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dc.contributor.authorAltaf, Muhammad
dc.contributor.authorButler, T.
dc.contributor.authorLuo, X.
dc.contributor.authorDawson, C.
dc.contributor.authorMayo, T.
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-05-14T06:15:32Z
dc.date.available2015-05-14T06:15:32Z
dc.date.issued2013-08
dc.identifier.citationImproving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation 2013, 141 (8):2705 Monthly Weather Review
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/MWR-D-12-00310.1
dc.identifier.urihttp://hdl.handle.net/10754/552734
dc.description.abstractThis paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.
dc.publisherAmerican Meteorological Society
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00310.1
dc.rights© Copyright 2013 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.subjectShort-range prediction
dc.subjectOperational forecasting
dc.subjectEnsembles
dc.subjectKalman filters
dc.titleImproving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation
dc.typeArticle
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.identifier.journalMonthly Weather Review
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDelft University of Technology, Delft, Netherlands
dc.contributor.institutionInstitute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas
dc.contributor.institutionInternational Research Institute of Stavanger, Bergen, Norway
kaust.personAltaf, Muhammad
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-06-14T07:47:01Z


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