Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 19, 2007
Publication Date: April 28, 2007
Repository URL: http://handle.nal.usda.gov/10113/59988
Citation: Crow, W.T., Bolten, J.D. 2007. Estimating precipitation errors using spaceborne surface soil moisture retrievals. Geophysical Research Letters. 34, L08403, doi:10.1029/2007GL029450. Interpretive Summary: Errors in global rainfall measurements are a key source of uncertainty in efforts to globally monitor and predict agricultural crop yields. Unfortunately, for many areas of the globe, ground-based precipitation observations are insufficient to accurately evaluate the quality of satellite-based rainfall estimates. This paper develops a technique for estimating the accuracy of satellite-based rainfall observations by examining their degree of dynamic consistency with satellite-based soil moisture retrievals. A higher degree of consistency with observed soil moisture variations is used as proof that a particular satellite-based rainfall product is more accurate and will yield the highest possible yield forecasts when incorporated into USDA’s global crop forecast system. Given the global availability of satellite-based soil moisture observations, such insight is globally available and not limited to sparse areas of North American and Europe containing extensive ground-based rainfall measuring resources.
Technical Abstract: Limitations in the availability of ground-based rain gauge data currently hamper our ability to quantify errors in global precipitation products over data-poor areas of the world. Over land, these limitations may be eased by approaches based on interpreting the degree of dynamic consistency existing between precipitation estimates and remotely sensed surface soil moisture retrievals. This paper demonstrates how such an approach can be implemented within an adaptive Kalman Filter to reliably estimate daily rainfall errors in global precipitation products without reliance on ground-based precipitation observations.