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Title:   Author  item
                                            Bolten, Johnitem
                                            Crow, Wadeitem
                                            ZHAN, X - NOAA NESDIS
  Submitted to: American Geophysical Union
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 11/30/2007
                                    Publication  Date: 12/12/2007
                                    Citation: Bolten, J.D., Crow, W.T., Zhan, X. 2007. A global soil moisture data assimilation system for the USDA-FAS Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System [abstract]. American Geophysical Union Fall Meeting. 2007 CDROM.Interpretive Summary:
                                Technical Abstract: An Ensemble Kalman Filter (EnKF) system has been designed to integrate soil moisture retrievals from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture (USDA) Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS). The operational soil moisture model currently used by CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The improved coverage and temporal resolution of AMSR-E soil moisture retrievals upon the AFWA and WMO data is envisaged to provide a better characterization of surface wetness conditions particularly where the AFWA and WMO data are sparse. This study evaluates the added value of the AMSR-E soil moisture data assimilation over the conterminous United States. The experimental methodology is based on designating a single model realization forced with reliable precipitation as truth. The EnKF is then applied to assimilate AMSR-E soil moisture estimates into the model runs forced by an error-prone precipitation dataset. Effectiveness of the soil moisture data assimilation system is evaluated by comparing the EnKF output soil moisture with the truth soil moisture. System design and model uncertainly is evaluated by comparisons with in-situ observations, and analyzing the filter divergence, and innovation statistics. Applying global AMSR-E soil moisture retrievals to the soil moisture data assimilation system for the CADRE decision support system of USDA-FAS will be discussed.


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