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United States Department of Agriculture

Agricultural Research Service

Title: Exploiting Potential Synergies Between Spaceborne Rainfall and Surface Soil Moisture Retrievals

Authors
item Crow, Wade
item Bolten, John
item Zhan, Xiwu - NOAA NESDIS

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: March 14, 2007
Publication Date: April 1, 2007
Citation: Crow, W.T., Bolten, J.D., Zhan, X. 2007. Exploiting potential synergies between spaceborne rainfall and surface soil moisture retrievals [abstract]. Workshop on Satellite Observation of the Global Water Cycle. 2007 CDROM. v.1.

Technical Abstract: The validation of satellite-based precipitation and soil moisture retrievals with ground-based resources represents a notable challenge - particularly in global areas lacking adequate ground-based rain radar and rain gauge observations. Over land, these difficulties may be eased by techniques that evaluate the degree of hydrologic consistency existing between rainfall and other hydrologic variables. In particular, the simultaneous spaceborne retrieval of both global precipitation and surface soil moisture products provides an opportunity to evaluate both products based on their mutual dynamic consistency. This presentation will discuss two simple, data assimilation-based validation strategies which exploit this potential. The first approach is based on applying an adaptive Kalman filtering strategy to the assimilation of spaceborne surface soil moisture retrievals into a simple linear water balance model forced by a range of global precipitation products. Modeling uncertainties derived via tuning of filter error parameters - based on the statistical analysis of filtering innovations - will be compared with actual errors in precipitation forcing products to evaluate whether the approach is capable of reliably estimating the accuracy of global precipitation products in the absence of ground-based rainfall observations. The second approach is based on evaluating the correlation coefficient existing between antecedent rainfall errors and analysis increments realized during the Kalman filter-based assimilation of remotely-sensed soil moisture products into a simple water balance model. Results will demonstrate that this correlation coefficient provides an effective, and readily available, proxy for the actual value of the remotely-sensed soil moisture product for data assimilation applications.

Last Modified: 8/31/2014
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