YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Monthly Weather Review
    • View Item
    •   YE&T Library
    • AMS
    • Monthly Weather Review
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Some Observing System Simulation Experiments with a Hybrid 3DEnVAR System for Storm-Scale Radar Data Assimilation

    Source: Monthly Weather Review:;2014:;volume( 142 ):;issue: 009::page 3326
    Author:
    Gao, Jidong
    ,
    Stensrud, David J.
    DOI: 10.1175/MWR-D-14-00025.1
    Publisher: American Meteorological Society
    Abstract: hybrid three-dimensional ensemble?variational data assimilation (3DEnVAR) algorithm is developed based on the 3D variational data assimilation (3DVAR) and ensemble Kalman filter (EnKF) programs with the Advanced Regional Prediction System (ARPS). The method uses the extended control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The method is applied to the assimilation of simulated data from two radars for a supercell storm. Some sensitivity experiments are performed to answer questions about how flow-dependent covariance estimated from the forecast ensemble can be best used in the hybrid 3DEnVAR scheme. When the ensemble size is relatively small (with 5 or 10 ensemble members), it is found that experiments with a weaker weighting value for the ensemble covariance leads to better analysis results. Even when severe sampling errors exist, introducing ensemble-estimated covariances into the variational method still benefits the analysis. For reasonably large ensemble sizes (50?100 members), a stronger relative weighting (>0.8) for the ensemble covariance leads to better analyses from the hybrid 3DEnVAR. In addition, the sensitivity experiments also indicate that the best results are obtained when the number of the augmented control variables is a function of three spatial dimensions and ensemble members, and is the same for all analysis variables.
    • Download: (1.871Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Some Observing System Simulation Experiments with a Hybrid 3DEnVAR System for Storm-Scale Radar Data Assimilation

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4230431
    Collections
    • Monthly Weather Review

    Show full item record

    contributor authorGao, Jidong
    contributor authorStensrud, David J.
    date accessioned2017-06-09T17:31:58Z
    date available2017-06-09T17:31:58Z
    date copyright2014/09/01
    date issued2014
    identifier issn0027-0644
    identifier otherams-86830.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230431
    description abstracthybrid three-dimensional ensemble?variational data assimilation (3DEnVAR) algorithm is developed based on the 3D variational data assimilation (3DVAR) and ensemble Kalman filter (EnKF) programs with the Advanced Regional Prediction System (ARPS). The method uses the extended control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The method is applied to the assimilation of simulated data from two radars for a supercell storm. Some sensitivity experiments are performed to answer questions about how flow-dependent covariance estimated from the forecast ensemble can be best used in the hybrid 3DEnVAR scheme. When the ensemble size is relatively small (with 5 or 10 ensemble members), it is found that experiments with a weaker weighting value for the ensemble covariance leads to better analysis results. Even when severe sampling errors exist, introducing ensemble-estimated covariances into the variational method still benefits the analysis. For reasonably large ensemble sizes (50?100 members), a stronger relative weighting (>0.8) for the ensemble covariance leads to better analyses from the hybrid 3DEnVAR. In addition, the sensitivity experiments also indicate that the best results are obtained when the number of the augmented control variables is a function of three spatial dimensions and ensemble members, and is the same for all analysis variables.
    publisherAmerican Meteorological Society
    titleSome Observing System Simulation Experiments with a Hybrid 3DEnVAR System for Storm-Scale Radar Data Assimilation
    typeJournal Paper
    journal volume142
    journal issue9
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-14-00025.1
    journal fristpage3326
    journal lastpage3346
    treeMonthly Weather Review:;2014:;volume( 142 ):;issue: 009
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian