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    Predicting Peak Wind Gusts during Specific Weather Types with the Meteorologically Stratified Gust Factor Model

    Source: Weather and Forecasting:;2022:;volume( 037 ):;issue: 008::page 1435
    Author:
    Victoria A. Lang
    ,
    Teresa J. Turner
    ,
    Brandon R. Selbig
    ,
    Austin R. Harris
    ,
    Jonathan D. W. Kahl
    DOI: 10.1175/WAF-D-21-0201.1
    Publisher: American Meteorological Society
    Abstract: Wind gusts present challenges to operational meteorologists, both to forecast accurately and also to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts. This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for seven specific weather conditions over an 8-yr period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 h. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the “rain with thunder” weather type. The MSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types.
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      Predicting Peak Wind Gusts during Specific Weather Types with the Meteorologically Stratified Gust Factor Model

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4289607
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    contributor authorVictoria A. Lang
    contributor authorTeresa J. Turner
    contributor authorBrandon R. Selbig
    contributor authorAustin R. Harris
    contributor authorJonathan D. W. Kahl
    date accessioned2023-04-12T18:24:26Z
    date available2023-04-12T18:24:26Z
    date copyright2022/08/01
    date issued2022
    identifier otherWAF-D-21-0201.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4289607
    description abstractWind gusts present challenges to operational meteorologists, both to forecast accurately and also to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts. This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for seven specific weather conditions over an 8-yr period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 h. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the “rain with thunder” weather type. The MSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types.
    publisherAmerican Meteorological Society
    titlePredicting Peak Wind Gusts during Specific Weather Types with the Meteorologically Stratified Gust Factor Model
    typeJournal Paper
    journal volume37
    journal issue8
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF-D-21-0201.1
    journal fristpage1435
    journal lastpage1446
    page1435–1446
    treeWeather and Forecasting:;2022:;volume( 037 ):;issue: 008
    contenttypeFulltext
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