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A hybrid particle-ensemble Kalman filter for problems with medium nonlinearity | | Gregor Robinson
; Ian Grooms
; | | Date: |
8 Jun 2020 | | Abstract: | A hybrid particle ensemble Kalman filter is developed for problems with
medium non-Gaussianity, i.e. problems where the prior is very non-Gaussian but
the posterior is approximately Gaussian. Such situations arise, e.g., when
nonlinear dynamics produce a non-Guassian forecast but a tight Gaussian
likelihood leads to a nearly-Gaussian posterior. The hybrid filter starts by
factoring the likelihood. First the particle filter assimilates the
observations with one factor of the likelihood to produce an intermediate prior
that is close to Gaussian, and then the ensemble Kalman filter completes the
assimilation with the remaining factor. How the likelihood gets split between
the two stages is determined in such a way to ensure that the particle filter
avoids collapse, and particle degeneracy is broken by a mean-preserving random
orthogonal transformation. The hybrid is tested in a multiscale system of ODEs
motivated by the Lorenz-’96 model, where it is shown to outperform a pure
ensemble Kalman filter, provided that the ensemble size is large enough. | | Source: | arXiv, 2006.4699 | | Services: | Forum | Review | PDF | Favorites | |
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