Gain form of the Ensemble Transform Kalman Filter and its relevance to satellite data assimilation with model space ensemble covariance localization

Gain form of the Ensemble Transform Kalman Filter and its relevance to satellite data... AbstractTo ameliorate sub-optimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble based satellite data assimilation. In the case of the Ensemble Transform Kalman Filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multi-spectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the Gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an EnSRF using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz 96 model, the GETKF analysis Root Mean Square Error (RMSE) matches EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSE for longer localization length scales. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Gain form of the Ensemble Transform Kalman Filter and its relevance to satellite data assimilation with model space ensemble covariance localization

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
D.O.I.
10.1175/MWR-D-17-0102.1
Publisher site
See Article on Publisher Site

Abstract

AbstractTo ameliorate sub-optimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble based satellite data assimilation. In the case of the Ensemble Transform Kalman Filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multi-spectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the Gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an EnSRF using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz 96 model, the GETKF analysis Root Mean Square Error (RMSE) matches EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSE for longer localization length scales.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Aug 16, 2017

References

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