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dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorLuo, Xiaodong
dc.contributor.authorPham, Dinh-Tuan
dc.date.accessioned2015-05-14T07:00:30Z
dc.date.available2015-05-14T07:00:30Z
dc.date.issued2012-02
dc.identifier.citationParticle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters* 2012, 140 (2):528 Monthly Weather Review
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/2011MWR3640.1
dc.identifier.urihttp://hdl.handle.net/10754/552775
dc.description.abstractThis paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an “ensemble of Kalman filters” operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, the authors consider the construction of the PKF through an “ensemble” of ensemble Kalman filters (EnKFs) instead, and call the implementation the particle EnKF (PEnKF). It is shown that different types of the EnKFs can be considered as special cases of the PEnKF. Similar to the situation in the particle filter, the authors also introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.
dc.publisherAmerican Meteorological Society
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/2011MWR3640.1
dc.relation.urlhttp://arxiv.org/abs/1108.0168
dc.rights© Copyright 2012 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyrights@ametsoc.org.
dc.subjectKalman filters
dc.subjectBayesian methods
dc.subjectData assimilation
dc.subjectFiltering techniques
dc.subjectEnsembles
dc.titleParticle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*
dc.typeArticle
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalMonthly Weather Review
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCentre National de la Recherche Scientifique, Grenoble, France
dc.identifier.arxividarXiv:1108.0168
kaust.personHoteit, Ibrahim
kaust.personLuo, Xiaodong
dc.versionv1
refterms.dateFOA2018-06-13T11:19:27Z
dc.date.posted2011-07-31


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