Wind class sampling of satellite SAR imagery for offshore wind resource mapping

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical-dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter are within 5% from the mast observations whereas the deviation on power density and the Weibull shape parameter is up to 7%. These results are promising and may be improved further through a better population of the wind classes. Advantages of the wind class sampling method over random sampling include, in principle, selection of the most representative SAR scenes such that wind resources can be predicted from a lower number of SAR samples. Further, the wind class weightings can be adjusted to represent any time period.
    Original languageEnglish
    JournalJournal of Applied Meteorology and Climatology
    Volume49
    Issue number12
    Pages (from-to)2474-2491
    ISSN1558-8424
    DOIs
    Publication statusPublished - 2010

    Keywords

    • Wind power meteorology
    • Wind Energy

    Cite this

    @article{43ec1282a62646abbf9bbbf64f5e275e,
    title = "Wind class sampling of satellite SAR imagery for offshore wind resource mapping",
    abstract = "High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical-dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter are within 5{\%} from the mast observations whereas the deviation on power density and the Weibull shape parameter is up to 7{\%}. These results are promising and may be improved further through a better population of the wind classes. Advantages of the wind class sampling method over random sampling include, in principle, selection of the most representative SAR scenes such that wind resources can be predicted from a lower number of SAR samples. Further, the wind class weightings can be adjusted to represent any time period.",
    keywords = "Wind power meteorology, Wind Energy, Vindkraftmeteorologi, Vindenergi",
    author = "Merete Badger and Jake Badger and Morten Nielsen and Hasager, {Charlotte Bay} and {Pena Diaz}, Alfredo",
    year = "2010",
    doi = "10.1175/2010JAMC2523.1",
    language = "English",
    volume = "49",
    pages = "2474--2491",
    journal = "Journal of Applied Meteorology and Climatology",
    issn = "1558-8424",
    publisher = "American Meteorological Society",
    number = "12",

    }

    TY - JOUR

    T1 - Wind class sampling of satellite SAR imagery for offshore wind resource mapping

    AU - Badger, Merete

    AU - Badger, Jake

    AU - Nielsen, Morten

    AU - Hasager, Charlotte Bay

    AU - Pena Diaz, Alfredo

    PY - 2010

    Y1 - 2010

    N2 - High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical-dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter are within 5% from the mast observations whereas the deviation on power density and the Weibull shape parameter is up to 7%. These results are promising and may be improved further through a better population of the wind classes. Advantages of the wind class sampling method over random sampling include, in principle, selection of the most representative SAR scenes such that wind resources can be predicted from a lower number of SAR samples. Further, the wind class weightings can be adjusted to represent any time period.

    AB - High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical-dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter are within 5% from the mast observations whereas the deviation on power density and the Weibull shape parameter is up to 7%. These results are promising and may be improved further through a better population of the wind classes. Advantages of the wind class sampling method over random sampling include, in principle, selection of the most representative SAR scenes such that wind resources can be predicted from a lower number of SAR samples. Further, the wind class weightings can be adjusted to represent any time period.

    KW - Wind power meteorology

    KW - Wind Energy

    KW - Vindkraftmeteorologi

    KW - Vindenergi

    U2 - 10.1175/2010JAMC2523.1

    DO - 10.1175/2010JAMC2523.1

    M3 - Journal article

    VL - 49

    SP - 2474

    EP - 2491

    JO - Journal of Applied Meteorology and Climatology

    JF - Journal of Applied Meteorology and Climatology

    SN - 1558-8424

    IS - 12

    ER -