Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART)

Recently, Crow et al. (2009) developed an algorithm for enhancing satellite-based land rainfall products via the assimilation of remotely sensed surface soil moisture retrievals into a water balance model. As a follow-up, this paper describes the benefits of modifying their approach to incorporate more complex data assimilation and land surface modeling methodologies. Specific modifications improving rainfall estimates are assembled into the Soil Moisture Analysis Rainfall Tool (SMART), and the resulting algorithm is applied outside the contiguous United States for the first time, with an emphasis on West African sites instrumented as part of the African Monsoon Multidisciplinary Analysis experiment. Results demonstrate that the SMART algorithm is superior to the Crow et al. baseline approach and is capable of broadly improving coarse-scale rainfall accumulations measurements with low risk of degradation. Comparisons with existing multisensor, satellite-based precipitation data products suggest that the introduction of soil moisture information from the Advanced Microwave Scanning Radiometer via SMART provides as much coarse-scale (3 day, 1 degrees) rainfall accumulation information as thermal infrared satellite observations and more information than monthly rain gauge observations in poorly instrumented regions.