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

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

2008-12-12

arxiv.org/abs/0812.2290v3

Lecture Notes in Computer Science 5545, 470-478, 2009

Statistics

Computation

ICCS 2009, to appear; 9 pages; minor edits

Scientific paper

10.1007/978-3-642-01973-9_53

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 non-Gaussian distribution. The 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.

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