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A Gaussian-mixture ensemble transform filter
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DOI：10.1002/qj.898.png)
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Abstract
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We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior distribution can take the form of a Gaussian mixture or a Gaussian kernel density estimator. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian-mixture filter (EGMF). The EGMF is implemented for three simple test problems (Brownian dynamics in one dimension, Langevin dynamics in two dimensions and the three-dimensional Lorenz-63 model). It is demonstrated that the EGMF is capable of tracking systems with non-Gaussian uni- and multimodal ensemble distributions. Copyright (C) 2011 Royal Meteorological Society
Keywords:
data assimilation
ensemble Kalman filter
nonlinear filtering
Gaussian mixtures
Gaussian kernel estimators
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