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dc.contributor.authorLuo, Xiaodong
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-05-14T07:09:52Z
dc.date.available2015-05-14T07:09:52Z
dc.date.issued2011-12
dc.identifier.citationRobust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter 2011, 139 (12):3938 Monthly Weather Review
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/MWR-D-10-05068.1
dc.identifier.urihttp://hdl.handle.net/10754/552781
dc.description.abstractA robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞ filter is more robust than the Kalman filter, in the sense that the estimation error in the H∞ filter in general has a finite growth rate with respect to the uncertainties in assimilation, except for a special case that corresponds to the Kalman filter. The original form of the H∞ filter contains global constraints in time, which may be inconvenient for sequential data assimilation problems. Therefore a variant is introduced that solves some time-local constraints instead, and hence it is called the time-local H∞ filter (TLHF). By analogy to the ensemble Kalman filter (EnKF), the concept of ensemble time-local H∞ filter (EnTLHF) is also proposed. The general form of the EnTLHF is outlined, and some of its special cases are discussed. In particular, it is shown that an EnKF with certain covariance inflation is essentially an EnTLHF. In this sense, the EnTLHF provides a general framework for conducting covariance inflation in the EnKF-based methods. Some numerical examples are used to assess the relative robustness of the TLHF–EnTLHF in comparison with the corresponding KF–EnKF method.
dc.publisherAmerican Meteorological Society
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-10-05068.1
dc.relation.urlhttp://arxiv.org/abs/1108.0158
dc.rights© Copyright 2011 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.subjectFiltering techniques
dc.subjectKalman filters
dc.subjectEnsembles
dc.titleRobust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter
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.identifier.arxividarXiv:1108.0158
kaust.personLuo, Xiaodong
kaust.personHoteit, Ibrahim
dc.versionv1
refterms.dateFOA2018-06-14T07:47:34Z
dc.date.posted2011-07-31


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