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dc.contributor.authorAit-El-Fquih, Boujemaa
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2018-04-10T08:38:05Z
dc.date.available2018-04-10T08:38:05Z
dc.date.issued2017-12-11
dc.identifier.citationAit-El-Fquih B, Hoteit I (2018) An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters. Monthly Weather Review 146: 871–887. Available: http://dx.doi.org/10.1175/MWR-D-16-0485.1.
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/MWR-D-16-0485.1
dc.identifier.urihttp://hdl.handle.net/10754/627424
dc.description.abstractThis work addresses the state-parameter filtering problem for dynamical systems with relatively large-dimensional state and low-dimensional parameters' vector. A Bayesian filtering algorithm combining the strengths of the particle filter (PF) and the ensemble Kalman filter (EnKF) is proposed. At each assimilation cycle of the proposed EnKF-PF, the PF is first used to sample the parameters' ensemble followed by the EnKF to compute the state ensemble conditional on the resulting parameters' ensemble. The proposed scheme is expected to be more efficient than the traditional state augmentation techniques, which suffer from the curse of dimensionality and inconsistency that is particularly pronounced when the state is a strongly nonlinear function of the parameters. In the new scheme, the EnKF and PF interact via their ensembles' members, in contrast with the recently introduced two-stage EnKF-PF (TS-EnKF-PF), which exchanges point estimates between EnKF and PF while requiring almost double the computational load. Numerical experiments are conducted with the Lorenz-96 model to assess the behavior of the proposed filter and to evaluate its performances against the joint PF, joint EnKF, and TS-EnKF-PF. Numerical results suggest that the EnKF-PF performs best in all tested scenarios. It was further found to be more robust, successfully estimating both state and parameters in different sensitivity experiments.
dc.description.sponsorshipResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).
dc.publisherAmerican Meteorological Society
dc.relation.urlhttps://journals.ametsoc.org/doi/10.1175/MWR-D-16-0485.1
dc.rights© Copyright 2017 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.subjectBayesian methods
dc.subjectData assimilation
dc.subjectFiltering techniques
dc.subjectKalman filters
dc.subjectNonlinear models
dc.titleAn Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle 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.versionPost-print
kaust.personAit-El-Fquih, Boujemaa
kaust.personHoteit, Ibrahim
refterms.dateFOA2019-03-27T00:00:00Z
dc.date.published-online2017-12-11
dc.date.published-print2018-03


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