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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.

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Metadaten
Author:Sebastian ReichORCiD
DOI:https://doi.org/10.1002/qj.898
ISSN:0035-9009 (print)
Parent Title (English):Quarterly journal of the Royal Meteorological Society
Publisher:Wiley-Blackwell
Place of publication:Malden
Document Type:Article
Language:English
Year of first Publication:2012
Year of Completion:2012
Release Date:2017/03/26
Tag:Gaussian kernel estimators; Gaussian mixtures; data assimilation; ensemble Kalman filter; nonlinear filtering
Volume:138
Issue:662
Pagenumber:12
First Page:222
Last Page:233
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
Peer Review:Referiert