Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

Handle URI:
http://hdl.handle.net/10754/552734
Title:
Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation
Authors:
Altaf, Muhammad; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
This 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.
KAUST Department:
Water Desalination and Reuse Research Center (WDRC); Physical Sciences and Engineering (PSE) Division
Citation:
Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation 2013, 141 (8):2705 Monthly Weather Review
Journal:
Monthly Weather Review
Issue Date:
Aug-2013
DOI:
10.1175/MWR-D-12-00310.1
Type:
Article
ISSN:
0027-0644; 1520-0493
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00310.1
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Water Desalination and Reuse Research Center (WDRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorAltaf, Muhammaden
dc.contributor.authorButler, T.en
dc.contributor.authorLuo, X.en
dc.contributor.authorDawson, C.en
dc.contributor.authorMayo, T.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-05-14T06:15:32Zen
dc.date.available2015-05-14T06:15:32Zen
dc.date.issued2013-08en
dc.identifier.citationImproving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation 2013, 141 (8):2705 Monthly Weather Reviewen
dc.identifier.issn0027-0644en
dc.identifier.issn1520-0493en
dc.identifier.doi10.1175/MWR-D-12-00310.1en
dc.identifier.urihttp://hdl.handle.net/10754/552734en
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.en
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00310.1en
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.en
dc.subjectShort-range predictionen
dc.subjectOperational forecastingen
dc.subjectEnsemblesen
dc.subjectKalman filtersen
dc.titleImproving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflationen
dc.typeArticleen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalMonthly Weather Reviewen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDelft University of Technology, Delft, Netherlandsen
dc.contributor.institutionInstitute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texasen
dc.contributor.institutionInternational Research Institute of Stavanger, Bergen, Norwayen
kaust.authorAltaf, Muhammaden
kaust.authorHoteit, Ibrahimen
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