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United States Department of Agriculture

Agricultural Research Service

Title: The Soil Moisture Analysis Rainfall Tool (SMART): Correcting satellite-based precipitation using land data assimilation)

item Crow, Wade

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/1/2010
Publication Date: 9/30/2010
Citation: Crow, W.T. 2010. The Soil Moisture Analysis Rainfall Tool (SMART): Correcting satellite-based precipitation using land data assimilation [abstract]. 2010 IAHS Remote Sensing and Hydrology Symposium. p. 25.

Interpretive Summary:

Technical Abstract: Despite the obvious physical connection between surface soil moisture conditions and antecedent rainfall, relatively little attention has been paid to date on integrating surface water balance information obtained from both spaceborne surface soil moisture and precipitation retrievals. Recently, Crow et al. (2008) developed a Kalman filter-based strategy for improving satellite-based rainfall estimates over land using remotely-sensed surface soil moisture retrievals. Their approach is based on the application of a Kalman filter to assimilate AMSR-E surface soil moisture retrievals into a linear water balance model forced by daily, satellite-based precipitation products. Kalman filtering analysis increments (i.e. net additions or subtractions of water imposed by the filter upon the sequential assimilation of a soil moisture observation) are then correctively added to the antecedent precipitation time series to compensate for stochastic error in the satellite rainfall time series. Despite its initial success in a demonstration study over the contiguous United States, the baseline approach of Crow et al. (2008) suffers from a number of shortcomings linked to the simplicity of its land surface modeling and data assimilation component. This talk will introduce the Soil Moisture Analysis Rainfall Tool (SMART). The SMART system is on is based on the existing, simple approach applied by Crow et al. (2007) but augmented to include: 1) a more complex land surface model, 2) a better representation of conditional rainfall errors and 3) an improved algorithm for rain/no-rain detection. These modifications, and the SMART system as whole, will then be globally evaluated using data products acquired from the Tropical Rainfall Measurement Mission (TRMM).

Last Modified: 8/24/2016
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