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dc.contributor.authorRaboudi, Naila Mohammed Fathi
dc.contributor.authorAit-El-Fquih, Boujemaa
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2018-02-14T11:52:52Z
dc.date.available2018-02-14T11:52:52Z
dc.date.issued2018-01-11
dc.identifier.citationRaboudi NF, Ait-El-Fquih B, Hoteit I (2018) Ensemble Kalman Filtering with One-Step-Ahead Smoothing. Monthly Weather Review 146: 561–581. Available: http://dx.doi.org/10.1175/mwr-d-17-0175.1.
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/mwr-d-17-0175.1
dc.identifier.urihttp://hdl.handle.net/10754/627130
dc.description.abstractThe ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.
dc.description.sponsorshipThis work is supported by King Abdullah University of Science and Technology Award CRG3-2156.
dc.publisherAmerican Meteorological Society
dc.relation.urlhttps://journals.ametsoc.org/doi/10.1175/MWR-D-17-0175.1
dc.rights© Copyright 2018 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.subjectEnsembles
dc.subjectOperational forecasting
dc.subjectData assimilation
dc.titleEnsemble Kalman filtering with one-step-ahead smoothing
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
kaust.personRaboudi, Naila Mohammed Fathi
kaust.personAit-El-Fquih, Boujemaa
kaust.personHoteit, Ibrahim
kaust.grant.numberCRG3-2156
refterms.dateFOA2018-08-13T00:00:00Z
dc.date.published-online2018-01-11
dc.date.published-print2018-02


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