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Yilmaz M.T.,George Mason University | Delsole T.,George Mason University | Delsole T.,Virginia Center for Ocean Land Atmosphere Studies | Yilmaz M.T.,U.S. Department of Agriculture
Journal of Hydrometeorology | Year: 2012

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. © 2012 American Meteorological Society. Source

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