An Ensemble Kalman-Particle Predictor-Corrector Filter for Non-Gaussian Data Assimilation

Authors:
Jan Mandel;Jonathan D. Beezley
Affiliations:
University of Colorado Denver, Denver, USA CO 80217-3364 and National Center for Atmospheric Research, Boulder, USA CO 80307-3000;University of Colorado Denver, Denver, USA CO 80217-3364 and National Center for Atmospheric Research, Boulder, USA CO 80307-3000
Venue:
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Year:
2009

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Abstract

An Ensemble Kalman Filter (EnKF, the predictor) is used make a large change in the state, followed by a Particle Filer (PF, the corrector), which assigns importance weights to describe a non-Gaussian distribution. The importance weights are obtained by nonparametric density estimation. It is demonstrated on several numerical examples that the new predictor-corrector filter combines the advantages of the EnKF and the PF and that it is suitable for high dimensional states which are discretizations of solutions of partial differential equations.