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Abstract:
Although surface soil moisture data from different sources (satellite retrievals,
ground measurements, and land model integrations of observed meteorological
forcing data) have been shown to contain consistent and useful information
in their seasonal cycle and anomaly signals, they typically exhibit very
different mean values and variability. These biases pose a severe obstacle to
exploiting the useful information contained in satellite retrievals through data
assimilation. A simple method of bias removal is to match the cumulative
distribution functions (cdf) of the satellite and model data. However, accurate
cdf estimation typically requires a long record of satellite data. We demonstrate
here that by using spatial sampling with a 2 degree moving window
we can obtain local statistics based on a one-year satellite record that are
a good approximation to those that would be derived from a much longer
time series. This result should increase the usefulness of relatively short satellite
data records.