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    Combined Kalman Filter and Universal Kriging to Improve Storm Wind Speed Predictions for the Northeastern United States

    Source: Weather and Forecasting:;2019:;volume 034:;issue 003::page 587
    Author:
    Samalot, Alexander
    ,
    Astitha, Marina
    ,
    Yang, Jaemo
    ,
    Galanis, George
    DOI: 10.1175/WAF-D-18-0068.1
    Publisher: American Meteorological Society
    Abstract: AbstractThe scope of this study is to assess a combination of well-known techniques for bias reduction and spatial interpolation in an attempt to improve wind speed prediction for storms on a gridded domain. This is accomplished by implementing Kalman filter (KF) for bias reduction and universal kriging (UK) for spatial interpolation as postprocessing steps for the Weather Research and Forecasting (WRF) Model. It is shown that for surface wind speed, a linear KF is adequate for eliminating systematic model errors with the available storm history. KF-estimated wind speed biases at station locations are then interpolated across the model domain using UK. The combined KF?UK approach improves the wind speed forecast median bias by 55% and RMSE by 15% (bulk statistics), while benefits obtained at station-specific locations can reach maximum improvements of 72% for RMSE and 100% for bias. Contingency statistics that inform on model performance over four categories of wind speed magnitude reveal that calm/moderate winds are successfully corrected but strong/gale winds cannot be adequately corrected by the combination of KF and UK, which is a disadvantage for improving prediction of severe storm conditions.
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      Combined Kalman Filter and Universal Kriging to Improve Storm Wind Speed Predictions for the Northeastern United States

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4263268
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    contributor authorSamalot, Alexander
    contributor authorAstitha, Marina
    contributor authorYang, Jaemo
    contributor authorGalanis, George
    date accessioned2019-10-05T06:44:20Z
    date available2019-10-05T06:44:20Z
    date copyright4/5/2019 12:00:00 AM
    date issued2019
    identifier otherWAF-D-18-0068.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4263268
    description abstractAbstractThe scope of this study is to assess a combination of well-known techniques for bias reduction and spatial interpolation in an attempt to improve wind speed prediction for storms on a gridded domain. This is accomplished by implementing Kalman filter (KF) for bias reduction and universal kriging (UK) for spatial interpolation as postprocessing steps for the Weather Research and Forecasting (WRF) Model. It is shown that for surface wind speed, a linear KF is adequate for eliminating systematic model errors with the available storm history. KF-estimated wind speed biases at station locations are then interpolated across the model domain using UK. The combined KF?UK approach improves the wind speed forecast median bias by 55% and RMSE by 15% (bulk statistics), while benefits obtained at station-specific locations can reach maximum improvements of 72% for RMSE and 100% for bias. Contingency statistics that inform on model performance over four categories of wind speed magnitude reveal that calm/moderate winds are successfully corrected but strong/gale winds cannot be adequately corrected by the combination of KF and UK, which is a disadvantage for improving prediction of severe storm conditions.
    publisherAmerican Meteorological Society
    titleCombined Kalman Filter and Universal Kriging to Improve Storm Wind Speed Predictions for the Northeastern United States
    typeJournal Paper
    journal volume34
    journal issue3
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF-D-18-0068.1
    journal fristpage587
    journal lastpage601
    treeWeather and Forecasting:;2019:;volume 034:;issue 003
    contenttypeFulltext
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