Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the submonthly scale accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain gauge observations. In order to adapt to the irregular retrieval frequency of heritage soil moisture products, a new variable correction window method is developed that allows for better efficiency in leveraging temporally sparse satellite soil moisture retrievals. Results confirm the advantage of using this variable window method relative to an existing fixed-window version of SMART over a range of one- to 30-day accumulation periods. Using this modified version of SMART and heritage satellite surface soil moisture products, a 1.0-deg, 20-year (1979 to 1998) global rainfall dataset over land is corrected and validated. Relative to the original precipitation product, the corrected dataset demonstrates improved correlation with a global gauge-based daily rainfall product, lower root-mean-square-error (−13%) on a 10-day scale and provides a higher probability of detection (+5%) and lower false alarm rates (−3.4%) for five-day rainfall accumulation estimates. This corrected rainfall dataset is expected to provide improved rainfall forcing data for the land surface modeling community.
Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global
crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US
Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system
utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature
data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of
this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal
coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at
the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing
product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of
Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational
data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E
soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system
design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.
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