An Ensemble Kalman-Particle Predictor-Corrector Filter for Non-Gaussian Data Assimilation
arXiv:0812.2290 · doi:10.1007/978-3-642-01973-9_53
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 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.
ICCS 2009, to appear; 9 pages; minor edits