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    EnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF Implementation

    Source: Monthly Weather Review:;2015:;volume( 143 ):;issue: 012::page 4847
    Author:
    Hamrud, Mats
    ,
    Bonavita, Massimo
    ,
    Isaksen, Lars
    DOI: 10.1175/MWR-D-14-00333.1
    Publisher: American Meteorological Society
    Abstract: he desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF. A broad description of the ECMWF EnKF is given in this paper, focusing on highlighting differences compared to standard EnKF practice. In particular, a discussion of the novel algorithm used to control imbalances between the mass and wind fields in the EnKF analysis is given. The scalability and computational properties of the EnKF are reviewed and the implementation choices adopted at ECMWF described. The sensitivity of the ECMWF EnKF to ensemble size, horizontal resolution, and representation of model errors is also discussed. A comparison with 4DVar will be found in Part II of this two-part study.
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      EnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF Implementation

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    contributor authorHamrud, Mats
    contributor authorBonavita, Massimo
    contributor authorIsaksen, Lars
    date accessioned2017-06-09T17:32:41Z
    date available2017-06-09T17:32:41Z
    date copyright2015/12/01
    date issued2015
    identifier issn0027-0644
    identifier otherams-87015.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230638
    description abstracthe desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF. A broad description of the ECMWF EnKF is given in this paper, focusing on highlighting differences compared to standard EnKF practice. In particular, a discussion of the novel algorithm used to control imbalances between the mass and wind fields in the EnKF analysis is given. The scalability and computational properties of the EnKF are reviewed and the implementation choices adopted at ECMWF described. The sensitivity of the ECMWF EnKF to ensemble size, horizontal resolution, and representation of model errors is also discussed. A comparison with 4DVar will be found in Part II of this two-part study.
    publisherAmerican Meteorological Society
    titleEnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF Implementation
    typeJournal Paper
    journal volume143
    journal issue12
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-14-00333.1
    journal fristpage4847
    journal lastpage4864
    treeMonthly Weather Review:;2015:;volume( 143 ):;issue: 012
    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