials use.

Land Surface Temperature- Comparing Data from Polar Orbiting and Geostationary Satellites

Science.gov (United States)

Comyn-Platt, E.; Remedios, J. J.; Good, E. J.; Ghent, D.; Saunders, R.

2012-04-01

Land Surface Temperature (LST) is a vital parameter in Earth climate science, driving long-wave radiation exchanges that control the surface energy budget and carbon fluxes, which are important factors in Numerical Weather Prediction (NWP) and the monitoring of climate change. Satellites offer a convenient way to observe LST consistently and regularly over large areas. A comparison between LST retrieved from a Geostationary Instrument, the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), and a Polar Orbiting Instrument, the Advanced Along Track Scanning Radiometer (AATSR) is presented. Both sensors offer differing benefits. AATSR offers superior precision and spatial resolution with global coverage but given its sun-synchronous platform only observes at two local times, ~10am and ~10pm. SEVIRI provides the high-temporal resolution (every 15 minutes) required for observing diurnal variability of surface temperatures but given its geostationary platform has a poorer resolution, 3km at nadir, which declines at higher latitudes. A number of retrieval methods are applied to the raw satellite data: First order coefficient based algorithms provided on an operational basis by the LandSAF (for SEVIRI) and the University of Leicester (for AATSR); Second order coefficient based algorithms put forward by the University of Valencia; and an optimal estimation method using the 1DVar software provided by the NWP SAF. Optimal estimation is an iterative technique based upon inverse theory, thus is very useful for expanding into data assimilation systems. The retrievals are assessed and compared on both a fine scale using in-situ data from recognised validation sites and on a broad scale using two 100x100 regions such that biases can be better understood. Overall, the importance of LST lies in monitoring daily temperature extremes, e.g. for estimating permafrost thawing depth or risk of crop damage due to frost, hence the ideal dataset would use a combination of observations

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

Science.gov (United States)

Zhao, Lei; Lee, Xuhui; Liu, Shoudong

2013-09-01

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

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

Science.gov (United States)

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

2016-05-01

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

Low temperature self-cleaning properties of superhydrophobic surfaces

Science.gov (United States)

Wang, Fajun; Shen, Taohua; Li, Changquan; Li, Wen; Yan, Guilong

2014-10-01

Outdoor surfaces are usually dirty surfaces. Ice accretion on outdoor surfaces could lead to serious accidents. In the present work, the superhydrophobic surface based on 1H, 1H, 2H, 2H-Perfluorodecanethiol (PFDT) modified Ag/PDMS composite was prepared to investigate the anti-icing property and self-cleaning property at temperatures below freezing point. The superhydrophobic surface was deliberately polluted with activated carbon before testing. It was observed that water droplet picked up dusts on the cold superhydrophobic surface and took it away without freezing at a measuring temperature of -10 °C. While on a smooth PFDT surface and a rough surface base on Ag/PDMS composite without PFDT modification, water droplets accumulated and then froze quickly at the same temperature. However, at even lower temperature of -12 °C, the superhydrophobic surface could not prevent the surface water from icing. In addition, it was observed that the frost layer condensed from the moisture pay an important role in determining the low temperature self-cleaning properties of a superhydrophobic surface.

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

Science.gov (United States)

Minnis, P.; Smith, W., Jr.; Bedka, K. M.; Nguyen, L.; Palikonda, R.; Hong, G.; Trepte, Q.; Chee, T.; Scarino, B. R.; Spangenberg, D.; Sun-Mack, S.; Fleeger, C.; Ayers, J. K.; Chang, F. L.; Heck, P. W.

2014-12-01

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-real time globally from both geostationary (GEO) and low-earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

Science.gov (United States)

Minnis, Patrick; Smith, William L., Jr.; Bedka, Kristopher M.; Nguyen, Louis; Palikonda, Rabindra; Hong, Gang; Trepte, Qing Z.; Chee, Thad; Scarino, Benjamin; Spangenberg, Douglas A.;

Develop a Hybrid Coordinate Ocean Model with Data Assimilation Capabilities

National Research Council Canada - National Science Library

Thacker, W. C

2003-01-01

.... The objectives of the research are as follows: (1) to develop a methodology for assimilating temperature and salinity profiles from XBT, CTD, and ARGO float data that accommodates the peculiarities of HYCOM's hybrid vertical coordinates, allowing...

A Surface Temperature Initiated Closure (STIC) for surface energy balance fluxes

DEFF Research Database (Denmark)

Mallick, Kaniska; Jarvis, Andrew J.; Boegh, Eva

2014-01-01

The use of Penman–Monteith (PM) equation in thermal remote sensing based surface energy balance modeling is not prevalent due to the unavailability of any direct method to integrate thermal data into the PM equation and due to the lack of physical models expressing the surface (or stomatal......) and boundary layer conductances (gS and gB) as a function of surface temperature. Here we demonstrate a new method that physically integrates the radiometric surface temperature (TS) into the PM equation for estimating the terrestrial surface energy balance fluxes (sensible heat, H and latent heat, λ......E). The method combines satellite TS data with standard energy balance closure models in order to derive a hybrid closure that does not require the specification of surface to atmosphere conductance terms. We call this the Surface Temperature Initiated Closure (STIC), which is formed by the simultaneous solution...

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

National Research Council Canada - National Science Library

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

2005-01-01

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

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

Science.gov (United States)

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

2012-12-01

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

Estimation of land surface temperature of Kaduna metropolis ...

African Journals Online (AJOL)

Estimation of land surface temperature of Kaduna metropolis, Nigeria using landsat images. Isa Zaharaddeen, Ibrahim I. Baba, Ayuba Zachariah. Abstract. Understanding the spatial variation of Land Surface Temperature (LST), will be helpful in urban micro climate studies. This study estimates the land surface temperature ...

High-Temperature Surface-Acoustic-Wave Transducer

Science.gov (United States)

Zhao, Xiaoliang; Tittmann, Bernhard R.

2010-01-01

Aircraft-engine rotating equipment usually operates at high temperature and stress. Non-invasive inspection of microcracks in those components poses a challenge for the non-destructive evaluation community. A low-profile ultrasonic guided wave sensor can detect cracks in situ. The key feature of the sensor is that it should withstand high temperatures and excite strong surface wave energy to inspect surface/subsurface cracks. As far as the innovators know at the time of this reporting, there is no existing sensor that is mounted to the rotor disks for crack inspection; the most often used technology includes fluorescent penetrant inspection or eddy-current probes for disassembled part inspection. An efficient, high-temperature, low-profile surface acoustic wave transducer design has been identified and tested for nondestructive evaluation of structures or materials. The development is a Sol-Gel bismuth titanate-based surface-acoustic-wave (SAW) sensor that can generate efficient surface acoustic waves for crack inspection. The produced sensor is very thin (submillimeter), and can generate surface waves up to 540 C. Finite element analysis of the SAW transducer design was performed to predict the sensor behavior, and experimental studies confirmed the results. One major uniqueness of the Sol-Gel bismuth titanate SAW sensor is that it is easy to implement to structures of various shapes. With a spray coating process, the sensor can be applied to surfaces of large curvatures. Second, the sensor is very thin (as a coating) and has very minimal effect on airflow or rotating equipment imbalance. Third, it can withstand temperatures up to 530 C, which is very useful for engine applications where high temperature is an issue.

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

Science.gov (United States)

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

2012-12-01

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

GODAE, SFCOBS - Surface Temperature Observations, 1998-present

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — GODAE, SFCOBS - Surface Temperature Observations: Ship, fixed/drifting buoy, and CMAN in-situ surface temperature. Global Telecommunication System (GTS) Data. The...

Quantitative impact of aerosols on numerical weather prediction. Part II: Impacts to IR radiance assimilation

Science.gov (United States)

Marquis, J. W.; Campbell, J. R.; Oyola, M. I.; Ruston, B. C.; Zhang, J.

2017-12-01

This is part II of a two-part series examining the impacts of aerosol particles on weather forecasts. In this study, the aerosol indirect effects on weather forecasts are explored by examining the temperature and moisture analysis associated with assimilating dust contaminated hyperspectral infrared radiances. The dust induced temperature and moisture biases are quantified for different aerosol vertical distribution and loading scenarios. The overall impacts of dust contamination on temperature and moisture forecasts are quantified over the west coast of Africa, with the assistance of aerosol retrievals from AERONET, MPL, and CALIOP. At last, methods for improving hyperspectral infrared data assimilation in dust contaminated regions are proposed.

ALMA observation of Ceres' Surface Temperature.

Science.gov (United States)

Titus, T. N.; Li, J. Y.; Sykes, M. V.; Ip, W. H.; Lai, I.; Moullet, A.

2016-12-01

Ceres, the largest object in the main asteroid belt, has been mapped by the Dawn spacecraft. The mapping includes measuring surface temperatures using the Visible and Infrared (VIR) spectrometer at high spatial resolution. However, the VIR instrument has a long wavelength cutoff at 5 μm, which prevents the accurate measurement of surface temperatures below 180 K. This restricts temperature determinations to low and mid-latitudes at mid-day. Observations from the Atacama Large Millimeter/submillimeter Array (ALMA) [1], while having lower spatial resolution, are sensitive to the full range of surface temperatures that are expected at Ceres. Forty reconstructed images at 75 km/beam resolution were acquired of Ceres that were consistent with a low thermal inertia surface. The diurnal temperature profiles were compared to the KRC thermal model [2, 3], which has been extensively used for Mars [e.g. 4, 5]. Variations in temperature as a function of local time are observed and are compared to predictions from the KRC model. The model temperatures are converted to radiance (Jy/Steradian) and are corrected for near-surface thermal gradients and limb effects for comparison to observations. Initial analysis is consistent with the presence of near-surface water ice in the north polar region. The edge of the ice table is between 50° and 70° North Latitude, consistent with the enhanced detection of hydrogen by the Dawn GRaND instrument [6]. Further analysis will be presented. This work is supported by the NASA Solar System Observations Program. References: [1] Wootten A. et al. (2015) IAU General Assembly, Meeting #29, #2237199 [2] Kieffer, H. H., et al. (1977) JGR, 82, 4249-4291. [3] Kieffer, Hugh H., (2013) Journal of Geophysical Research: Planets, 118(3), 451-470. [4] Titus, T. N., H. H. Kieffer, and P. N. Christensen (2003) Science, 299, 1048-1051. [5] Fergason, R. L. et al. (2012) Space Sci. Rev, 170, 739-773[6] Prettyman, T. et al. (2016) LPSC 47, #2228.

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

Science.gov (United States)

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

2017-12-01

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

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

Science.gov (United States)

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

2016-12-01

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

Nitrogen uptake and assimilation by corn roots

International Nuclear Information System (INIS)

Yoneyama, Tadakatsu; Akiyama, Yoko; Kumazawa, Kikuo

1977-01-01

The site of nitrogen uptake in the apical root zone of corn was experimentally investigated. Two experiments were performed. The one is to see the assimilation of nitrate and ammonium and the effects of low temperature on it. The 4-day-old roots were treated with 15 N-labelled inorganic nitrogen of 20 ppm N in 5 x 10 -4 M CaSO 4 solution at 30 deg. C and 0 deg. C. The other is to see the nitrogen uptake at apical root zone and the utilization of newly absorbed nitrogen at the root top. The 4-day-old roots were transferred into 5 x 10 -4 M CaSO 4 solution containing 15 N-labelled ammonium nitrate of 40 ppm N. As a result, the effect of low temperature on the nitrogen uptake appeared to be more drastic in the case of nitrate than ammonium. The 15 N content of amino acids indicates that ammonium is assimilated into amino acids even at 0 deg. C, but nitrate is not. The ammonium nitrogen seemed to be absorbed at both cell dividing and elongating zones. On the other hand, nitrate nitrogen seemed to be strongly absorbed at cell elongating zone. The nitrogen in the apical part may be supplied not only by direct absorption but also by translocation from the basal part. The clear difference was found in the utilization of nitrate and ammonium nitrogen at the root top when the root was elongating. This may be due to the difference of assimilation products of inorganic nitrogen. Newly absorbed ammonium nitrogen is more utilizable for the growth of root top than nitrate nitrogen. (Iwakiri, K.)

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

Science.gov (United States)

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

2017-12-01

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

Low temperature surface chemistry and nanostructures

Science.gov (United States)

Sergeev, G. B.; Shabatina, T. I.

2002-03-01

The new scientific field of low temperature surface chemistry, which combines the low temperature chemistry (cryochemistry) and surface chemistry approaches, is reviewed in this paper. One of the most exciting achievements in this field of science is the development of methods to create highly ordered hybrid nanosized structures on different organic and inorganic surfaces and to encapsulate nanosized metal particles in organic and polymer matrices. We consider physical and chemical behaviour for the systems obtained by co-condensation of the components vapours on the surfaces cooled down to 4-10 and 70-100 K. In particular the size effect of both types, the number of atoms in the reactive species structure and the thickness of growing co-condensate film, on the chemical activity of the system is analysed in detail. The effect of the internal mechanical stresses on the growing interfacial co-condensate film formation and on the generation of fast (explosive) spontaneous reactions at low temperatures is discussed. The examples of unusual chemical interactions of metal atoms, clusters and nanosized particles, obtained in co-condensate films on the cooled surfaces under different conditions, are presented. The examples of highly ordered surface and volume hybrid nanostructures formation are analysed.

An Ensemble Recentering Kalman Filter with an Application to Argo Temperature Data Assimilation into the NASA GEOS-5 Coupled Model

Science.gov (United States)

Keppenne, Christian L.

2013-01-01

A two-step ensemble recentering Kalman filter (ERKF) analysis scheme is introduced. The algorithm consists of a recentering step followed by an ensemble Kalman filter (EnKF) analysis step. The recentering step is formulated such as to adjust the prior distribution of an ensemble of model states so that the deviations of individual samples from the sample mean are unchanged but the original sample mean is shifted to the prior position of the most likely particle, where the likelihood of each particle is measured in terms of closeness to a chosen subset of the observations. The computational cost of the ERKF is essentially the same as that of a same size EnKF. The ERKF is applied to the assimilation of Argo temperature profiles into the OGCM component of an ensemble of NASA GEOS-5 coupled models. Unassimilated Argo salt data are used for validation. A surprisingly small number (16) of model trajectories is sufficient to significantly improve model estimates of salinity over estimates from an ensemble run without assimilation. The two-step algorithm also performs better than the EnKF although its performance is degraded in poorly observed regions.

Eye surface temperature detects stress response in budgerigars (Melopsittacus undulatus).

Science.gov (United States)

Ikkatai, Yuko; Watanabe, Shigeru

2015-08-05

Previous studies have suggested that stressors not only increase body core temperature but also body surface temperature in many animals. However, it remains unclear whether surface temperature could be used as an alternative to directly measure body core temperature, particularly in birds. We investigated whether surface temperature is perceived as a stress response in budgerigars. Budgerigars have been used as popular animal models to investigate various neural mechanisms such as visual perception, vocal learning, and imitation. Developing a new technique to understand the basic physiological mechanism would help neuroscience researchers. First, we found that cloacal temperature correlated with eye surface temperature. Second, eye surface temperature increased after handling stress. Our findings suggest that eye surface temperature is closely related to cloacal temperature and that the stress response can be measured by eye surface temperature in budgerigars.

Performance analysis of PV panel under varying surface temperature

Directory of Open Access Journals (Sweden)

Kumar Tripathi Abhishek

2018-01-01

Full Text Available The surface temperature of PV panel has an adverse impact on its performance. The several electrical parameters of PV panel, such as open circuit voltage, short circuit current, power output and fill factor depends on the surface temperature of PV panel. In the present study, an experimental work was carried out to investigate the influence of PV panel surface temperature on its electrical parameters. The results obtained from this experimental study show a significant reduction in the performance of PV panel with an increase in panel surface temperature. A 5W PV panel experienced a 0.4% decrease in open circuit voltage for every 1°C increase in panel surface temperature. Similarly, there was 0.6% and 0.32% decrease in maximum power output and in fill factor, respectively, for every 1°C increase in panel surface temperature. On the other hand, the short circuit current increases with the increase in surface temperature at the rate of 0.09%/°C.

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

Science.gov (United States)

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

2016-04-01

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

Assessment of broiler surface temperature variation when exposed to different air temperatures

Directory of Open Access Journals (Sweden)

GR Nascimento

2011-12-01

Full Text Available This study was conducted to determine the effect of the air temperature variation on the mean surface temperature (MST of 7- to 35-day-old broiler chickens using infrared thermometry to estimate MST, and to study surface temperature variation of the wings, head, legs, back and comb as affected by air temperature and broiler age. One hundred Cobb® broilers were used in the experiment. Starting on day 7, 10 birds were weekly selected at random, housed in an environmental chamber and reared under three distinct temperatures (18, 25 and 32 ºC to record their thermal profile using an infrared thermal camera. The recorded images were processed to estimate MST by selecting the whole area of the bird within the picture and comparing it with the values obtained using selected equations in literature, and to record the surface temperatures of the body parts. The MST estimated by infrared images were not statistically different (p > 0.05 from the values obtained by the equations. MST values significantly increased (p < 0.05 when the air temperature increased, but were not affected by bird age. However, age influenced the difference between MST and air temperature, which was highest on day 14. The technique of infrared thermal image analysis was useful to estimate the mean surface temperature of broiler chickens.

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

Directory of Open Access Journals (Sweden)

S. Skachko

2008-12-01

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

Response of an eddy-permitting ocean model to the assimilation of sparse in situ data

Science.gov (United States)

Li, Jian-Guo; Killworth, Peter D.; Smeed, David A.

2003-04-01

The response of an eddy-permitting ocean model to changes introduced by data assimilation is studied when the available in situ data are sparse in both space and time (typical for the majority of the ocean). Temperature and salinity (T&S) profiles from the WOCE upper ocean thermal data set were assimilated into a primitive equation ocean model over the North Atlantic, using a simple nudging scheme with a time window of about 2 days and a horizontal spatial radius of about 1°. When data are sparse the model returns to its unassimilated behavior, locally "forgetting" or rejecting the assimilation, on timescales determined by the local advection and diffusion. Increasing the spatial weighting radius effectively reduces both processes and hence lengthens the model restoring time (and with it, the impact of assimilation). Increasing the nudging factor enhances the assimilation effect but has little effect on the model restoring time.

Temperature sensitive surfaces and methods of making same

Science.gov (United States)

Liang, Liang [Richland, WA; Rieke, Peter C [Pasco, WA; Alford, Kentin L [Pasco, WA

2002-09-10

Poly-n-isopropylacrylamide surface coatings demonstrate the useful property of being able to switch charateristics depending upon temperature. More specifically, these coatings switch from being hydrophilic at low temperature to hydrophobic at high temperature. Research has been conducted for many years to better characterize and control the properties of temperature sensitive coatings. The present invention provides novel temperature sensitive coatings on articles and novel methods of making temperature sensitive coatings that are disposed on the surfaces of various articles. These novel coatings contain the reaction products of n-isopropylacrylamide and are characterized by their properties such as advancing contact angles. Numerous other characteristics such as coating thickness, surface roughness, and hydrophilic-to-hydrophobic transition temperatures are also described. The present invention includes articles having temperature-sensitve coatings with improved properties as well as improved methods for forming temperature sensitive coatings.

A virtual climate library of surface temperature over North America for 1979-2015

Science.gov (United States)

Kravtsov, Sergey; Roebber, Paul; Brazauskas, Vytaras

2017-10-01

The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979-2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life.

Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

Science.gov (United States)

Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

2011-01-01

This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

Directory of Open Access Journals (Sweden)

Ronan Fablet

2018-02-01

Full Text Available The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST. We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale.

Variational data assimilation for the optimized ozone initial state and the short-time forecasting

Directory of Open Access Journals (Sweden)

S.-Y. Park

2016-03-01

Full Text Available In this study, we apply the four-dimensional variational (4D-Var data assimilation to optimize initial ozone state and to improve the predictability of air quality. The numerical modeling systems used for simulations of atmospheric condition and chemical formation are the Weather Research and Forecasting (WRF model and the Community Multiscale Air Quality (CMAQ model. The study area covers the capital region of South Korea, where the surface measurement sites are relatively evenly distributed. The 4D-Var code previously developed for the CMAQ model is modified to consider background error in matrix form, and various numerical tests are conducted. The results are evaluated with an idealized covariance function for the appropriateness of the modified codes. The background error is then constructed using the NMC method with long-term modeling results, and the characteristics of the spatial correlation scale related to local circulation are analyzed. The background error is applied in the 4D-Var research, and a surface observational assimilation is conducted to optimize the initial concentration of ozone. The statistical results for the 12 h assimilation periods and the 120 observatory sites show a 49.4 % decrease in the root mean squared error (RMSE, and a 59.9 % increase in the index of agreement (IOA. The temporal variation of spatial distribution of the analysis increments indicates that the optimized initial state of ozone concentration is transported to inland areas by the clockwise-rotating local circulation during the assimilation windows. To investigate the predictability of ozone concentration after the assimilation window, a short-time forecasting is carried out. The ratios of the RMSE (root mean squared error with assimilation versus that without assimilation are 8 and 13 % for the +24 and +12 h, respectively. Such a significant improvement in the forecast accuracy is obtained solely by using the optimized initial state. The potential

Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

NARCIS (Netherlands)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Liu, Qing; Ardizzone, Joseph V.; Colliander, Andreas; Conaty, Austin; Crow, Wade; Jackson, Thomas J.; Jones, Lucas A.; Kimball, John S.; Koster, Randal D.; Mahanama, Sarith P.; Smith, Edmond B.; Berg, Aaron; Bircher, Simone; Bosch, David; Caldwell, Todd G.; Cosh, Michael; Holifield Collins, Chandra D.; Jensen, Karsten H.; Livingston, Stan; Lopez-baeza, Ernesto; Martínez-fernández, José; Mcnairn, Heather; Moghaddam, Mahta; Pacheco, Anna; Pellarin, Thierry; Prueger, John; Rowlandson, Tracy; Seyfried, Mark; Starks, Patrick; Su, Bob; Thibeault, Marc; Van Der Velde, Rogier; Walker, Jeffrey; Wu, Xiaoling; Zeng, Yijian

2017-01-01

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present

NCAR High-resolution Land Data Assimilation System and Its Recent Applications

Science.gov (United States)

Chen, F.; Manning, K.; Barlage, M.; Gochis, D.; Tewari, M.

2008-05-01

A High-Resolution Land Data Assimilation System (HRLDAS) has been developed at NCAR to meet the need for high-resolution initial conditions of land state (soil moisture and temperature) by today's numerical weather prediction models coupled to a land surface model such as the WRF/Noah coupled modeling system. Intended for conterminous US application, HRLDAS uses observed hourly 4-km national precipitation analysis and satellite-derived surface-solar-downward radiation to drive, in uncoupled mode, the Noah land surface model to simulate long-term evolution of soil state. The advantage of HRLDAS is its use of 1-km resolution land-use and soil texture maps and 4-km rainfall data. As a result, it is able to capture fine-scale heterogeneity at the surface and in the soil. The ultimate goal of HRLDAS development is to characterize soil moisture/temperature and vegetation variability at small scales (~4km) over large areas to provide improved initial land and vegetation conditions for the WRF/Noah coupled model. Hence, HRLDAS is configured after the WRF/Noah coupled model configuration to ensure the consistency in model resolution, physical configuration (e.g., terrain height), soil model, and parameters between the uncoupled soil initialization system and its coupled forecast counterpart. We will discuss various characteristics of HRLDAS, including its spin-up and sensitivity to errors in forcing data. We will describe recent enhancement in terms of hydrological modeling and the use of remote sensing data. We will discuss recent applications of HRLDAS for flood forecast, agriculture, and arctic land system.

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

Science.gov (United States)

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

2017-12-01

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

Completing the Feedback Loop: The Impact of Chlorophyll Data Assimilation on the Ocean State

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Borovikov, Anna; Keppenne, Christian; Kovach, Robin

2015-01-01

In anticipation of the integration of a full biochemical model into the next generation GMAO coupled system, an intermediate solution has been implemented to estimate the penetration depth (1Kd_PAR) of ocean radiation based on the chlorophyll concentration. The chlorophyll is modeled as a tracer with sources-sinks coming from the assimilation of MODIS chlorophyll data. Two experiments were conducted with the coupled ocean-atmosphere model. In the first, climatological values of Kpar were used. In the second, retrieved daily chlorophyll concentrations were assimilated and Kd_PAR was derived according to Morel et al (2007). No other data was assimilated to isolate the effects of the time-evolving chlorophyll field. The daily MODIS Kd_PAR product was used to validate the skill of the penetration depth estimation and the MERRA-OCEAN re-analysis was used as a benchmark to study the sensitivity of the upper ocean heat content and vertical temperature distribution to the chlorophyll input. In the experiment with daily chlorophyll data assimilation, the penetration depth was estimated more accurately, especially in the tropics. As a result, the temperature bias of the model was reduced. A notably robust albeit small (2-5 percent) improvement was found across the equatorial Pacific ocean, which is a critical region for seasonal to inter-annual prediction.

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

Science.gov (United States)

Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

2011-01-01

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Long-term changes in sea surface temperatures

International Nuclear Information System (INIS)

Parker, D.E.

1994-01-01

Historical observations of sea surface temperature since 1856 have been improved by applying corrections to compensate for the predominant use of uninsulated or partly insulated buckets until the Second World War. There are large gaps in coverage in the late nineteenth century and around the two world wars, but a range of statistical techniques suggest that these gaps do not severely prejudice estimates of global and regional climatic change. Nonetheless, to improve the analysis on smaller scales, many unused historical data are to be digitized and incorporated. For recent years, satellite-based sea surface temperatures have improved the coverage, after adjustments for their biases relative to in situ data. An initial version of a nominally globally complete sea ice and interpolated sea surface temperature data set, beginning in 1871, has been created for use in numerical simulations of recent climate. Long time series of corrected regional, hemispheric, and global sea surface temperatures are mostly consistent with corresponding night marine air temperature series, and confirm the regionally specific climatic changes portrayed in the Scientific Assessments of the intergovernmental Panel on Climate Change. The observations also show an El Nino-like oscillation on bidecadal and longer time scales

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

Science.gov (United States)

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

2013-12-01

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

Improved Seasonal Prediction of European Summer Temperatures With New Five-Layer Soil-Hydrology Scheme

Science.gov (United States)

Bunzel, Felix; Müller, Wolfgang A.; Dobrynin, Mikhail; Fröhlich, Kristina; Hagemann, Stefan; Pohlmann, Holger; Stacke, Tobias; Baehr, Johanna

2018-01-01

We evaluate the impact of a new five-layer soil-hydrology scheme on seasonal hindcast skill of 2 m temperatures over Europe obtained with the Max Planck Institute Earth System Model (MPI-ESM). Assimilation experiments from 1981 to 2010 and 10-member seasonal hindcasts initialized on 1 May each year are performed with MPI-ESM in two soil configurations, one using a bucket scheme and one a new five-layer soil-hydrology scheme. We find the seasonal hindcast skill for European summer temperatures to improve with the five-layer scheme compared to the bucket scheme and investigate possible causes for these improvements. First, improved indirect soil moisture assimilation allows for enhanced soil moisture-temperature feedbacks in the hindcasts. Additionally, this leads to improved prediction of anomalies in the 500 hPa geopotential height surface, reflecting more realistic atmospheric circulation patterns over Europe.

NOAA Global Surface Temperature Dataset, Version 4.0

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is derived from two independent analyses: the Extended Reconstructed Sea Surface Temperature (ERSST)...

Development of a three-dimensional variational data assimilation system for the Seto Island Sea, Japan

Science.gov (United States)

Kurosawa, K.; Uchiyama, Y.

2016-12-01

By optimally combined ocean models with observation data, numerical oceanic reanalysis and forecast systems allow us to predict the ocean more precisely. In general, data assimilation is exploited to prepare the initial condition for the forecast. This technique has widely been employed in atmospheric prediction, whereas oceanic prediction lags behind weather forecast. Accurate oceanic prediction systems have been demanded for operational purposes such as for fisheries, vessel navigation, marine construction, offshore platform management, marine monitoring, etc. In particular, in crowded harbors and estuaries including the Seto Inland Sea (SIS), Japan, data assimilation has seldom been adapted because data from satellites and Argo floats essential to successful oceanic predictions is desperately limited. In addition, although static data assimilation, typically three-dimensional variational data assimilation (3DVAR), is computationally cheap and statistically optimal, but is not physically balanced. For instance, 3DVAR is known to modify velocity and density fields merely mathematically, yet it does not adequately consider quasi-geostrophic balance, which is generally true in most cases. In the present study, we develop a 3DVAR system for Regional Oceanic Modeling Systems (ROMS) and apply to the high-resolution SIS model in a double nested configuration (Kosako et al., 2015). The SIS is the largest estuary in Japan with a number of autonomous in-situ monitoring of vertical profiles of temperature and salinity, tens of tidal gages, along with continuous surface current measurement using HF radars. We first present a theoretical framework of the 3DVAR algorithm by considering geostrophic and thermal-wind balance to find plausible relationships among physical variables to avoid undesirable modifications. Subsequently, the developed 3DVAR is coupled with the SIS ROMS model to compare the model outcomes against some observation data. The 3DVAR ROMS model for the SIS

Assimilation of the ESA CCI Soil Moisture ACTIVE and PASSIVE Product into the SURFEX Land Surface Model using the Ensemble Transform Kalman Filter

Science.gov (United States)

Blyverket, J.; Hamer, P.; Bertino, L.; Lahoz, W. A.

2017-12-01

The European Space Agency Climate Change Initiative for soil moisture (ESA CCI SM) was initiated in 2012 for a period of six years, the objective for this period was to produce the most complete and consistent global soil moisture data record based on both active and passive sensors. The ESA CCI SM products consist of three surface soil moisture datasets: The ACTIVE product and the PASSIVE product were created by fusing scatterometer and radiometer soil moisture data, respectively. The COMBINED product is a blended product based on the former two datasets. In this study we assimilate globally both the ACTIVE and PASSIVE product at a 25 km spatial resolution. The different satellite platforms have different overpass times, an observation is mapped to the hours 00.00, 06.00, 12.00 or 18.00 if it falls within a 3 hour window centred at these times. We use the SURFEX land surface model with the ISBA diffusion scheme for the soil hydrology. For the assimilation routine we apply the Ensemble Transform Kalman Filter (ETKF). The land surface model is driven by perturbed MERRA-2 atmospheric forcing data, which has a temporal resolution of one hour and is mapped to the SURFEX model grid. Bias between the land surface model and the ESA CCI product is removed by cumulative distribution function (CDF) matching. This work is a step towards creating a global root zone soil moisture product from the most comprehensive satellite surface soil moisture product available. As a first step we consider the period from 2010 - 2016. This allows for comparison against other global root zone soil moisture products (SMAP Level 4, which is independent of the ESA CCI SM product).

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

Science.gov (United States)

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

2008-12-01

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

Data assimilation and PWR primary measurement

International Nuclear Information System (INIS)

Mercier, Thibaud

2015-01-01

A Pressurized Water Reactor (PWR) Reactor Coolant System (RCS) is a highly complex physical process: heterogeneous power, flow and temperature distributions are difficult to be accurately measured, since instrumentations are limited in number, thus leading to the relevant safety and protection margins. EDF R and D is seeking to assess the potential benefits of applying Data Assimilation to a PWR's RCS (Reactor Coolant System) measurements, in order to improve the estimators for parameters of a reactor's operating setpoint, i.e. improving accuracy and reducing uncertainties and biases of measured RCS parameters. In this thesis, we define a 0D semi-empirical model for RCS, satisfying the description level usually chosen by plant operators, and construct a Monte-Carlo Method (inspired from Ensemble Methods) in order to use this model with Data Assimilation tools. We apply this method on simulated data in order to assess the reduction of uncertainties on key parameters: results are beyond expectations, however strong hypothesis are required, implying a careful preprocessing of input data. (author)

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

Science.gov (United States)

Vira, J.; Sofiev, M.

2015-02-01

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

Temperature Dependence of Arn+ Cluster Backscattering from Polymer Surfaces: a New Method to Determine the Surface Glass Transition Temperature.

Science.gov (United States)

Poleunis, Claude; Cristaudo, Vanina; Delcorte, Arnaud

2018-01-01

In this work, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to study the intensity variations of the backscattered Ar n + clusters as a function of temperature for several amorphous polymer surfaces (polyolefins, polystyrene, and polymethyl methacrylate). For all these investigated polymers, our results show a transition of the ratio Ar 2 + /(Ar 2 + + Ar 3 + ) when the temperature is scanned from -120 °C to +125 °C (the exact limits depend on the studied polymer). This transition generally spans over a few tens of degrees and the temperature of the inflection point of each curve is always lower than the bulk glass transition temperature (T g ) reported for the considered polymer. Due to the surface sensitivity of the cluster backscattering process (several nanometers), the presented analysis could provide a new method to specifically evaluate a surface transition temperature of polymers, with the same lateral resolution as the gas cluster beam. Graphical abstract ᅟ.

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

Science.gov (United States)

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

2017-12-01

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

Multiproxy summer and winter surface air temperature field reconstructions for southern South America covering the past centuries

Energy Technology Data Exchange (ETDEWEB)

Neukom, R.; Grosjean, M.; Wanner, H. [University of Bern, Oeschger Centre for Climate Change Research (OCCR), Bern (Switzerland); University of Bern, Institute of Geography, Climatology and Meteorology, Bern (Switzerland); Luterbacher, J. [Justus Liebig University of Giessen, Department of Geography, Climatology, Climate Dynamics and Climate Change, Giessen (Germany); Villalba, R.; Morales, M.; Srur, A. [CONICET, Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales (IANIGLA), Mendoza (Argentina); Kuettel, M. [University of Bern, Oeschger Centre for Climate Change Research (OCCR), Bern (Switzerland); University of Bern, Institute of Geography, Climatology and Meteorology, Bern (Switzerland); University of Washington, Department of Earth and Space Sciences, Seattle (United States); Frank, D. [Swiss Federal Research Institute WSL, Birmensdorf (Switzerland); Jones, P.D. [University of East Anglia, Climatic Research Unit, School of Environmental Sciences, Norwich (United Kingdom); Aravena, J.-C. [Centro de Estudios Cuaternarios de Fuego Patagonia y Antartica (CEQUA), Punta Arenas (Chile); Black, D.E. [Stony Brook University, School of Marine and Atmospheric Sciences, Stony Brook (United States); Christie, D.A.; Urrutia, R. [Universidad Austral de Chile Valdivia, Laboratorio de Dendrocronologia, Facultad de Ciencias Forestales y Recursos Naturales, Valdivia (Chile); D' Arrigo, R. [Earth Institute at Columbia University, Tree-Ring Laboratory, Lamont-Doherty Earth Observatory, Palisades, NY (United States); Lara, A. [Universidad Austral de Chile Valdivia, Laboratorio de Dendrocronologia, Facultad de Ciencias Forestales y Recursos Naturales, Valdivia (Chile); Nucleo Cientifico Milenio FORECOS, Fundacion FORECOS, Valdivia (Chile); Soliz-Gamboa, C. [Utrecht Univ., Inst. of Environmental Biology, Utrecht (Netherlands); Gunten, L. von [Univ. of Bern (Switzerland); Univ. of Massachusetts, Climate System Research Center, Amherst (United States)

2011-07-15

We statistically reconstruct austral summer (winter) surface air temperature fields back to ad 900 (1706) using 22 (20) annually resolved predictors from natural and human archives from southern South America (SSA). This represents the first regional-scale climate field reconstruction for parts of the Southern Hemisphere at this high temporal resolution. We apply three different reconstruction techniques: multivariate principal component regression, composite plus scaling, and regularized expectation maximization. There is generally good agreement between the results of the three methods on interannual and decadal timescales. The field reconstructions allow us to describe differences and similarities in the temperature evolution of different sub-regions of SSA. The reconstructed SSA mean summer temperatures between 900 and 1350 are mostly above the 1901-1995 climatology. After 1350, we reconstruct a sharp transition to colder conditions, which last until approximately 1700. The summers in the eighteenth century are relatively warm with a subsequent cold relapse peaking around 1850. In the twentieth century, summer temperatures reach conditions similar to earlier warm periods. The winter temperatures in the eighteenth and nineteenth centuries were mostly below the twentieth century average. The uncertainties of our reconstructions are generally largest in the eastern lowlands of SSA, where the coverage with proxy data is poorest. Verifications with independent summer temperature proxies and instrumental measurements suggest that the interannual and multi-decadal variations of SSA temperatures are well captured by our reconstructions. This new dataset can be used for data/model comparison and data assimilation as well as for detection and attribution studies at sub-continental scales. (orig.)

An Improved GRACE Terrestrial Water Storage Assimilation System For Estimating Large-Scale Soil Moisture and Shallow Groundwater

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Girotto, M.; De Lannoy, G. J. M.; Reichle, R. H.; Rodell, M.

2015-12-01

The Gravity Recovery And Climate Experiment (GRACE) mission is unique because it provides highly accurate column integrated estimates of terrestrial water storage (TWS) variations. Major limitations of GRACE-based TWS observations are related to their monthly temporal and coarse spatial resolution (around 330 km at the equator), and to the vertical integration of the water storage components. These challenges can be addressed through data assimilation. To date, it is still not obvious how best to assimilate GRACE-TWS observations into a land surface model, in order to improve hydrological variables, and many details have yet to be worked out. This presentation discusses specific recent features of the assimilation of gridded GRACE-TWS data into the NASA Goddard Earth Observing System (GEOS-5) Catchment land surface model to improve soil moisture and shallow groundwater estimates at the continental scale. The major recent advancements introduced by the presented work with respect to earlier systems include: 1) the assimilation of gridded GRACE-TWS data product with scaling factors that are specifically derived for data assimilation purposes only; 2) the assimilation is performed through a 3D assimilation scheme, in which reasonable spatial and temporal error standard deviations and correlations are exploited; 3) the analysis step uses an optimized calculation and application of the analysis increments; 4) a poor-man's adaptive estimation of a spatially variable measurement error. This work shows that even if they are characterized by a coarse spatial and temporal resolution, the observed column integrated GRACE-TWS data have potential for improving our understanding of soil moisture and shallow groundwater variations.

The 15th century Arctic warming in coupled model simulations with data assimilation

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E. Crespin

2009-07-01

Full Text Available An ensemble of simulations of the climate of the past millennium conducted with a three-dimensional climate model of intermediate complexity are constrained to follow temperature histories obtained from a recent compilation of well-calibrated surface temperature proxies using a simple data assimilation technique. Those simulations provide a reconstruction of the climate of the Arctic that is compatible with the model physics, the forcing applied and the proxy records. Available observational data, proxy-based reconstructions and our model results suggest that the Arctic climate is characterized by substantial variations in surface temperature over the past millennium. Though the most recent decades are likely to be the warmest of the past millennium, we find evidence for substantial past warming episodes in the Arctic. In particular, our model reconstructions show a prominent warm event during the period 1470–1520. This warm period is likely related to the internal variability of the climate system, that is the variability present in the absence of any change in external forcing. We examine the roles of competing mechanisms that could potentially produce this anomaly. This study leads us to conclude that changes in atmospheric circulation, through enhanced southwesterly winds towards northern Europe, Siberia and Canada, are likely the main cause of the late 15th/early 16th century Arctic warming.

Extended Reconstructed Sea Surface Temperature (ERSST), Version 4

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National Oceanic and Atmospheric Administration, Department of Commerce — The Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global monthly sea surface temperature analysis on a 2x2 degree grid derived from the...

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

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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.

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

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

2016-01-01

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

The midday depression of CO2 assimilation in leaves of Arbutus unedo L.: diurnal changes in photosynthetic capacity related to changes in temperature and humidity.

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Raschke, K; Resemann, A

1986-09-01

Parts of attached leaves of the sclerophyllous shrub Arbutus unedo were subjected to simulated mediterranean days. Gas exchange was recorded in order to recognize the causes of the midday depression in CO2 assimilation. Depressions could be induced in part of a leaf: they were local responses. The CO2-saturation curves of photosynthesis, determined during the morning and afternoon maxima of CO2 assimilation and during the minimum at midday, established that depressions in CO2 assimilation were in one-half of the investigated cases totally caused by reversible reductions in the photosynthetic capacity of the leaves, and in the other half almost totally caused by such reductions. An analysis of 37 daily courses showed that morning reductions and afternoon recoveries of stomatal conductance and rate of photosynthesis occurred simultaneously and in proportion to each other, with the result that the partial pressure of CO2 in the intercellular spaces remained more or less constant. Midday depressions occurred also in detached leaves standing in water. The initiation of a midday depression was not caused by a circadian rhythm, nor was high quantum flux or high temperature a requirement. There was no correlation between the rate of water loss from the leaves, or the amount of water lost, with the degree of reduction of the photosynthetic capacity. However, depressions occurred if an apparent threshold in the water-vapor pressure difference between leaf and air was exceeded. This critical value varied between about 20 and 30 mbar, depending on the leaf investigated. The dominating role of humidity in the induction of the midday depression was further demonstrated when leaf temperature was held constant and the vapor-pressure difference was made to follow the pattern of the mediterranean day: depressions occurred. Depressions however were hardly noticeable when the water-vapor pressure difference was held constant and leaf temperature was allowed to vary. In another set of

Surface Temperature Data Analysis

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Hansen, James; Ruedy, Reto

2012-01-01

Small global mean temperature changes may have significant to disastrous consequences for the Earth's climate if they persist for an extended period. Obtaining global means from local weather reports is hampered by the uneven spatial distribution of the reliably reporting weather stations. Methods had to be developed that minimize as far as possible the impact of that situation. This software is a method of combining temperature data of individual stations to obtain a global mean trend, overcoming/estimating the uncertainty introduced by the spatial and temporal gaps in the available data. Useful estimates were obtained by the introduction of a special grid, subdividing the Earth's surface into 8,000 equal-area boxes, using the existing data to create virtual stations at the center of each of these boxes, and combining temperature anomalies (after assessing the radius of high correlation) rather than temperatures.

Modelling global fresh surface water temperature

NARCIS (Netherlands)

Beek, L.P.H. van; Eikelboom, T.; Vliet, M.T.H. van; Bierkens, M.F.P.

2011-01-01

Temperature directly determines a range of water physical properties including vapour pressure, surface tension, density and viscosity, and the solubility of oxygen and other gases. Indirectly water temperature acts as a strong control on fresh water biogeochemistry, influencing sediment

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

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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

A virtual climate library of surface temperature over North America for 1979–2015

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Kravtsov, Sergey; Roebber, Paul; Brazauskas, Vytaras

2017-01-01

The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979–2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life. PMID:29039842

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

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

Radiance Assimilation Shows Promise for Snowpack Characterization: A 1-D Case Study

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Durand, Michael; Kim, Edward; Margulis, Steve

2008-01-01

We demonstrate an ensemble-based radiometric data assimilation (DA) methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) observations at 18.7 and 36.5 GHz collected during the NASA CLPX-1, March 2003, Colorado, USA. A land surface model was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-DA-based retrieval algorithm using the same PM data had a bias of -18.3 cm. The sensitivity of the assimilation scheme to the grain size uncertainty was evaluated; over the range of grain size uncertainty tested, the posterior snow depth estimate bias ranges from -2.99 cm to -9.85 cm, which is uniformly better than both the prior and retrieval estimates. This study demonstrates the potential applicability of radiometric DA at larger scales.

High-frequency fluctuations of surface temperatures in an urban environment

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Christen, Andreas; Meier, Fred; Scherer, Dieter

2012-04-01

This study presents an attempt to resolve fluctuations in surface temperatures at scales of a few seconds to several minutes using time-sequential thermography (TST) from a ground-based platform. A scheme is presented to decompose a TST dataset into fluctuating, high-frequency, and long-term mean parts. To demonstrate the scheme's application, a set of four TST runs (day/night, leaves-on/leaves-off) recorded from a 125-m-high platform above a complex urban environment in Berlin, Germany is used. Fluctuations in surface temperatures of different urban facets are measured and related to surface properties (material and form) and possible error sources. A number of relationships were found: (1) Surfaces with surface temperatures that were significantly different from air temperature experienced the highest fluctuations. (2) With increasing surface temperature above (below) air temperature, surface temperature fluctuations experienced a stronger negative (positive) skewness. (3) Surface materials with lower thermal admittance (lawns, leaves) showed higher fluctuations than surfaces with high thermal admittance (walls, roads). (4) Surface temperatures of emerged leaves fluctuate more compared to trees in a leaves-off situation. (5) In many cases, observed fluctuations were coherent across several neighboring pixels. The evidence from (1) to (5) suggests that atmospheric turbulence is a significant contributor to fluctuations. The study underlines the potential of using high-frequency thermal remote sensing in energy balance and turbulence studies at complex land-atmosphere interfaces.

The EUSTACE project: delivering global, daily information on surface air temperature

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Ghent, D.; Rayner, N. A.

2017-12-01

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, in the EUSTACE project (2015-2018, https://www.eustaceproject.eu) we have developed an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals is used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. This includes developing new "Big Data" analysis methods as the data volumes involved are considerable. We will present recent progress along this road in the EUSTACE project, i.e.: • identifying inhomogeneities in daily surface air temperature measurement series from weather stations and correcting for these over Europe; • estimating surface air temperature over all surfaces of Earth from surface skin temperature retrievals; • using new statistical techniques to provide information on higher spatial and temporal scales than currently available, making optimum use of information in data-rich eras. Information will also be given on how interested users can become involved.

Do Aphids Alter Leaf Surface Temperature Patterns During Early Infestation?

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Thomas Cahon

2018-03-01

Full Text Available Arthropods at the surface of plants live in particular microclimatic conditions that can differ from atmospheric conditions. The temperature of plant leaves can deviate from air temperature, and leaf temperature influences the eco-physiology of small insects. The activity of insects feeding on leaf tissues, may, however, induce changes in leaf surface temperatures, but this effect was only rarely demonstrated. Using thermography analysis of leaf surfaces under controlled environmental conditions, we quantified the impact of presence of apple green aphids on the temperature distribution of apple leaves during early infestation. Aphids induced a slight change in leaf surface temperature patterns after only three days of infestation, mostly due to the effect of aphids on the maximal temperature that can be found at the leaf surface. Aphids may induce stomatal closure, leading to a lower transpiration rate. This effect was local since aphids modified the configuration of the temperature distribution over leaf surfaces. Aphids were positioned at temperatures near the maximal leaf surface temperatures, thus potentially experiencing the thermal changes. The feedback effect of feeding activity by insects on their host plant can be important and should be quantified to better predict the response of phytophagous insects to environmental changes.

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

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Y. Tang

2017-12-01

Full Text Available This study applies the Gridpoint Statistical Interpolation (GSI 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP, to improve surface PM2.5 predictions over the contiguous United States (CONUS by assimilating aerosol optical depth (AOD and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ modeling system. An optimal interpolation (OI method implemented earlier (Tang et al., 2015 for the CMAQ modeling system is also tested for the same period (July 2011 over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter < 2.5 µm leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE. It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.

Multisource data assimilation in a Richards equation-based integrated hydrological model: a real-world application to an experimental hillslope

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Camporese, M.; Botto, A.

2017-12-01

Data assimilation is becoming increasingly popular in hydrological and earth system modeling, as it allows for direct integration of multisource observation data in modeling predictions and uncertainty reduction. For this reason, data assimilation has been recently the focus of much attention also for integrated surface-subsurface hydrological models, whereby multiple terrestrial compartments (e.g., snow cover, surface water, groundwater) are solved simultaneously, in an attempt to tackle environmental problems in a holistic approach. Recent examples include the joint assimilation of water table, soil moisture, and river discharge measurements in catchment models of coupled surface-subsurface flow using the ensemble Kalman filter (EnKF). Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant. Here, the model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. We assimilate pressure head, soil moisture, and subsurface outflow with EnKF in a number of assimilation scenarios and discuss the challenges, issues, and tradeoffs arising from the assimilation of multisource data in a real-world test case, with particular focus on the capability of DA to update the subsurface parameters.

Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

Science.gov (United States)

Drusch, M.

2007-02-01

Satellite-derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analyzed from the modeled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. For this study, three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been performed for the 2-month period of June and July 2002: a control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating TMI (TRMM Microwave Imager) derived soil moisture over the southern United States. In this experimental run the satellite-derived soil moisture product is introduced through a nudging scheme using 6-hourly increments. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analyzed in the nudging experiment is the most accurate estimate when compared against in situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.

Recent trends in sea surface temperature off Mexico

NARCIS (Netherlands)

Lluch-Cota, S.E.; Tripp-Valdéz, M.; Lluch-Cota, D.B.; Lluch-Belda, D.; Verbesselt, J.; Herrera-Cervantes, H.; Bautista-Romero, J.

2013-01-01

Changes in global mean sea surface temperature may have potential negative implications for natural and socioeconomic systems; however, measurements to predict trends in different regions have been limited and sometimes contradictory. In this study, an assessment of sea surface temperature change

Data Assimilation for Applied Meteorology

Science.gov (United States)

Haupt, S. E.

2012-12-01

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

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

Directory of Open Access Journals (Sweden)

A. F. Arellano Jr.

2007-11-01

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

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

Science.gov (United States)

Capecchi, V.; Gozzini, B.

2012-04-01

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

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

Science.gov (United States)

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

2000-01-01

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

Temperature-dependent surface density of alkylthiol monolayers on gold nanocrystals

Science.gov (United States)

Liu, Xuepeng; Lu, Pin; Zhai, Hua; Wu, Yucheng

2018-03-01

Atomistic molecular dynamics (MD) simulations are performed to study the surface density of passivating monolayers of alkylthiol chains on gold nanocrystals at temperatures ranging from 1 to 800 K. The results show that the surface density of alkylthiol monolayer reaches a maximum value at near room temperature (200-300 K), while significantly decreases with increasing temperature in the higher temperature region (> 300 {{K}}), and slightly decreases with decreasing temperature at low temperature (< 200 {{K}}). We find that the temperature dependence of surface ligand density in the higher temperature region is attributed to the substantial ligand desorption induced by the thermal fluctuation, while that at low temperature results from the reduction in entropy caused by the change in the ordering of passivating monolayer. These results are expected helpful to understand the temperature-dependent surface coverage of gold nanocrystals.

A physically based model of global freshwater surface temperature

Science.gov (United States)

van Beek, Ludovicus P. H.; Eikelboom, Tessa; van Vliet, Michelle T. H.; Bierkens, Marc F. P.

2012-09-01

Temperature determines a range of physical properties of water and exerts a strong control on surface water biogeochemistry. Thus, in freshwater ecosystems the thermal regime directly affects the geographical distribution of aquatic species through their growth and metabolism and indirectly through their tolerance to parasites and diseases. Models used to predict surface water temperature range between physically based deterministic models and statistical approaches. Here we present the initial results of a physically based deterministic model of global freshwater surface temperature. The model adds a surface water energy balance to river discharge modeled by the global hydrological model PCR-GLOBWB. In addition to advection of energy from direct precipitation, runoff, and lateral exchange along the drainage network, energy is exchanged between the water body and the atmosphere by shortwave and longwave radiation and sensible and latent heat fluxes. Also included are ice formation and its effect on heat storage and river hydraulics. We use the coupled surface water and energy balance model to simulate global freshwater surface temperature at daily time steps with a spatial resolution of 0.5° on a regular grid for the period 1976-2000. We opt to parameterize the model with globally available data and apply it without calibration in order to preserve its physical basis with the outlook of evaluating the effects of atmospheric warming on freshwater surface temperature. We validate our simulation results with daily temperature data from rivers and lakes (U.S. Geological Survey (USGS), limited to the USA) and compare mean monthly temperatures with those recorded in the Global Environment Monitoring System (GEMS) data set. Results show that the model is able to capture the mean monthly surface temperature for the majority of the GEMS stations, while the interannual variability as derived from the USGS and NOAA data was captured reasonably well. Results are poorest for

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

Science.gov (United States)

Tang, Youhua; Pagowski, Mariusz; Chai, Tianfeng; Pan, Li; Lee, Pius; Baker, Barry; Kumar, Rajesh; Delle Monache, Luca; Tong, Daniel; Kim, Hyun-Cheol

2017-12-01

This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.

Methodological Developments in Geophysical Assimilation Modeling

Science.gov (United States)

Christakos, George

2005-06-01

This work presents recent methodological developments in geophysical assimilation research. We revisit the meaning of the term "solution" of a mathematical model representing a geophysical system, and we examine its operational formulations. We argue that an assimilation solution based on epistemic cognition (which assumes that the model describes incomplete knowledge about nature and focuses on conceptual mechanisms of scientific thinking) could lead to more realistic representations of the geophysical situation than a conventional ontologic assimilation solution (which assumes that the model describes nature as is and focuses on form manipulations). Conceptually, the two approaches are fundamentally different. Unlike the reasoning structure of conventional assimilation modeling that is based mainly on ad hoc technical schemes, the epistemic cognition approach is based on teleologic criteria and stochastic adaptation principles. In this way some key ideas are introduced that could open new areas of geophysical assimilation to detailed understanding in an integrated manner. A knowledge synthesis framework can provide the rational means for assimilating a variety of knowledge bases (general and site specific) that are relevant to the geophysical system of interest. Epistemic cognition-based assimilation techniques can produce a realistic representation of the geophysical system, provide a rigorous assessment of the uncertainty sources, and generate informative predictions across space-time. The mathematics of epistemic assimilation involves a powerful and versatile spatiotemporal random field theory that imposes no restriction on the shape of the probability distributions or the form of the predictors (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated) and accounts rigorously for the uncertainty features of the geophysical system. In the epistemic cognition context the assimilation concept may be used to

Impact of satellite data assimilation on the predictability of monsoon intraseasonal oscillations in a regional model

KAUST Repository

Parekh, Anant; Attada, Raju; Chowdary, J. S.; Gnanaseelan, C.

2017-01-01

) and in the second, apart from NCEP forcing, AIRS temperature and moisture profiles are assimilated (ASSIM). Ten active and break cases are identified from each simulation. Three dimensional temperature states of identified active and break cases are perturbed using

Data assimilation in hydrological modelling

DEFF Research Database (Denmark)

Drecourt, Jean-Philippe

Data assimilation is an invaluable tool in hydrological modelling as it allows to efficiently combine scarce data with a numerical model to obtain improved model predictions. In addition, data assimilation also provides an uncertainty analysis of the predictions made by the hydrological model....... In this thesis, the Kalman filter is used for data assimilation with a focus on groundwater modelling. However the developed techniques are general and can be applied also in other modelling domains. Modelling involves conceptualization of the processes of Nature. Data assimilation provides a way to deal...... with model non-linearities and biased errors. A literature review analyzes the most popular techniques and their application in hydrological modelling. Since bias is an important problem in groundwater modelling, two bias aware Kalman filters have been implemented and compared using an artificial test case...

Satellite-based Calibration of Heat Flux at the Ocean Surface

Science.gov (United States)

Barron, C. N.; Dastugue, J. M.; May, J. C.; Rowley, C. D.; Smith, S. R.; Spence, P. L.; Gremes-Cordero, S.

2016-02-01

Model forecasts of upper ocean heat content and variability on diurnal to daily scales are highly dependent on estimates of heat flux through the air-sea interface. Satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. Traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle. Subsequent evolution depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. The COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates) endeavors to correct ocean forecast bias through a responsive error partition among surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using Navy operational global or regional atmospheric forcing. COFFEE addresses satellite-calibration of surface fluxes to estimate surface error covariances and links these to the ocean interior. Experiment cases combine different levels of flux calibration with different assimilation alternatives. The cases may use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is equally applicable to other regions. These approaches within a 3DVAR application are anticipated to be useful for global and larger

Data assimilation strategies for volcano geodesy

Science.gov (United States)

Zhan, Yan; Gregg, Patricia M.

2017-09-01

Ground deformation observed using near-real time geodetic methods, such as InSAR and GPS, can provide critical information about the evolution of a magma chamber prior to volcanic eruption. Rapid advancement in numerical modeling capabilities has resulted in a number of finite element models targeted at better understanding the connection between surface uplift associated with magma chamber pressurization and the potential for volcanic eruption. Robust model-data fusion techniques are necessary to take full advantage of the numerical models and the volcano monitoring observations currently available. In this study, we develop a 3D data assimilation framework using the Ensemble Kalman Filter (EnKF) approach in order to combine geodetic observations of surface deformation with geodynamic models to investigate volcanic unrest. The EnKF sequential assimilation method utilizes disparate data sets as they become available to update geodynamic models of magma reservoir evolution. While the EnKF has been widely applied in hydrologic and climate modeling, the adaptation for volcano monitoring is in its initial stages. As such, our investigation focuses on conducting a series of sensitivity tests to optimize the EnKF for volcano applications and on developing specific strategies for assimilation of geodetic data. Our numerical experiments illustrate that the EnKF is able to adapt well to the spatial limitations posed by GPS data and the temporal limitations of InSAR, and that specific strategies can be adopted to enhance EnKF performance to improve model forecasts. Specifically, our numerical experiments indicate that: (1) incorporating additional iterations of the EnKF analysis step is more efficient than increasing the number of ensemble members; (2) the accuracy of the EnKF results are not affected by initial parameter assumptions; (3) GPS observations near the center of uplift improve the quality of model forecasts; (4) occasionally shifting continuous GPS stations to

Ensemble Assimilation Using Three First-Principles Thermospheric Models as a Tool for 72-hour Density and Satellite Drag Forecasts

Science.gov (United States)

Hunton, D.; Pilinski, M.; Crowley, G.; Azeem, I.; Fuller-Rowell, T. J.; Matsuo, T.; Fedrizzi, M.; Solomon, S. C.; Qian, L.; Thayer, J. P.; Codrescu, M.

2014-12-01

Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by variability in the density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. As the population of satellites in Earth orbit grows, higher space-weather prediction accuracy is required for critical missions, such as accurate catalog maintenance, collision avoidance for manned and unmanned space flight, reentry prediction, satellite lifetime prediction, defining on-board fuel requirements, and satellite attitude dynamics. We describe ongoing work to build a comprehensive nowcast and forecast system for neutral density, winds, temperature, composition, and satellite drag. This modeling tool will be called the Atmospheric Density Assimilation Model (ADAM). It will be based on three state-of-the-art coupled models of the thermosphere-ionosphere running in real-time, using assimilative techniques to produce a thermospheric nowcast. It will also produce, in realtime, 72-hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition. We will review the requirements for the ADAM system, the underlying full-physics models, the plethora of input options available to drive the models, a feasibility study showing the performance of first-principles models as it pertains to satellite-drag operational needs, and review challenges in designing an assimilative space-weather prediction model. The performance of the ensemble assimilative model is expected to exceed the performance of current empirical and assimilative density models.

Diode temperature sensor array for measuring and controlling micro scale surface temperature

International Nuclear Information System (INIS)

Han, Il Young; Kim, Sung Jin

2004-01-01

The needs of micro scale thermal detecting technique are increasing in biology and chemical industry. For example, thermal finger print, Micro PCR(Polymer Chain Reaction), TAS and so on. To satisfy these needs, we developed a DTSA(Diode Temperature Sensor Array) for detecting and controlling the temperature on small surface. The DTSA is fabricated by using VLSI technique. It consists of 32 array of diodes(1,024 diodes) for temperature detection and 8 heaters for temperature control on a 8mm surface area. The working principle of temperature detection is that the forward voltage drop across a silicon diode is approximately proportional to the inverse of the absolute temperature of diode. And eight heaters (1K) made of poly-silicon are added onto a silicon wafer and controlled individually to maintain a uniform temperature distribution across the DTSA. Flip chip packaging used for easy connection of the DTSA. The circuitry for scanning and controlling DTSA are also developed

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

Science.gov (United States)

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

1999-11-01

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

Comparison of Sequential and Variational Data Assimilation

Science.gov (United States)

Alvarado Montero, Rodolfo; Schwanenberg, Dirk; Weerts, Albrecht

2017-04-01

Data assimilation is a valuable tool to improve model state estimates by combining measured observations with model simulations. It has recently gained significant attention due to its potential in using remote sensing products to improve operational hydrological forecasts and for reanalysis purposes. This has been supported by the application of sequential techniques such as the Ensemble Kalman Filter which require no additional features within the modeling process, i.e. it can use arbitrary black-box models. Alternatively, variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function describes the trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables. While sequential techniques have been commonly applied to hydrological processes, variational techniques are seldom used. In our believe, this is mainly attributed to the required computation of first order sensitivities by algorithmic differentiation techniques and related model enhancements, but also to lack of comparison between both techniques. We contribute to filling this gap and present the results from the assimilation of streamflow data in two basins located in Germany and Canada. The assimilation introduces noise to precipitation and temperature to produce better initial estimates of an HBV model. The results are computed for a hindcast period and assessed using lead time performance metrics. The study concludes with a discussion of the main features of each technique and their advantages/disadvantages in hydrological applications.

Temperature distribution and heat radiation of patterned surfaces at short wavelengths

Science.gov (United States)

Emig, Thorsten

2017-05-01

We analyze the equilibrium spatial distribution of surface temperatures of patterned surfaces. The surface is exposed to a constant external heat flux and has a fixed internal temperature that is coupled to the outside heat fluxes by finite heat conductivity across the surface. It is assumed that the temperatures are sufficiently high so that the thermal wavelength (a few microns at room temperature) is short compared to all geometric length scales of the surface patterns. Hence the radiosity method can be employed. A recursive multiple scattering method is developed that enables rapid convergence to equilibrium temperatures. While the temperature distributions show distinct dependence on the detailed surface shapes (cuboids and cylinder are studied), we demonstrate robust universal relations between the mean and the standard deviation of the temperature distributions and quantities that characterize overall geometric features of the surface shape.

GRACE-Assimilated Drought Indicators for the U.S. Drought Monitor

Science.gov (United States)

Rui, Hualan; Vollmer, Bruce; Teng, Bill; Loeser, Carlee; Beaudoing, Hiroko; Rodell, Matt

2018-01-01

The Gravity Recovery and Climate Experiment (GRACE) mission detects changes in Earth's gravity field by precisely monitoring the changes in distance between two satellites orbiting the Earth in tandem. Scientists at NASA's Goddard Space Flight Center generate GRACE-assimilated groundwater and soil moisture drought indicators each week, for drought monitor-related studies and applications. The GRACE-assimilated Drought Indicator Version 2.0 data product (GRACE-DA-DM V2.0) is archived at, and distributed by, the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center). More information about the data and data access is available on the data product landing page at https://disc.gsfc.nasa.gov/datasets /GRACEDADM_CLSM0125US_7D_2.0/summary. The GRACE-DA-DM V2.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. The drought indicators are of wet or dry conditions, expressed as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2012. These GRACE-assimilated drought indicators, with improved spatial and temporal resolutions, should provide a more comprehensive and objective identification of drought conditions. This presentation describes the basic characteristics of the data and data services at NASA GES DISC and collaborative organizations, and uses a few examples to demonstrate the simple ways to explore the GRACE-assimilated drought indicator data.

Data Assimilation in Integrated and Distributed Hydrological Models

DEFF Research Database (Denmark)

Zhang, Donghua

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

Data Assimilation in Marine Models

DEFF Research Database (Denmark)

Frydendall, Jan

maximum likelihood framework. These issues are discussed in paper B. The third part of the thesis falls a bit out of the above context is work published in papers C, F. In the first paper, a simple data assimilation scheme was investigated to examine the potential benefits of incorporating a data......This thesis consists of six research papers published or submitted for publication in the period 2006-2009 together with a summary report. The main topics of this thesis are nonlinear data assimilation techniques and estimation in dynamical models. The focus has been on the nonlinear filtering...... techniques for large scale geophysical numerical models and making them feasible to work with in the data assimilation framework. The filtering techniques investigated are all Monte Carlo simulation based. Some very nice features that can be exploited in the Monte Carlo based data assimilation framework from...

Surface temperature retrieval in a temperate grassland with multiresolution sensors

Science.gov (United States)

Goetz, S. J.; Halthore, R. N.; Hall, F. G.; Markham, B. L.

1995-12-01

Radiometric surface temperatures retrieved at various spatial resolutions from aircraft and satellite measurements at the FIFE site in eastern Kansas were compared with near-surface temperature measurements to determine the accuracy of the retrieval techniques and consistency between the various sensors. Atmospheric characterizations based on local radiosonde profiles of temperature, pressure, and water vapor were used with the LOWTRAN-7 and MODTRAN atmospheric radiance models to correct measured thermal radiances of water and grassland targets for atmospheric attenuation. Comparison of retrieved surface temperatures from a helicopter-mounted modular multispectral radiometer (MMR) (˜5-m "pixel"), C-130 mounted thematic mapper simulator (TMS) (NS001, ˜20-m pixel), and the Landsat 5 thematic mapper (TM) (120-m pixel) was done. Differences between atmospherically corrected radiative temperatures and near-surface measurements ranged from less than 1°C to more than 8°C. Corrected temperatures from helicopter-MMR and NS001-TMS were in general agreement with near-surface infrared radiative thermometer (IRT) measurements collected from automated meteorological stations, with mean differences of 3.2°C and 1.7°C for grassland targets. Much better agreement (within 1°C) was found between the retrieved aircraft surface temperatures and near-surface measurements acquired with a hand-held mast equipped with a MMR and IRT. The NS001-TMS was also in good agreement with near-surface temperatures acquired over water targets. In contrast, the Landsat 5 TM systematically overestimated surface temperature in all cases. This result has been noted previously but not consistently. On the basis of the results reported here, surface measurements were used to provide a calibration of the TM thermal channel. Further evaluation of the in-flight radiometric calibration of the TM thermal channel is recommended.

ISLSCP II Sea Surface Temperature

Data.gov (United States)

National Aeronautics and Space Administration — Sea surface temperature (SST) is an important indicator of the state of the earth climate system as well as a key variable in the coupling between the atmosphere and...

Merged Land and Ocean Surface Temperature, Version 3.5

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The historical Merged Land-Ocean Surface Temperature Analysis (MLOST) is derived from two independent analyses, an Extended Reconstructed Sea Surface Temperature...

NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global monthly sea surface temperature dataset derived from the International...

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

Science.gov (United States)

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

2015-10-01

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

Assimilation of ZDR Columns for Improving the Spin-Up and Forecasts of Convective Storms

Science.gov (United States)

Carlin, J.; Gao, J.; Snyder, J.; Ryzhkov, A.

2017-12-01

A primary motivation for assimilating radar reflectivity data is the reduction of spin-up time for modeled convection. To accomplish this, cloud analysis techniques seek to induce and sustain convective updrafts in storm-scale models by inserting temperature and moisture increments and hydrometeor mixing ratios into the model analysis from simple relations with reflectivity. Polarimetric radar data provide additional insight into the microphysical and dynamic structure of convection. In particular, the radar meteorology community has known for decades that convective updrafts cause, and are typically co-located with, differential reflectivity (ZDR) columns - vertical protrusions of enhanced ZDR above the environmental 0˚C level. Despite these benefits, limited work has been done thus far to assimilate dual-polarization radar data into numerical weather prediction models. In this study, we explore the utility of assimilating ZDR columns to improve storm-scale model analyses and forecasts of convection. We modify the existing Advanced Regional Prediction System's (ARPS) cloud analysis routine to adjust model temperature and moisture state variables using detected ZDR columns as proxies for convective updrafts, and compare the resultant cycled analyses and forecasts with those from the original reflectivity-based cloud analysis formulation. Results indicate qualitative and quantitative improvements from assimilating ZDR columns, including more coherent analyzed updrafts, forecast updraft helicity swaths that better match radar-derived rotation tracks, more realistic forecast reflectivity fields, and larger equitable threat scores. These findings support the use of dual-polarization radar signatures to improve storm-scale model analyses and forecasts.

Temperature-mediated transition from Dyakonov-Tamm surface waves to surface-plasmon-polariton waves

Science.gov (United States)

Chiadini, Francesco; Fiumara, Vincenzo; Mackay, Tom G.; Scaglione, Antonio; Lakhtakia, Akhlesh

2017-08-01

The effect of changing the temperature on the propagation of electromagnetic surface waves (ESWs), guided by the planar interface of a homogeneous isotropic temperature-sensitive material (namely, InSb) and a temperature-insensitive structurally chiral material (SCM) was numerically investigated in the terahertz frequency regime. As the temperature rises, InSb transforms from a dissipative dielectric material to a dissipative plasmonic material. Correspondingly, the ESWs transmute from Dyakonov-Tamm surface waves into surface-plasmon-polariton waves. The effects of the temperature change are clearly observed in the phase speeds, propagation distances, angular existence domains, multiplicity, and spatial profiles of energy flow of the ESWs. Remarkably large propagation distances can be achieved; in such instances the energy of an ESW is confined almost entirely within the SCM. For certain propagation directions, simultaneous excitation of two ESWs with (i) the same phase speeds but different propagation distances or (ii) the same propagation distances but different phase speeds are also indicated by our results.

Recent Development on the NOAA's Global Surface Temperature Dataset

Science.gov (United States)

Zhang, H. M.; Huang, B.; Boyer, T.; Lawrimore, J. H.; Menne, M. J.; Rennie, J.

2016-12-01

Global Surface Temperature (GST) is one of the most widely used indicators for climate trend and extreme analyses. A widely used GST dataset is the NOAA merged land-ocean surface temperature dataset known as NOAAGlobalTemp (formerly MLOST). The NOAAGlobalTemp had recently been updated from version 3.5.4 to version 4. The update includes a significant improvement in the ocean surface component (Extended Reconstructed Sea Surface Temperature or ERSST, from version 3b to version 4) which resulted in an increased temperature trends in recent decades. Since then, advancements in both the ocean component (ERSST) and land component (GHCN-Monthly) have been made, including the inclusion of Argo float SSTs and expanded EOT modes in ERSST, and the use of ISTI databank in GHCN-Monthly. In this presentation, we describe the impact of those improvements on the merged global temperature dataset, in terms of global trends and other aspects.

Improving Sediment Transport Prediction by Assimilating Satellite Images in a Tidal Bay Model of Hong Kong

Directory of Open Access Journals (Sweden)

Peng Zhang

2014-03-01

Full Text Available Numerical models being one of the major tools for sediment dynamic studies in complex coastal waters are now benefitting from remote sensing images that are easily available for model inputs. The present study explored various methods of integrating remote sensing ocean color data into a numerical model to improve sediment transport prediction in a tide-dominated bay in Hong Kong, Deep Bay. Two sea surface sediment datasets delineated from satellite images from the Moderate Resolution Imaging Spectra-radiometer (MODIS were assimilated into a coastal ocean model of the bay for one tidal cycle. It was found that remote sensing sediment information enhanced the sediment transport model ability by validating the model results with in situ measurements. Model results showed that root mean square errors of forecast sediment both at the surface layer and the vertical layers from the model with satellite sediment assimilation are reduced by at least 36% over the model without assimilation.

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

Science.gov (United States)

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

2001-01-01

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

A data-assimilative ocean forecasting system for the Prince William sound and an evaluation of its performance during sound Predictions 2009

Science.gov (United States)

Farrara, John D.; Chao, Yi; Li, Zhijin; Wang, Xiaochun; Jin, Xin; Zhang, Hongchun; Li, Peggy; Vu, Quoc; Olsson, Peter Q.; Schoch, G. Carl; Halverson, Mark; Moline, Mark A.; Ohlmann, Carter; Johnson, Mark; McWilliams, James C.; Colas, Francois A.

2013-07-01

The development and implementation of a three-dimensional ocean modeling system for the Prince William Sound (PWS) is described. The system consists of a regional ocean model component (ROMS) forced by output from a regional atmospheric model component (the Weather Research and Forecasting Model, WRF). The ROMS ocean model component has a horizontal resolution of 1km within PWS and utilizes a recently-developed multi-scale 3DVAR data assimilation methodology along with freshwater runoff from land obtained via real-time execution of a digital elevation model. During the Sound Predictions Field Experiment (July 19-August 3, 2009) the system was run in real-time to support operations and incorporated all available real-time streams of data. Nowcasts were produced every 6h and a 48-h forecast was performed once a day. In addition, a sixteen-member ensemble of forecasts was executed on most days. All results were published at a web portal (http://ourocean.jpl.nasa.gov/PWS) in real time to support decision making.The performance of the system during Sound Predictions 2009 is evaluated. The ROMS results are first compared with the assimilated data as a consistency check. RMS differences of about 0.7°C were found between the ROMS temperatures and the observed vertical profiles of temperature that are assimilated. The ROMS salinities show greater discrepancies, tending to be too salty near the surface. The overall circulation patterns observed throughout the Sound are qualitatively reproduced, including the following evolution in time. During the first week of the experiment, the weather was quite stormy with strong southeasterly winds. This resulted in strong north to northwestward surface flow in much of the central PWS. Both the observed drifter trajectories and the ROMS nowcasts showed strong surface inflow into the Sound through the Hinchinbrook Entrance and strong generally northward to northwestward flow in the central Sound that was exiting through the Knight

The effects of sea surface temperature gradients on surface turbulent fluxes

Science.gov (United States)

Steffen, John

A positive correlation between sea surface temperature (SST) and wind stress perturbation near strong SST gradients (DeltaSST) has been observed in different parts of the world ocean, such as the Gulf Stream in the North Atlantic and the Kuroshio Extension east of Japan. These changes in winds and SSTs can modify near-surface stability, surface stress, and latent and sensible heat fluxes. In general, these small scale processes are poorly modeled in Numerical Weather Prediction (NWP) and climate models. Failure to account for these air--sea interactions produces inaccurate values of turbulent fluxes, and therefore a misrepresentation of the energy, moisture, and momentum budgets. Our goal is to determine the change in these surface turbulent fluxes due to overlooking the correlated variability in winds, SSTs, and related variables. To model these air--sea interactions, a flux model was forced with and without SST--induced changes to the surface wind fields. The SST modification to the wind fields is based on a baroclinic argument as implemented by the University of Washington Planetary Boundary-Layer (UWPBL) model. Other input parameters include 2-m air temperature, 2-m dew point temperature, surface pressure (all from ERA--interim), and Reynolds Daily Optimum Interpolation Sea Surface Temperature (OISST). Flux model runs are performed every 6 hours starting in December 2002 and ending in November 2003. From these model outputs, seasonal, monthly, and daily means of the difference between DeltaSST and no DeltaSST effects on sensible heat flux (SHF), latent heat flux (LHF), and surface stress are calculated. Since the greatest impacts occur during the winter season, six additional December-January-February (DJF) seasons were analyzed for 1987--1990 and 1999--2002. The greatest differences in surface turbulent fluxes are concentrated near strong SST fronts associated with the Gulf Stream and Kuroshio Extension. On average, 2002---2003 DJF seasonal differences in SHF

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

Science.gov (United States)

Hoshiba, Yasuhiro; Hirata, Takafumi; Shigemitsu, Masahito; Nakano, Hideyuki; Hashioka, Taketo; Masuda, Yoshio; Yamanaka, Yasuhiro

2018-06-01

Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a one-dimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

Surface temperature and surface heat flux determination of the inverse heat conduction problem for a slab

International Nuclear Information System (INIS)

Kuroyanagi, Toshiyuki

1983-07-01

Based on an idea that surface conditions should be a reflection of interior temperature and interior heat flux variation as inverse as interior conditions has been determined completely by the surface temperature and/on surface heat flux as boundary conditions, a method is presented for determining the surface temperature and the surface heat flux of a solid when the temperature and heat flux at an interior point are a prescribed function of time. The method is developed by the integration of Duhumels' integral which has unknown temperature or unknown heat flux in its integrand. Specific forms of surface condition determination are developed for a sample inverse problem: slab. Ducussing the effect of a degree of avairable informations at an interior point due to damped system and the effect of variation of surface conditions on those formulations, it is shown that those formulations are capable of representing the unknown surface conditions except for small time interval followed by discontinuous change of surface conditions. The small un-resolved time interval is demonstrated by a numerical example. An evaluation method of heat flux at an interior point, which is requested by those formulations, is discussed. (author)

Skill Assessment in Ocean Biological Data Assimilation

Science.gov (United States)

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

2008-01-01

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

Fiber-Optic Surface Temperature Sensor Based on Modal Interference

Directory of Open Access Journals (Sweden)

Frédéric Musin

2016-07-01

Full Text Available Spatially-integrated surface temperature sensing is highly useful when it comes to controlling processes, detecting hazardous conditions or monitoring the health and safety of equipment and people. Fiber-optic sensing based on modal interference has shown great sensitivity to temperature variation, by means of cost-effective image-processing of few-mode interference patterns. New developments in the field of sensor configuration, as described in this paper, include an innovative cooling and heating phase discrimination functionality and more precise measurements, based entirely on the image processing of interference patterns. The proposed technique was applied to the measurement of the integrated surface temperature of a hollow cylinder and compared with a conventional measurement system, consisting of an infrared camera and precision temperature probe. As a result, the optical technique is in line with the reference system. Compared with conventional surface temperature probes, the optical technique has the following advantages: low heat capacity temperature measurement errors, easier spatial deployment, and replacement of multiple angle infrared camera shooting and the continuous monitoring of surfaces that are not visually accessible.

Surface alloying in Sn/Au(111) at elevated temperature

Science.gov (United States)

Sadhukhan, Pampa; Singh, Vipin Kumar; Rai, Abhishek; Bhattacharya, Kuntala; Barman, Sudipta Roy

2018-04-01

On the basis of x-ray photoelectron spectroscopy, we show that when Sn is deposited on Au(111) single crystal surface at a substrate temperature TS=373 K, surface alloying occurs with the formation of AuSn phase. The evolution of the surface structure and the surface morphology has been studied by low energy electron diffraction and scanning tunneling microscopy, respectively as a function of Sn coverage and substrate temperatures.

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

Science.gov (United States)

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

2017-12-01

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

Biomass assimilation in coupled ecohydrodynamical model of the Mediterranean Sea

Science.gov (United States)

Crispi, G.; Bournaski, E.; Crise, A.

2003-04-01

Data assimilation has raised new interest in the last years in the context of the environmental sciences. The swift increment of the attention paid to it in oceanography is due to the coming age of operational services for the marine environment which is going to dramatically increase the demand for accurate, timely and reliable estimates of the space and time distribution both for physical and in a near future for biogeochemical fields. Data assimilation combines information derived from measurements with knowledge of the rules that govern the evolution of the system of interest through formalization and implementation in numerical models. The importance of ocean data assimilation has been recognized by several international programmes as JGOFS, GOOS and CLIVAR. This work presents an eco-hydrodynamic model of the Mediterranean Sea developed at the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy. It includes 3-D MOM-based hydrodynamics of the Mediterranean Sea, coupled with biochemical model of Nitrogen, Phytoplankton, Zooplankton, and Detritus (NPZD). Monthly mean wind forcings are adopted to force this MOM-NPZD model. For better prediction and analysis of N, P, Z and D distributions in the sea the model needs data assimilation from biomass observations on the sea surface. Chosen approach for evaluating performances of data assimilation techniques in coupled model is the definition of a twin experiment testbed where a reference run is carried out assuming its result as the truth. We define a sampling strategy to obtain different datasets to be incorporated in another ecological model in successive runs in order to appraise the potential of the data assimilation and sampling strategy. The runs carried out with different techniques and different spatio-temporal coverages are compared in order to evaluate the sensitivity to different coverage of dataset. The discussed alternative way is to assume the ecosystem at steady state and

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

Science.gov (United States)

Patil, Amol; Ramsankaran, RAAJ

2017-12-01

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

SiGe Based Low Temperature Electronics for Lunar Surface Applications

Science.gov (United States)

Mojarradi, Mohammad M.; Kolawa, Elizabeth; Blalock, Benjamin; Cressler, John

2012-01-01

The temperature at the permanently shadowed regions of the moon's surface is approximately -240 C. Other areas of the lunar surface experience temperatures that vary between 120 C and -180 C during the day and night respectively. To protect against the large temperature variations of the moon surface, traditional electronics used in lunar robotics systems are placed inside a thermally controlled housing which is bulky, consumes power and adds complexity to the integration and test. SiGe Based electronics have the capability to operate over wide temperature range like that of the lunar surface. Deploying low temperature SiGe electronics in a lander platform can minimize the need for the central thermal protection system and enable the development of a new generation of landers and mobility platforms with highly efficient distributed architecture. For the past five years a team consisting of NASA, university and industry researchers has been examining the low temperature and wide temperature characteristic of SiGe based transistors for developing electronics for wide temperature needs of NASA environments such as the Moon, Titan, Mars and Europa. This presentation reports on the status of the development of wide temperature SiGe based electronics for the landers and lunar surface mobility systems.

Data assimilation of citizen collected information for real-time flood hazard mapping

Science.gov (United States)

Sayama, T.; Takara, K. T.

2017-12-01

Many studies in data assimilation in hydrology have focused on the integration of satellite remote sensing and in-situ monitoring data into hydrologic or land surface models. For flood predictions also, recent studies have demonstrated to assimilate remotely sensed inundation information with flood inundation models. In actual flood disaster situations, citizen collected information including local reports by residents and rescue teams and more recently tweets via social media also contain valuable information. The main interest of this study is how to effectively use such citizen collected information for real-time flood hazard mapping. Here we propose a new data assimilation technique based on pre-conducted ensemble inundation simulations and update inundation depth distributions sequentially when local data becomes available. The propose method is composed by the following two-steps. The first step is based on weighting average of preliminary ensemble simulations, whose weights are updated by Bayesian approach. The second step is based on an optimal interpolation, where the covariance matrix is calculated from the ensemble simulations. The proposed method was applied to case studies including an actual flood event occurred. It considers two situations with more idealized one by assuming continuous flood inundation depth information is available at multiple locations. The other one, which is more realistic case during such a severe flood disaster, assumes uncertain and non-continuous information is available to be assimilated. The results show that, in the first idealized situation, the large scale inundation during the flooding was estimated reasonably with RMSE effective. Nevertheless, the applications of the proposed data assimilation method demonstrated a high potential of this method for assimilating citizen collected information for real-time flood hazard mapping in the future.

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

Directory of Open Access Journals (Sweden)

E. E. Stigter

2017-07-01

Full Text Available Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE. Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF. Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May and decreases during the late melt season (June to September as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

On the importance of measurement error correlations in data assimilation for integrated hydrological models

Science.gov (United States)

Camporese, Matteo; Botto, Anna

2017-04-01

Data assimilation is becoming increasingly popular in hydrological and earth system modeling, as it allows us to integrate multisource observation data in modeling predictions and, in doing so, to reduce uncertainty. For this reason, data assimilation has been recently the focus of much attention also for physically-based integrated hydrological models, whereby multiple terrestrial compartments (e.g., snow cover, surface water, groundwater) are solved simultaneously, in an attempt to tackle environmental problems in a holistic approach. Recent examples include the joint assimilation of water table, soil moisture, and river discharge measurements in catchment models of coupled surface-subsurface flow using the ensemble Kalman filter (EnKF). One of the typical assumptions in these studies is that the measurement errors are uncorrelated, whereas in certain situations it is reasonable to believe that some degree of correlation occurs, due for example to the fact that a pair of sensors share the same soil type. The goal of this study is to show if and how the measurement error correlations between different observation data play a significant role on assimilation results in a real-world application of an integrated hydrological model. The model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental hillslope. The physical model, located in the Department of Civil, Environmental and Architectural Engineering of the University of Padova (Italy), consists of a reinforced concrete box containing a soil prism with maximum height of 3.5 m, length of 6 m, and width of 2 m. The hillslope is equipped with sensors to monitor the pressure head and soil moisture responses to a series of generated rainfall events applied onto a 60 cm thick sand layer overlying a sandy clay soil. The measurement network is completed by two tipping bucket flow gages to measure the two components (subsurface and surface) of the outflow. By collecting

Enhanced Soil Moisture Initialization Using Blended Soil Moisture Product and Regional Optimization of LSM-RTM Coupled Land Data Assimilation System.

Science.gov (United States)

Nair, A. S.; Indu, J.

2017-12-01

Prediction of soil moisture dynamics is high priority research challenge because of the complex land-atmosphere interaction processes. Soil moisture (SM) plays a decisive role in governing water and energy balance of the terrestrial system. An accurate SM estimate is imperative for hydrological and weather prediction models. Though SM estimates are available from microwave remote sensing and land surface model (LSM) simulations, it is affected by uncertainties from several sources during estimation. Past studies have generally focused on land data assimilation (DA) for improving LSM predictions by assimilating soil moisture from single satellite sensor. This approach is limited by the large time gap between two consequent soil moisture observations due to satellite repeat cycle of more than three days at the equator. To overcome this, in the present study, we have performed DA using ensemble products from the soil moisture operational product system (SMOPS) blended soil moisture retrievals from different satellite sensors into Noah LSM. Before the assimilation period, the Noah LSM is initialized by cycling through seven multiple loops from 2008 to 2010 forcing with Global data assimilation system (GDAS) data over the Indian subcontinent. We assimilated SMOPS into Noah LSM for a period of two years from 2010 to 2011 using Ensemble Kalman Filter within NASA's land information system (LIS) framework. Results show that DA has improved Noah LSM prediction with a high correlation of 0.96 and low root mean square difference of 0.0303 m3/m3 (figure 1a). Further, this study has also investigated the notion of assimilating microwave brightness temperature (Tb) as a proxy for SM estimates owing to the close proximity of Tb and SM. Preliminary sensitivity analysis show a strong need for regional parameterization of radiative transfer models (RTMs) to improve Tb simulation. Towards this goal, we have optimized the forward RTM using swarm optimization technique for direct Tb

Surface layer temperature inversion in the Bay of Bengal

Digital Repository Service at National Institute of Oceanography (India)

Pankajakshan, T.; Gopalakrishna, V.V.; Muraleedharan, P.M.; Reddy, G.V.; Araligidad, N.; Shenoy, Shrikant

Surface layer temperature inversion occurring in the Bay of Bengal has been addressed. Hydrographic data archived in the Indian Oceanographic Data Center are used to understand various aspects of the temperature inversion of surface layer in the Bay...

Temperature effect on surface oxidation of titanium

International Nuclear Information System (INIS)

Vaquilla, I.; Barco, J.L. del; Ferron, J.

1990-01-01

The effect of temperature on the first stages of the superficial oxidation of polycrystalline titanium was studied using both Auger electron spectroscopy (AES) and emission shreshold (AEAPS). The number of compounds present on the surface was determined by application of the factor analysis technique. Reaction evolution was followed through the relative variation of Auger LMM and LMV transitions which are characteristic of titanium. Also the evolution of the chemical shift was determined by AEAPS. The amount of oxygen on the surface was quantified using transition KLL of oxygen. It was found that superficial oxidation depends on temperature. As much as three different compounds were determined according to substrate temperature and our exposure ranges. (Author). 7 refs., 5 figs

Atmospheric Simulations Using OGCM-Assimilation SST: Influence of the Wintertime Japan Sea on Monthly Precipitation

Directory of Open Access Journals (Sweden)

Masaru Yamamoto Naoki Hirose

2010-01-01

Full Text Available Temperature data for the Japan Sea obtained from ocean data assimilation modeling is applied to atmospheric simulations of monthly precipitation for January 2005. Because the volume of flow of the Tsushima Warm Current was large during the winter season, the sea surface temperature (SST and coastal precipitation were higher in comparison with those in 2003. In order to evaluate influence of SST on monthly precipitation, we use surface temperatures of the Japan Sea in 2003 and 2005 for comparative simulations of precipitation for January 2005. The precipitation in experiment C (using cool SST data in 2003 is smaller than that in experiment W (using warm SST data in 2005 in a large part of the sea area, since the small evaporation results from the low SST over the upstream area of northwesterly winter monsoon. In the domain of 33.67 - 45.82°N and 125.89 - 142.9°E, the averaged evaporation and precipitation in experiment C are 10% and 13% smaller than those in experiment W, respectively. About half of the difference between the precipitations observed for January 2003 and 2005 in a heavy snow area is equal to the difference between the two simulations. Our results show that the mesoscale SST difference between 2003 and 2005 is related to the local difference of monthly precipitation.

Identifying anthropogenic anomalies in air, surface and groundwater temperatures in Germany.

Science.gov (United States)

Benz, Susanne A; Bayer, Peter; Blum, Philipp

2017-04-15

Human activity directly influences ambient air, surface and groundwater temperatures. The most prominent phenomenon is the urban heat island effect, which has been investigated particularly in large and densely populated cities. This study explores the anthropogenic impact on the thermal regime not only in selected urban areas, but on a countrywide scale for mean annual temperature datasets in Germany in three different compartments: measured surface air temperature, measured groundwater temperature, and satellite-derived land surface temperature. Taking nighttime lights as an indicator of rural areas, the anthropogenic heat intensity is introduced. It is applicable to each data set and provides the difference between measured local temperature and median rural background temperature. This concept is analogous to the well-established urban heat island intensity, but applicable to each measurement point or pixel of a large, even global, study area. For all three analyzed temperature datasets, anthropogenic heat intensity grows with increasing nighttime lights and declines with increasing vegetation, whereas population density has only minor effects. While surface anthropogenic heat intensity cannot be linked to specific land cover types in the studied resolution (1km×1km) and classification system, both air and groundwater show increased heat intensities for artificial surfaces. Overall, groundwater temperature appears most vulnerable to human activity, albeit the different compartments are partially influenced through unrelated processes; unlike land surface temperature and surface air temperature, groundwater temperatures are elevated in cultivated areas as well. At the surface of Germany, the highest anthropogenic heat intensity with 4.5K is found at an open-pit lignite mine near Jülich, followed by three large cities (Munich, Düsseldorf and Nuremberg) with annual mean anthropogenic heat intensities >4K. Overall, surface anthropogenic heat intensities >0K and

An eddy-permitting, dynamically consistent adjoint-based assimilation system for the tropical Pacific: Hindcast experiments in 2000

KAUST Repository

Hoteit, Ibrahim

2010-03-02

An eddy-permitting adjoint-based assimilation system has been implemented to estimate the state of the tropical Pacific Ocean. The system uses the Massachusetts Institute of Technology\\'s general circulation model and its adjoint. The adjoint method is used to adjust the model to observations by controlling the initial temperature and salinity; temperature, salinity, and horizontal velocities at the open boundaries; and surface fluxes of momentum, heat, and freshwater. The model is constrained with most of the available data sets in the tropical Pacific, including Tropical Atmosphere and Ocean, ARGO, expendable bathythermograph, and satellite SST and sea surface height data, and climatologies. Results of hindcast experiments in 2000 suggest that the iterated adjoint-based descent is able to significantly improve the model consistency with the multivariate data sets, providing a dynamically consistent realization of the tropical Pacific circulation that generally matches the observations to within specified errors. The estimated model state is evaluated both by comparisons with observations and by checking the controls, the momentum balances, and the representation of small-scale features that were not well sampled by the observations used in the assimilation. As part of these checks, the estimated controls are smoothed and applied in independent model runs to check that small changes in the controls do not greatly change the model hindcast. This is a simple ensemble-based uncertainty analysis. In addition, the original and smoothed controls are applied to a version of the model with doubled horizontal resolution resulting in a broadly similar “downscaled” hindcast, showing that the adjustments are not tuned to a single configuration (meaning resolution, topography, and parameter settings). The time-evolving model state and the adjusted controls should be useful for analysis or to supply the forcing, initial, and boundary conditions for runs of other models.

ERP ASSIMILATION: AN END-USER APPROACH

Directory of Open Access Journals (Sweden)

Hurbean Luminita

2013-07-01

The paper discusses the ERP adoption based on the IT assimilation theory. The ERP lifecycle is associated with the IT assimilation steps. We propose a distribution of these steps along the lifecycle. Derived from the findings in the reviewed literature we will focus the cultural factors, in particular those related to the end-users (determined as a major impact factor in our previous study: Negovan et al., 2011. Our empirical study is centred on the end-users perspective and it tries to determine if and how their behaviour affects the achievement of the ERP assimilation steps. The paper reasons that organizations that understand the IT assimilation steps correlated to the ERP implementation critical factors are more likely to implement and use ERP successfully.

Effective assimilation of global precipitation: simulation experiments

Directory of Open Access Journals (Sweden)

Guo-Yuan Lien

2013-07-01

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

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

Science.gov (United States)

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

2015-01-01

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

An experimental method for making spectral emittance and surface temperature measurements of opaque surfaces

International Nuclear Information System (INIS)

Moore, Travis J.; Jones, Matthew R.; Tree, Dale R.; Daniel Maynes, R.; Baxter, Larry L.

2011-01-01

An experimental procedure has been developed to make spectral emittance and temperature measurements. The spectral emittance of an object is calculated using measurements of the spectral emissive power and of the surface temperature of the object obtained using a Fourier transform infrared (FTIR) spectrometer. A calibration procedure is described in detail which accounts for the temperature dependence of the detector. The methods used to extract the spectral emissive power and surface temperature from measured infrared spectra were validated using a blackbody radiator at known temperatures. The average error in the measured spectral emittance was 2.1% and the average difference between the temperature inferred from the recorded spectra and the temperature indicated on the blackbody radiator was 1.2%. The method was used to measure the spectral emittance of oxidized copper at various temperatures.

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

Science.gov (United States)

Kuang, Weijia; Tangborn, Andrew

2014-01-01

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

Molecular dynamics simulation of temperature effects on low energy near-surface cascades and surface damage in Cu

Science.gov (United States)

Zhu, Guo; Sun, Jiangping; Guo, Xiongxiong; Zou, Xixi; Zhang, Libin; Gan, Zhiyin

2017-06-01

The temperature effects on near-surface cascades and surface damage in Cu(0 0 1) surface under 500 eV argon ion bombardment were studied using molecular dynamics (MD) method. In present MD model, substrate system was fully relaxed for 1 ns and a read-restart scheme was introduced to save total computation time. The temperature dependence of damage production was calculated. The evolution of near-surface cascades and spatial distribution of adatoms at varying temperature were analyzed and compared. It was found that near-surface vacancies increased with temperature, which was mainly due to the fact that more atoms initially located in top two layers became adatoms with the decrease of surface binding energy. Moreover, with the increase of temperature, displacement cascades altered from channeling-like structure to branching structure, and the length of collision sequence decreased gradually, because a larger portion of energy of primary knock-on atom (PKA) was scattered out of focused chain. Furthermore, increasing temperature reduced the anisotropy of distribution of adatoms, which can be ascribed to that regular registry of surface lattice atoms was changed with the increase of thermal vibration amplitude of surface atoms.

Molecular dynamics simulation of temperature effects on low energy near-surface cascades and surface damage in Cu

Energy Technology Data Exchange (ETDEWEB)

Zhu, Guo; Sun, Jiangping; Guo, Xiongxiong; Zou, Xixi; Zhang, Libin; Gan, Zhiyin, E-mail: ganzhiyin@126.com

2017-06-15

The temperature effects on near-surface cascades and surface damage in Cu(0 0 1) surface under 500 eV argon ion bombardment were studied using molecular dynamics (MD) method. In present MD model, substrate system was fully relaxed for 1 ns and a read-restart scheme was introduced to save total computation time. The temperature dependence of damage production was calculated. The evolution of near-surface cascades and spatial distribution of adatoms at varying temperature were analyzed and compared. It was found that near-surface vacancies increased with temperature, which was mainly due to the fact that more atoms initially located in top two layers became adatoms with the decrease of surface binding energy. Moreover, with the increase of temperature, displacement cascades altered from channeling-like structure to branching structure, and the length of collision sequence decreased gradually, because a larger portion of energy of primary knock-on atom (PKA) was scattered out of focused chain. Furthermore, increasing temperature reduced the anisotropy of distribution of adatoms, which can be ascribed to that regular registry of surface lattice atoms was changed with the increase of thermal vibration amplitude of surface atoms.

Effect of Vertical Canopy Architecture on Transpiration, Thermoregulation and Carbon Assimilation

Directory of Open Access Journals (Sweden)

Tirtha Banerjee

2018-04-01

Full Text Available Quantifying the impact of natural and anthropogenic disturbances such as deforestation, forest fires and vegetation thinning among others on net ecosystem—atmosphere exchanges of carbon dioxide, water vapor and heat—is an important aspect in the context of modeling global carbon, water and energy cycles. The absence of canopy architectural variation in horizontal and vertical directions is a major source of uncertainty in current climate models attempting to address these issues. This manuscript demonstrates the importance of considering the vertical distribution of foliage density by coupling a leaf level plant biophysics model with analytical solutions of wind flow and light attenuation in a horizontally homogeneous canopy. It is demonstrated that plant physiological response in terms of carbon assimilation, transpiration and canopy surface temperature can be widely different for two canopies with the same leaf area index (LAI but different leaf area density distributions, under several conditions of wind speed, light availability, soil moisture availability and atmospheric evaporative demand.

The approximate determination of the critical temperature of a liquid by measuring surface tension versus the temperature

International Nuclear Information System (INIS)

Maroto, J A; Nieves, F J de las; Quesada-Perez, M

2004-01-01

A classical experience in a physics student laboratory is to determine the surface tension of a liquid versus the temperature and to check the linear appearance of the obtained graph. In this work we show a simple method to estimate the critical temperature of three liquids by using experimental data of surface tension at different temperatures. By a logarithm fitting between surface tension and temperature, the critical temperature can be determined and compared with data from the literature. For two liquids (butanol and nitrobenzene) the comparison is acceptable but the differences are too high for the third liquid (water). By discussing the results it seems to be clear that the difference between the critical temperature of the liquid and the maximum temperature of the surface tension measurements is the determining factor in obtaining acceptable results. From this study it is possible to obtain more information on the liquid characteristics from surface tension measurements that are currently carried out in a student laboratory. Besides, in this paper it is shown how to select the most suitable liquids which provide both acceptable values for the critical temperature and measurements of the surface tension at moderate temperatures. The complementary use of numerical methods permits us to offer a complete experience for the students with a simple laboratory experiment which we recommend for physics students in advanced university courses

Remote sensing of land surface temperature: The directional viewing effect

International Nuclear Information System (INIS)

Smith, J.A.; Schmugge, T.J.; Ballard, J.R. Jr.

1997-01-01

Land Surface Temperature (LST) is an important parameter in understanding global environmental change because it controls many of the underlying processes in the energy budget at the surface and heat and water transport between the surface and the atmosphere. The measurement of LST at a variety of spatial and temporal scales and extension to global coverage requires remote sensing means to achieve these goals. Land surface temperature and emissivity products are currently being derived from satellite and aircraft remote sensing data using a variety of techniques to correct for atmospheric effects. Implicit in the commonly employed approaches is the assumption of isotropy in directional thermal infrared exitance. The theoretical analyses indicate angular variations in apparent infrared temperature will typically yield land surface temperature errors ranging from 1 to 4 C unless corrective measures are applied

Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site

OpenAIRE

Lei Ren; Michael Hartnett

2017-01-01

A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR) data into models of complex inshore waters strongly influenced by both ...

Temperature profiles on the gadolinium surface during electron beam evaporation

Energy Technology Data Exchange (ETDEWEB)

Ohba, Hironori; Shibata, Takemasa [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

1995-03-01

The distributions of surface temperature of gadolinium in a water-cooled copper crucible during electron beam evaporation were measured by optical pyrometry. The surface temperatures were obtained from the radiation intensity ratio of the evaporating surface and a reference light source using Planck`s law of radiation. The emitted radiation from the evaporating surface and a reference source was detected by a CCD sensor through a band pass filter of 650 nm. The measured surface temperature generally agreed with those estimated from the deposition rate and the data of the saturated vapor pressure. At high input powers, it was found that the measured value had small difference with the estimated one due to variation of the surface condition. (author).

Temperature profiles on the gadolinium surface during electron beam evaporation

International Nuclear Information System (INIS)

Ohba, Hironori; Shibata, Takemasa

1995-01-01

The distributions of surface temperature of gadolinium in a water-cooled copper crucible during electron beam evaporation were measured by optical pyrometry. The surface temperatures were obtained from the radiation intensity ratio of the evaporating surface and a reference light source using Planck's law of radiation. The emitted radiation from the evaporating surface and a reference source was detected by a CCD sensor through a band pass filter of 650 nm. The measured surface temperature generally agreed with those estimated from the deposition rate and the data of the saturated vapor pressure. At high input powers, it was found that the measured value had small difference with the estimated one due to variation of the surface condition. (author)

TWO METHODS FOR REMOTE ESTIMATION OF COMPLETE URBAN SURFACE TEMPERATURE

Directory of Open Access Journals (Sweden)

L. Jiang

2017-09-01

Full Text Available Complete urban surface temperature (TC is a key parameter for evaluating the energy exchange between the urban surface and atmosphere. At the present stage, the estimation of TC still needs detailed 3D structure information of the urban surface, however, it is often difficult to obtain the geometric structure and composition of the corresponding temperature of urban surface, so that there is still lack of concise and efficient method for estimating the TC by remote sensing. Based on the four typical urban surface scale models, combined with the Envi-met model, thermal radiant directionality forward modeling and kernel model, we analyzed a complete day and night cycle hourly component temperature and radiation temperature in each direction of two seasons of summer and winter, and calculated hemispherical integral temperature and TC. The conclusion is obtained by examining the relationship of directional radiation temperature, hemispherical integral temperature and TC: (1 There is an optimal angle of radiation temperature approaching the TC in a single observation direction when viewing zenith angle is 45–60°, the viewing azimuth near the vertical surface of the sun main plane, the average absolute difference is about 1.1 K in the daytime. (2 There are several (3–5 times directional temperatures of different view angle, under the situation of using the thermal radiation directionality kernel model can more accurately calculate the hemispherical integral temperature close to TC, the mean absolute error is about 1.0 K in the daytime. This study proposed simple and effective strategies for estimating TC by remote sensing, which are expected to improve the quantitative level of remote sensing of urban thermal environment.

Integrating remotely sensed surface water extent into continental scale hydrology.

Science.gov (United States)

Revilla-Romero, Beatriz; Wanders, Niko; Burek, Peter; Salamon, Peter; de Roo, Ad

2016-12-01

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE', NSE, PBIAS%, R 2 , RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that

Titan's Surface Temperatures Maps from Cassini - CIRS Observations

Science.gov (United States)

Cottini, Valeria; Nixon, C. A.; Jennings, D. E.; Anderson, C. M.; Samuelson, R. E.; Irwin, P. G. J.; Flasar, F. M.

2009-09-01

The Cassini Composite Infrared Spectrometer (CIRS) observations of Saturn's largest moon, Titan, are providing us with the ability to detect the surface temperature of the planet by studying its outgoing radiance through a spectral window in the thermal infrared at 19 μm (530 cm-1) characterized by low opacity. Since the first acquisitions of CIRS Titan data the instrument has gathered a large amount of spectra covering a wide range of latitudes, longitudes and local times. We retrieve the surface temperature and the atmospheric temperature profile by modeling proper zonally averaged spectra of nadir observations with radiative transfer computations. Our forward model uses the correlated-k approximation for spectral opacity to calculate the emitted radiance, including contributions from collision induced pairs of CH4, N2 and H2, haze, and gaseous emission lines (Irwin et al. 2008). The retrieval method uses a non-linear least-squares optimal estimation technique to iteratively adjust the model parameters to achieve a spectral fit (Rodgers 2000). We show an accurate selection of the wide amount of data available in terms of footprint diameter on the planet and observational conditions, together with the retrieved results. Our results represent formal retrievals of surface brightness temperatures from the Cassini CIRS dataset using a full radiative transfer treatment, and we compare to the earlier findings of Jennings et al. (2009). In future, application of our methodology over wide areas should greatly increase the planet coverage and accuracy of our knowledge of Titan's surface brightness temperature. References: Irwin, P.G.J., et al.: "The NEMESIS planetary atmosphere radiative transfer and retrieval tool" (2008). JQSRT, Vol. 109, pp. 1136-1150, 2008. Rodgers, C. D.: "Inverse Methods For Atmospheric Sounding: Theory and Practice". World Scientific, Singapore, 2000. Jennings, D.E., et al.: "Titan's Surface Brightness Temperatures." Ap. J. L., Vol. 691, pp. L103-L

SURFACE TEMPERATURES ON TITAN DURING NORTHERN WINTER AND SPRING

Energy Technology Data Exchange (ETDEWEB)

Jennings, D. E.; Cottini, V.; Nixon, C. A.; Achterberg, R. K.; Flasar, F. M.; Kunde, V. G.; Romani, P. N.; Samuelson, R. E. [Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Mamoutkine, A. [ADNET Systems, Inc., Bethesda, MD 20817 (United States); Gorius, N. J. P. [The Catholic University of America, Washington, DC 20064 (United States); Coustenis, A. [Laboratoire d’Etudes Spatiales et d’Instrumentation en Astrophysique (LESIA), Observatoire de Paris, CNRS, UPMC Univ. Paris 06, Univ. Paris-Diderot, 5, place Jules Janssen, F-92195 Meudon Cedex (France); Tokano, T., E-mail: donald.e.jennings@nasa.gov [Universität zu Köln, Albertus-Magnus-Platz, D-50923 Köln (Germany)

2016-01-01

Meridional brightness temperatures were measured on the surface of Titan during the 2004–2014 portion of the Cassini mission by the Composite Infrared Spectrometer. Temperatures mapped from pole to pole during five two-year periods show a marked seasonal dependence. The surface temperature near the south pole over this time decreased by 2 K from 91.7 ± 0.3 to 89.7 ± 0.5 K while at the north pole the temperature increased by 1 K from 90.7 ± 0.5 to 91.5 ± 0.2 K. The latitude of maximum temperature moved from 19 S to 16 N, tracking the sub-solar latitude. As the latitude changed, the maximum temperature remained constant at 93.65 ± 0.15 K. In 2010 our temperatures repeated the north–south symmetry seen by Voyager one Titan year earlier in 1980. Early in the mission, temperatures at all latitudes had agreed with GCM predictions, but by 2014 temperatures in the north were lower than modeled by 1 K. The temperature rise in the north may be delayed by cooling of sea surfaces and moist ground brought on by seasonal methane precipitation and evaporation.

SURFACE TEMPERATURES ON TITAN DURING NORTHERN WINTER AND SPRING

International Nuclear Information System (INIS)

Jennings, D. E.; Cottini, V.; Nixon, C. A.; Achterberg, R. K.; Flasar, F. M.; Kunde, V. G.; Romani, P. N.; Samuelson, R. E.; Mamoutkine, A.; Gorius, N. J. P.; Coustenis, A.; Tokano, T.

2016-01-01

Meridional brightness temperatures were measured on the surface of Titan during the 2004–2014 portion of the Cassini mission by the Composite Infrared Spectrometer. Temperatures mapped from pole to pole during five two-year periods show a marked seasonal dependence. The surface temperature near the south pole over this time decreased by 2 K from 91.7 ± 0.3 to 89.7 ± 0.5 K while at the north pole the temperature increased by 1 K from 90.7 ± 0.5 to 91.5 ± 0.2 K. The latitude of maximum temperature moved from 19 S to 16 N, tracking the sub-solar latitude. As the latitude changed, the maximum temperature remained constant at 93.65 ± 0.15 K. In 2010 our temperatures repeated the north–south symmetry seen by Voyager one Titan year earlier in 1980. Early in the mission, temperatures at all latitudes had agreed with GCM predictions, but by 2014 temperatures in the north were lower than modeled by 1 K. The temperature rise in the north may be delayed by cooling of sea surfaces and moist ground brought on by seasonal methane precipitation and evaporation

AMOC variations in 1979-2008 simulated by NCEP operational ocean data assimilation system

Energy Technology Data Exchange (ETDEWEB)

Huang, Boyin [Climate Prediction Center, NOAA, Camp Springs, MD (United States); National Climate Data Center, Asheville, NC (United States); Xue, Yan; Kumar, Arun [Climate Prediction Center, NOAA, Camp Springs, MD (United States); Behringer, David W. [Environmental Modeling Center, NOAA, Camp Springs, MD (United States)

2012-02-15

Variations in the Atlantic meridional overturning circulation (AMOC) between 1979 and 2008 are documented using the operational ocean analysis, the Global Ocean Data Assimilation System (GODAS), at the National Centers for Climate Prediction (NCEP). The maximum AMOC at 40 N is about 16 Sv in average with peak-to-peak variability of 3-4 Sv. The AMOC variations are dominated by an upward trend from 1980 to 1995, and a downward trend from 1995 to 2008. The maximum AMOC at 26.5 N is slightly weaker than hydrographic estimates and observations from mooring array. The dominant variability of the AMOC in 20 -65 N (the first EOF, 51% variance) is highly correlated with that in the subsurface temperature (the first EOF, 33% variance), and therefore, with density (the first EOF, 25% variance) in the North Atlantic, and is consistent with the observational estimates based on the World Ocean Database 2005. The dominant variabilities of AMOC and subsurface temperature are also analyzed in the context of possible links with the net surface heat flux, deep convection, western boundary current, and subpolar gyre. Variation in the net surface heat flux is further linked to the North Atlantic Oscillation (NAO) index which is found to lead AMOC variations by about 5 years. Our results indicate that AMOC variations can be documented based on an ocean analysis system such as GODAS. (orig.)

OW NOAA GOES Sea-Surface Temperature

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The dataset contains satellite-derived sea-surface temperature measurements collected by means of the Geostationary Orbiting Environmental Satellite. The data is...

Sensitivity of CAM-Chem/DART MOPITT CO Assimilation Performance to the Choice of Ensemble System Configuration: A Case Study for Fires in the Amazon

Science.gov (United States)

Arellano, A. F., Jr.; Tang, W.

2017-12-01

Assimilating observational data of chemical constituents into a modeling system is a powerful approach in assessing changes in atmospheric composition and estimating associated emissions. However, the results of such chemical data assimilation (DA) experiments are largely subject to various key factors such as: a) a priori information, b) error specification and representation, and c) structural biases in the modeling system. Here we investigate the sensitivity of an ensemble-based data assimilation state and emission estimates to these key factors. We focus on investigating the assimilation performance of the Community Earth System Model (CESM)/CAM-Chem with the Data Assimilation Research Testbed (DART) in representing biomass burning plumes in the Amazonia during the 2008 fire season. We conduct the following ensemble DA MOPITT CO experiments: 1) use of monthly-average NCAR's FINN surface fire emissionss, 2) use of daily FINN surface fire emissions, 3) use of daily FINN emissions with climatological injection heights, and 4) use of perturbed FINN emission parameters to represent not only the uncertainties in combustion activity but also in combustion efficiency. We show key diagnostics of assimilation performance for these experiments and verify with available ground-based and aircraft-based measurements.

Surface temperature measurement of plasma facing components in tokamaks

International Nuclear Information System (INIS)

Amiel, Stephane

2014-01-01

During this PhD, the challenges on the non-intrusive surface temperature measurements of metallic plasma facing components in tokamaks are reported. Indeed, a precise material emissivity value is needed for classical infrared methods and the environment contribution has to be known particularly for low emissivities materials. Although methods have been developed to overcome these issues, they have been implemented solely for dedicated experiments. In any case, none of these methods are suitable for surface temperature measurement in tokamaks.The active pyrometry introduced in this study allows surface temperature measurements independently of reflected flux and emissivities using pulsed and modulated photothermal effect. This method has been validated in laboratory on metallic materials with reflected fluxes for pulsed and modulated modes. This experimental validation is coupled with a surface temperature variation induced by photothermal effect and temporal signal evolvement modelling in order to optimize both the heating source characteristics and the data acquisition and treatment. The experimental results have been used to determine the application range in temperature and detection wavelengths. In this context, the design of an active pyrometry system on tokamak has been completed, based on a bicolor camera for a thermography application in metallic (or low emissivity) environment.The active pyrometry method introduced in this study is a complementary technique of classical infrared methods used for thermography in tokamak environment which allows performing local and 2D surface temperature measurements independently of reflected fluxes and emissivities. (author) [fr

Analysed foundation sea surface temperature, global

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST). To utilize this, scientists at Remote...

Meteorological data assimilation for real-time emergency response

International Nuclear Information System (INIS)

Sugiyama, G.; Chan, S.T.

1996-11-01

The US Department of Energy's Atmospheric Release Advisory Capability (ARAC) provides real-time dose assessments of airborne pollutant releases. Diverse data assimilation techniques are required to meet the needs of a new generation of ARAC models and to take advantage of the rapidly expanding availability of meteorological data. We are developing a hierarchy of algorithms to provide gridded meteorological fields which can be used to drive dispersion codes or to provide initial fields for mesoscale models. Data to be processed include winds, temperature, moisture, and turbulence

Turbulent viscosity optimized by data assimilation

Directory of Open Access Journals (Sweden)

Y. Leredde

Full Text Available As an alternative approach to classical turbulence modelling using a first or second order closure, the data assimilation method of optimal control is applied to estimate a time and space-dependent turbulent viscosity in a three-dimensional oceanic circulation model. The optimal control method, described for a 3-D primitive equation model, involves the minimization of a cost function that quantifies the discrepancies between the simulations and the observations. An iterative algorithm is obtained via the adjoint model resolution. In a first experiment, a k + L model is used to simulate the one-dimensional development of inertial oscillations resulting from a wind stress at the sea surface and with the presence of a halocline. These results are used as synthetic observations to be assimilated. The turbulent viscosity is then recovered without the k + L closure, even with sparse and noisy observations. The problems of controllability and of the dimensions of the control are then discussed. A second experiment consists of a two-dimensional schematic simulation. A 2-D turbulent viscosity field is estimated from data on the initial and final states of a coastal upwelling event.

Key words. Oceanography: general (numerical modelling · Oceanography: physical (turbulence · diffusion · and mixing processes

Effects of temperature and surface orientation on migration behaviours of helium atoms near tungsten surfaces

Energy Technology Data Exchange (ETDEWEB)

Wang, Xiaoshuang; Wu, Zhangwen; Hou, Qing, E-mail: qhou@scu.edu.cn

2015-10-15

Molecular dynamics simulations were performed to study the dependence of migration behaviours of single helium atoms near tungsten surfaces on the surface orientation and temperature. For W{100} and W{110} surfaces, He atoms can quickly escape out near the surface without accumulation even at a temperature of 400 K. The behaviours of helium atoms can be well-described by the theory of continuous diffusion of particles in a semi-infinite medium. For a W{111} surface, the situation is complex. Different types of trap mutations occur within the neighbouring region of the W{111} surface. The trap mutations hinder the escape of He atoms, resulting in their accumulation. The probability of a He atom escaping into vacuum from a trap mutation depends on the type of the trap mutation, and the occurrence probabilities of the different types of trap mutations are dependent on the temperature. This finding suggests that the escape rate of He atoms on the W{111} surface does not show a monotonic dependence on temperature. For instance, the escape rate at T = 1500 K is lower than the rate at T = 1100 K. Our results are useful for understanding the structural evolution and He release on tungsten surfaces and for designing models in other simulation methods beyond molecular dynamics.

Estimation of the under-surface temperature pattern by dynamic remote sensing