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    Assimilation of multinational radar reflectivity data in a mesoscale model: A proof of concept

    Source: Journal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 006::page 1739
    Author:
    Ridal, Martin
    ,
    Dahlbom, Mats
    DOI: 10.1175/JAMC-D-16-0247.1
    Publisher: American Meteorological Society
    Abstract: adar reflectivity observations have proven to be beneficial for improving the skill of numerical weather prediction (NWP) models. A few countries around the world use radar reflectivity observations in their operational runs. The majority of experiments and usage are still only including the local radar observations from the country in which the forecasts are made. The model domains on the other hand cover areas far greater than this and therefore observations from surrounding countries need to be included. As of today there is no central collection and re-distribution of volume data in Europe. Since a few years back there is a collection and an initiative to harmonize European radar observations but the re-distribution is only of centrally constructed composites. This study describes the efforts to collect volume reflectivity data from several data providers and make them available for use in a NWP model. A preprocessing of the reflectivity data has been set up in order to handle the different incoming data and to make a first data reduction in order for the NWP models to be able to include the new observations. Assimilation experiments have been performed that proves that it is possible to assimilate operational radar reflectivity data from several countries with a neutral to positive impact.
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      Assimilation of multinational radar reflectivity data in a mesoscale model: A proof of concept

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4217758
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    contributor authorRidal, Martin
    contributor authorDahlbom, Mats
    date accessioned2017-06-09T16:51:37Z
    date available2017-06-09T16:51:37Z
    date issued2017
    identifier issn1558-8424
    identifier otherams-75423.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217758
    description abstractadar reflectivity observations have proven to be beneficial for improving the skill of numerical weather prediction (NWP) models. A few countries around the world use radar reflectivity observations in their operational runs. The majority of experiments and usage are still only including the local radar observations from the country in which the forecasts are made. The model domains on the other hand cover areas far greater than this and therefore observations from surrounding countries need to be included. As of today there is no central collection and re-distribution of volume data in Europe. Since a few years back there is a collection and an initiative to harmonize European radar observations but the re-distribution is only of centrally constructed composites. This study describes the efforts to collect volume reflectivity data from several data providers and make them available for use in a NWP model. A preprocessing of the reflectivity data has been set up in order to handle the different incoming data and to make a first data reduction in order for the NWP models to be able to include the new observations. Assimilation experiments have been performed that proves that it is possible to assimilate operational radar reflectivity data from several countries with a neutral to positive impact.
    publisherAmerican Meteorological Society
    titleAssimilation of multinational radar reflectivity data in a mesoscale model: A proof of concept
    typeJournal Paper
    journal volume056
    journal issue006
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-16-0247.1
    journal fristpage1739
    journal lastpage1751
    treeJournal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 006
    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
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