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Read Full Article (file size: 1702262 bytes) Cited by
WATER RESOURCES RESEARCH,
VOL. 43,
W09410,
doi:10.1029/2006WR005449,
2007
Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter
Gabriëlle J. M. De Lannoy
Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
Rolf H. Reichle
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland, USA
Paul R. Houser
George Mason University and Center for Research on Environment and Water, Calverton, Maryland, USA
Valentijn R. N. Pauwels
Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
Niko E. C. Verhoest
Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
Abstract
Land surface models are usually biased in at least a subset of the simulated variables even after calibration. Bias estimation
may therefore be needed for data assimilation. Here, in situ soil moisture profile observations in a small agricultural field
were merged with Community Land Model (CLM2.0) simulations using different algorithms for state and forecast bias estimation
with and without bias correction feedback. Simple state updating with the conventional ensemble Kalman filter (EnKF) allows
for some implicit forecast bias correction. It is possible to estimate the soil moisture bias explicitly and derive superior
soil moisture estimates with a generalized EnKF that uses a simple persistence model for the bias and assumes that the a priori
bias error covariance is proportional to the a priori state error covariance. For the case of bi-weekly assimilation of the
entire profile of soil moisture observations, bias estimation and correction typically reduces the RMSE in soil moisture (over the standard EnKF without bias correction) by around 60 percent. However, under the above assumptions,
significant improvements are limited to state variables for which observations are available. Therefore, it is crucial to
measure the state variables of interest. The best variant for state and bias estimation depends on the nature of the model
bias and the output of interest to the user. In a model that is only biased for soil moisture, large and frequent increments
for soil moisture updating may be required, which in turn may negatively impact the water balance and output fluxes. It is
then better to post-process the soil moisture with the bias analysis without updating the model state.
Received 21
August
2006;
accepted 21
June
2007;
published 19
September
2007.
Keywords: Soil moisture;
ensemble;
Kalman filter;
bias;
land surface model;
model error;
forecast error.
Index Terms: 1836 Hydrology: Hydrological cycles and budgets (1218, 1655); 1840 Hydrology: Hydrometeorology; 1866 Hydrology: Soil moisture; 1873 Hydrology: Uncertainty assessment (3275).
Read Full Article (file size: 1702262 bytes) Cited by
Citation: De Lannoy, G. J. M., R. H. Reichle, P. R. Houser, V. R. N. Pauwels, and N. E. C. Verhoest
(2007),
Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter,
Water Resour. Res.,
43,
W09410,
doi:10.1029/2006WR005449.
Copyright 2007 by the American Geophysical Union.
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