Abstract
Abstract The importance of horizontal error correlations in background (i.e., model forecast) fields for large-scale soil moisture estimation is assessed by comparing the performance of one- and three-dimensional ensemble Kalman filters (EnKF) in a twin experiment. Over a domain centered on the U. S. Great Plains, gauge-based precipitation data is used to force the “true” model solution, and reanalysis data for the prior (or background) fields. The difference between the two precipitation datasets is thought to be representative of errors that might be encountered in a global land assimilation system. To ensure realistic conditions the synthetic observations of surface soil moisture match the spatiotemporal pattern and expected errors of retrievals from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite. After filter calibration, average actual estimation errors in the (volumetric) root zone moisture content are 0.015 m3 m−3 for the 3D-EnKF, 0.019 m3 m−3 for the 1D-EnKF, and 0...
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