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    Scale-Dependent Background Error Covariance Localization: Evaluation in a Global Deterministic Weather Forecasting System

    Source: Monthly Weather Review:;2018:;volume 146:;issue 005::page 1367
    Author:
    Caron, Jean-François
    ,
    Buehner, Mark
    DOI: 10.1175/MWR-D-17-0369.1
    Publisher: American Meteorological Society
    Abstract: AbstractScale-dependent localization (SDL) consists of applying the appropriate (i.e., different) amount of localization to different ranges of background error covariance spatial scales while simultaneously assimilating all of the available observations. The SDL method proposed by Buehner and Shlyaeva for ensemble?variational (EnVar) data assimilation was tested in a 3D-EnVar version of the Canadian operational global data assimilation system. It is shown that a horizontal-scale-dependent horizontal localization leads to implicit vertical-level-dependent, variable-dependent, and location-dependent horizontal localization. The results from data assimilation cycles show that horizontal-scale-dependent horizontal covariance localization is able to improve the forecasts up to day 5 in the Northern Hemisphere extratropical summer period and up to day 7 in the Southern Hemisphere extratropical winter period. In the tropics, use of SDL results in improvements similar to what can be obtained by increasing the uniform amount of spatial localization. An investigation of the dynamical balance in the resulting analysis increments demonstrates that SDL does not further harm the balance between the mass and the rotational wind fields, as compared to the traditional localization approach. Potential future applications for the SDL method are also discussed.
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      Scale-Dependent Background Error Covariance Localization: Evaluation in a Global Deterministic Weather Forecasting System

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261281
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    contributor authorCaron, Jean-François
    contributor authorBuehner, Mark
    date accessioned2019-09-19T10:04:44Z
    date available2019-09-19T10:04:44Z
    date copyright3/23/2018 12:00:00 AM
    date issued2018
    identifier othermwr-d-17-0369.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261281
    description abstractAbstractScale-dependent localization (SDL) consists of applying the appropriate (i.e., different) amount of localization to different ranges of background error covariance spatial scales while simultaneously assimilating all of the available observations. The SDL method proposed by Buehner and Shlyaeva for ensemble?variational (EnVar) data assimilation was tested in a 3D-EnVar version of the Canadian operational global data assimilation system. It is shown that a horizontal-scale-dependent horizontal localization leads to implicit vertical-level-dependent, variable-dependent, and location-dependent horizontal localization. The results from data assimilation cycles show that horizontal-scale-dependent horizontal covariance localization is able to improve the forecasts up to day 5 in the Northern Hemisphere extratropical summer period and up to day 7 in the Southern Hemisphere extratropical winter period. In the tropics, use of SDL results in improvements similar to what can be obtained by increasing the uniform amount of spatial localization. An investigation of the dynamical balance in the resulting analysis increments demonstrates that SDL does not further harm the balance between the mass and the rotational wind fields, as compared to the traditional localization approach. Potential future applications for the SDL method are also discussed.
    publisherAmerican Meteorological Society
    titleScale-Dependent Background Error Covariance Localization: Evaluation in a Global Deterministic Weather Forecasting System
    typeJournal Paper
    journal volume146
    journal issue5
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-17-0369.1
    journal fristpage1367
    journal lastpage1381
    treeMonthly Weather Review:;2018:;volume 146:;issue 005
    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