Editorial Type:
Article
Article Type:
Research Article

Reducing Water Imbalance in Land Data Assimilation: Ensemble Filtering without Perturbed Observations

M. Tugrul Yilmaz George Mason University, Fairfax, Virginia

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Timothy DelSole George Mason University, Fairfax, Virginia, and Virginia Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

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Paul R. Houser George Mason University, Fairfax, Virginia

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Print Publication:
01 Feb 2012
DOI:
https://doi.org/10.1175/JHM-D-11-010.1
Page(s):
413–420
Restricted access

Abstract

It is well known that the ensemble Kalman filter (EnKF) requires updating each ensemble member with perturbed observations in order to produce the proper analysis-error covariances. While increased accuracy in a mean square sense may be preferable in many applications, less accuracy might be preferable in other applications, especially if the variables being assimilated obey certain conservation laws. In land data assimilation, for instance, the update in soil moisture often produces a water balance residual, in the sense that the input water is not equal to output water. This study shows that suppressing the perturbation of observations in the EnKF and in the weakly constrained ensemble Kalman filter significantly improves the water balance residuals without significantly increasing the state errors.

Corresponding author address: M. Tugrul Yilmaz, Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, 10300 Baltimore Ave., BARC-WEST, Bldg 007, Room 104, Beltsville, MD 20705. E-mail: tugrul.yilmaz@ars.usda.gov

Current affiliation: Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland.

Abstract

It is well known that the ensemble Kalman filter (EnKF) requires updating each ensemble member with perturbed observations in order to produce the proper analysis-error covariances. While increased accuracy in a mean square sense may be preferable in many applications, less accuracy might be preferable in other applications, especially if the variables being assimilated obey certain conservation laws. In land data assimilation, for instance, the update in soil moisture often produces a water balance residual, in the sense that the input water is not equal to output water. This study shows that suppressing the perturbation of observations in the EnKF and in the weakly constrained ensemble Kalman filter significantly improves the water balance residuals without significantly increasing the state errors.

Corresponding author address: M. Tugrul Yilmaz, Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, 10300 Baltimore Ave., BARC-WEST, Bldg 007, Room 104, Beltsville, MD 20705. E-mail: tugrul.yilmaz@ars.usda.gov

Current affiliation: Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland.

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Journal of Hydrometeorology

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