Reducing water imbalance in land data assimilation: Ensemble filtering without perturbed observations

Date

2012-02-01

Author

Yılmaz, Mustafa Tuğrul
Delsole, Timothy
Yilmaz, M. Tugrul

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Atmospheric Science

URI

https://hdl.handle.net/11511/36823

Collections

Department of Civil Engineering, Article