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Abstract:
We assess the performance of the Extended Kalman filter (EKF)
and the Ensemble Kalman filter (EnKF) for soil moisture estimation.
In a twin experiment for the south-eastern United States
we assimilate synthetic observations of near-surface
soil moisture once every three days, neglecting horizontal error
correlations and treating catchments independently.
Both filters provide satisfactory estimates of soil moisture.
The average actual estimation error in volumetric moisture
content of the soil profile is 2.2 percent for the EKF and 2.2 percent
(or 2.1 percent; or 2.0 percent) for the EnKF with 4 (or 10; or 500)
ensemble members.
Expected error covariances of both filters generally differ from
actual estimation errors.
Nevertheless, nonlinearities in soil processes are treated adequately
by both filters.
In our application, the EKF and the EnKF with four ensemble members
are equally accurate at comparable computational cost.
Because of its flexibility and its performance in our study,
the EnKF is a promising approach for soil moisture initialization
problems.