Abstract:
We assess the importance of horizontal error correlations in background fields for large-scale soil moisture estimation by comparing the performance of one- and three-dimensional ensemble Kalman filters (EnKF) in a twin experiment. Over a domain centered on the United States Great Plains we use gauge-based precipitation data to force the ``true'' model solution, and reanalysis data for the prior (or background) fields. The difference between the two precipitation data sets is thought to be representative of errors we might encounter in a global land assimilation system. To ensure realistic conditions the synthetic observations of surface soil moisture match the spatio-temporal 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/m3 for the 3D-EnKF, 0.019 m3/m3 for the 1D-EnKF, and 0.036 m3/m3 without assimilation. Clearly, taking horizontal error correlations into account improves estimation accuracy. Soil moisture estimation errors in the 3D-EnKF are smallest for a correlation scale of 2 degrees, which coincides with the horizontal scale of difference fields between gauge-based and reanalysis precipitation. In our case the 3D-EnKF requires 1.6 times the computational effort of the 1D-EnKF, but this factor depends on the experiment setup.