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

    Source: Monthly Weather Review:;2013:;volume( 141 ):;issue: 008::page 2705
    Author:
    Altaf, M. U.
    ,
    Butler, T.
    ,
    Luo, X.
    ,
    Dawson, C.
    ,
    Mayo, T.
    ,
    Hoteit, I.
    DOI: 10.1175/MWR-D-12-00310.1
    Publisher: American Meteorological Society
    Abstract: his 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.
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      Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4230084
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    • Monthly Weather Review

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    contributor authorAltaf, M. U.
    contributor authorButler, T.
    contributor authorLuo, X.
    contributor authorDawson, C.
    contributor authorMayo, T.
    contributor authorHoteit, I.
    date accessioned2017-06-09T17:30:47Z
    date available2017-06-09T17:30:47Z
    date copyright2013/08/01
    date issued2013
    identifier issn0027-0644
    identifier otherams-86517.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230084
    description abstracthis 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.
    publisherAmerican Meteorological Society
    titleImproving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation
    typeJournal Paper
    journal volume141
    journal issue8
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-12-00310.1
    journal fristpage2705
    journal lastpage2720
    treeMonthly Weather Review:;2013:;volume( 141 ):;issue: 008
    contenttypeFulltext
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    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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