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    Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications

    Source: Journal of Applied Meteorology and Climatology:;2014:;volume( 053 ):;issue: 008::page 1901
    Author:
    Badger, Jake
    ,
    Frank, Helmut
    ,
    Hahmann, Andrea N.
    ,
    Giebel, Gregor
    DOI: 10.1175/JAMC-D-13-0147.1
    Publisher: American Meteorological Society
    Abstract: his paper demonstrates that a statistical?dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.
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      Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4217154
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    contributor authorBadger, Jake
    contributor authorFrank, Helmut
    contributor authorHahmann, Andrea N.
    contributor authorGiebel, Gregor
    date accessioned2017-06-09T16:49:47Z
    date available2017-06-09T16:49:47Z
    date copyright2014/08/01
    date issued2014
    identifier issn1558-8424
    identifier otherams-74881.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217154
    description abstracthis paper demonstrates that a statistical?dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.
    publisherAmerican Meteorological Society
    titleWind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications
    typeJournal Paper
    journal volume53
    journal issue8
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-13-0147.1
    journal fristpage1901
    journal lastpage1919
    treeJournal of Applied Meteorology and Climatology:;2014:;volume( 053 ):;issue: 008
    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