A Gaussian-mixture ensemble transform filter
- 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.
Author details: | Sebastian ReichORCiDGND |
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DOI: | https://doi.org/10.1002/qj.898 |
ISSN: | 0035-9009 |
Title of parent work (English): | Quarterly journal of the Royal Meteorological Society |
Publisher: | Wiley-Blackwell |
Place of publishing: | Malden |
Publication type: | Article |
Language: | English |
Year of first publication: | 2012 |
Publication year: | 2012 |
Release date: | 2017/03/26 |
Tag: | Gaussian kernel estimators; Gaussian mixtures; data assimilation; ensemble Kalman filter; nonlinear filtering |
Volume: | 138 |
Issue: | 662 |
Number of pages: | 12 |
First page: | 222 |
Last Page: | 233 |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
Peer review: | Referiert |