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    A Physically Based Identification of Vertical Profiles of Reflectivity from Volume Scan Radar Data

    Source: Journal of Applied Meteorology and Climatology:;2013:;volume( 052 ):;issue: 007::page 1645
    Author:
    Kirstetter, Pierre-Emmanuel
    ,
    Andrieu, Hervé
    ,
    Boudevillain, Brice
    ,
    Delrieu, Guy
    DOI: 10.1175/JAMC-D-12-0228.1
    Publisher: American Meteorological Society
    Abstract: he vertical profile of reflectivity (VPR) must be identified to correct estimations of rainfall rates by radar for the nonuniform beam filling associated with the vertical variation of radar reflectivity. A method for identifying VPRs from volumetric radar data is presented that takes into account the radar sampling. Physically based constraints on the vertical structure of rainfall are introduced with simple VPR models within a rainfall classification procedure defining more homogeneous precipitation patterns. The model parameters are identified in the framework of an extended Kalman filter to ensure their temporal consistency. The method is assessed using the dataset from a volume-scanning strategy for radar quantitative precipitation estimation designed in 2002 for the Bollène radar (France). The physical consistency of the retrieved VPR is evaluated. Positive results are obtained insofar as the physically based identified VPR (i) presents physically consistent shapes and characteristics considering beam effects, (ii) shows improved robustness in the difficult radar measurement context of the Cévennes?Vivarais region, and (iii) provides consistent physical insight into the rain field.
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      A Physically Based Identification of Vertical Profiles of Reflectivity from Volume Scan Radar Data

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4217015
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    contributor authorKirstetter, Pierre-Emmanuel
    contributor authorAndrieu, Hervé
    contributor authorBoudevillain, Brice
    contributor authorDelrieu, Guy
    date accessioned2017-06-09T16:49:22Z
    date available2017-06-09T16:49:22Z
    date copyright2013/07/01
    date issued2013
    identifier issn1558-8424
    identifier otherams-74755.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217015
    description abstracthe vertical profile of reflectivity (VPR) must be identified to correct estimations of rainfall rates by radar for the nonuniform beam filling associated with the vertical variation of radar reflectivity. A method for identifying VPRs from volumetric radar data is presented that takes into account the radar sampling. Physically based constraints on the vertical structure of rainfall are introduced with simple VPR models within a rainfall classification procedure defining more homogeneous precipitation patterns. The model parameters are identified in the framework of an extended Kalman filter to ensure their temporal consistency. The method is assessed using the dataset from a volume-scanning strategy for radar quantitative precipitation estimation designed in 2002 for the Bollène radar (France). The physical consistency of the retrieved VPR is evaluated. Positive results are obtained insofar as the physically based identified VPR (i) presents physically consistent shapes and characteristics considering beam effects, (ii) shows improved robustness in the difficult radar measurement context of the Cévennes?Vivarais region, and (iii) provides consistent physical insight into the rain field.
    publisherAmerican Meteorological Society
    titleA Physically Based Identification of Vertical Profiles of Reflectivity from Volume Scan Radar Data
    typeJournal Paper
    journal volume52
    journal issue7
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-12-0228.1
    journal fristpage1645
    journal lastpage1663
    treeJournal of Applied Meteorology and Climatology:;2013:;volume( 052 ):;issue: 007
    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