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    A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography

    Source: Monthly Weather Review:;2008:;volume( 136 ):;issue: 001::page 317
    Author:
    Hoteit, I.
    ,
    Pham, D-T.
    ,
    Triantafyllou, G.
    ,
    Korres, G.
    DOI: 10.1175/2007MWR1927.1
    Publisher: American Meteorological Society
    Abstract: This paper introduces a new approximate solution of the optimal nonlinear filter suitable for nonlinear oceanic and atmospheric data assimilation problems. The method is based on a local linearization in a low-rank kernel representation of the state?s probability density function. In the resulting low-rank kernel particle Kalman (LRKPK) filter, the standard (weight type) particle filter correction is complemented by a Kalman-type correction for each particle using the covariance matrix of the kernel mixture. The LRKPK filter?s solution is then obtained as the weighted average of several low-rank square root Kalman filters operating in parallel. The Kalman-type correction reduces the risk of ensemble degeneracy, which enables the filter to efficiently operate with fewer particles than the particle filter. Combined with the low-rank approximation, it allows the implementation of the LRKPK filter with high-dimensional oceanic and atmospheric systems. The new filter is described and its relevance demonstrated through applications with the simple Lorenz model and a realistic configuration of the Princeton Ocean Model (POM) in the Mediterranean Sea.
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      A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4207510
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    contributor authorHoteit, I.
    contributor authorPham, D-T.
    contributor authorTriantafyllou, G.
    contributor authorKorres, G.
    date accessioned2017-06-09T16:20:49Z
    date available2017-06-09T16:20:49Z
    date copyright2008/01/01
    date issued2008
    identifier issn0027-0644
    identifier otherams-66201.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4207510
    description abstractThis paper introduces a new approximate solution of the optimal nonlinear filter suitable for nonlinear oceanic and atmospheric data assimilation problems. The method is based on a local linearization in a low-rank kernel representation of the state?s probability density function. In the resulting low-rank kernel particle Kalman (LRKPK) filter, the standard (weight type) particle filter correction is complemented by a Kalman-type correction for each particle using the covariance matrix of the kernel mixture. The LRKPK filter?s solution is then obtained as the weighted average of several low-rank square root Kalman filters operating in parallel. The Kalman-type correction reduces the risk of ensemble degeneracy, which enables the filter to efficiently operate with fewer particles than the particle filter. Combined with the low-rank approximation, it allows the implementation of the LRKPK filter with high-dimensional oceanic and atmospheric systems. The new filter is described and its relevance demonstrated through applications with the simple Lorenz model and a realistic configuration of the Princeton Ocean Model (POM) in the Mediterranean Sea.
    publisherAmerican Meteorological Society
    titleA New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography
    typeJournal Paper
    journal volume136
    journal issue1
    journal titleMonthly Weather Review
    identifier doi10.1175/2007MWR1927.1
    journal fristpage317
    journal lastpage334
    treeMonthly Weather Review:;2008:;volume( 136 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian