Ensemble square-root Kalman filters are currently the computationally most efficient ensemble-based Kalman filter methods. In particular, the Ensemble Transform Kalman Filter (ETKF) is known to provide a minimum ensemble transformation in a very efficient way. In order to further improve the computational efficiency, the Error-Subspace Transform Kalman Filter (ESTKF) was developed. The ESTKF solves the optimization problem of the Kalman filter in the error-subspace that is represented by the ensemble. As the ETKF, the ESTKF provides the minimum ensemble transformation, but at a slightly lower cost. We discuss the ESTKF and its localized counter part the LESTKF using numerical experiments with the parallel data assimilation framework PDAF and models of different complexity.
AWI Organizations > Infrastructure > Computing and Data Centre
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