Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: November 5, 2006
Publication Date: December 11, 2006
Citation: Crow, W., Zhan, X. 2006. A novel data ssimilation approach for the continental-scale evaluation of spaceborne soil moisture products [abstract]. EOS Transactions, American Geophysical Union, Fall Supplements. 87(52):H21I-02. Technical Abstract: A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the Kalman filter-based assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e. discrete additions or subtractions of water suggested by the Kalman filter) are then compared to antecedent precipitation errors determined using higher quality rain gauge observations. A synthetic twin data assimilation experiment demonstrates that the correlation coefficient between antecedent precipitation errors and filter increments provides a robust proxy for the accuracy of the soil moisture retrievals themselves. In addition, the presence of rainfall error/analysis increment correlation explicitly captures the added utility of assimilating spaceborne soil moisture products to compensate model predictions for the impact of errors in global precipitation products. Given the inherent difficulty of directly validating remotely-sensed soil moisture products using ground-based soil moisture observations, this assimilation-based proxy provides a valuable tool for efforts to improve soil moisture retrieval strategies and quantify the novel information content of remotely sensed soil moisture retrievals for land surface modeling applications. Using multi-year spaceborne soil moisture products derived from thermal (GOES), passive microwave (AMSR-E) and microwave scatterometer (ERS 1/2) sensors, the data assimilation approach is applied over the entire conterminous United States. Results define the fractional area of the United States in which remote measurements contribute to land surface modeling and clarify the relative effectiveness of various approaches over different land surface conditions.