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    Sampling Errors in Ensemble Kalman Filtering. Part II: Application to a Barotropic Model

    Source: Monthly Weather Review:;2009:;volume( 137 ):;issue: 005::page 1640
    Author:
    Sacher, William
    ,
    Bartello, Peter
    DOI: 10.1175/2008MWR2685.1
    Publisher: American Meteorological Society
    Abstract: In the current study, the authors are concerned with the comparison of the average performance of stochastic versions of the ensemble Kalman filter with and without covariance inflation, as well as the double ensemble Kalman filter. The theoretical results obtained in Part I of this study are confronted with idealized simulations performed with a perfect barotropic quasigeostrophic model. Results obtained are very consistent with the analytic expressions found in Part I. It is also shown that both the double ensemble Kalman filter and covariance inflation techniques can avoid filter divergence. Nevertheless, covariance inflation gives efficient results in terms of accuracy and reliability for a much lower computational cost than the double ensemble Kalman filter and for smaller ensemble sizes.
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      Sampling Errors in Ensemble Kalman Filtering. Part II: Application to a Barotropic Model

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4209502
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    contributor authorSacher, William
    contributor authorBartello, Peter
    date accessioned2017-06-09T16:26:43Z
    date available2017-06-09T16:26:43Z
    date copyright2009/05/01
    date issued2009
    identifier issn0027-0644
    identifier otherams-67994.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4209502
    description abstractIn the current study, the authors are concerned with the comparison of the average performance of stochastic versions of the ensemble Kalman filter with and without covariance inflation, as well as the double ensemble Kalman filter. The theoretical results obtained in Part I of this study are confronted with idealized simulations performed with a perfect barotropic quasigeostrophic model. Results obtained are very consistent with the analytic expressions found in Part I. It is also shown that both the double ensemble Kalman filter and covariance inflation techniques can avoid filter divergence. Nevertheless, covariance inflation gives efficient results in terms of accuracy and reliability for a much lower computational cost than the double ensemble Kalman filter and for smaller ensemble sizes.
    publisherAmerican Meteorological Society
    titleSampling Errors in Ensemble Kalman Filtering. Part II: Application to a Barotropic Model
    typeJournal Paper
    journal volume137
    journal issue5
    journal titleMonthly Weather Review
    identifier doi10.1175/2008MWR2685.1
    journal fristpage1640
    journal lastpage1654
    treeMonthly Weather Review:;2009:;volume( 137 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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