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dc.contributor.authorSong, Hajoon
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorCornuelle, Bruce D.
dc.contributor.authorSubramanian, Aneesh C.
dc.date.accessioned2015-05-14T07:13:57Z
dc.date.available2015-05-14T07:13:57Z
dc.date.issued2010-07
dc.identifier.citationAn Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter 2010, 138 (7):2825 Monthly Weather Review
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/2010MWR2871.1
dc.identifier.urihttp://hdl.handle.net/10754/552800
dc.description.abstractA new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.
dc.publisherAmerican Meteorological Society
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/2010MWR2871.1
dc.rights© Copyright 2010 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.subjectEnsembles
dc.subjectKalman filters
dc.subjectData assimilation
dc.titleAn Adaptive Approach to Mitigate Background Covariance Limitations 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.contributor.institutionScripps Institution of Oceanography, University of California, San Diego, La Jolla, California
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-06-14T06:21:39Z


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