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    Downslope Windstorms of San Diego County. Part II: Physics Ensemble Analyses and Gust Forecasting

    Source: Weather and Forecasting:;2018:;volume 033:;issue 002::page 539
    Author:
    Cao, Yang
    ,
    Fovell, Robert G.
    DOI: 10.1175/WAF-D-17-0177.1
    Publisher: American Meteorological Society
    Abstract: AbstractThe ?Santa Ana? winds of Southern California represent a high-impact weather event because their dry, fast winds can significantly elevate the wildfire threat. This high-resolution numerical study of six events of moderate or greater strength employs physics parameterization and stochastic perturbation ensembles to determine the optimal model configuration for predicting winds in San Diego County, with verification performed against observations from the San Diego Gas and Electric (SDG&E) mesonet. Results demonstrate model physics can have a material effect on the strength, location, and timing of the winds, with the land surface model playing an outsized role via its specification of surface roughness lengths. Even when bias in the network-averaged sustained wind forecasts is minimized, systematic biases remain in that many stations are consistently over- or underforecasted. The argument is made that this is an ?unavoidable? error that represents localized anemometer exposure issues revealed through the station gust factor. A very simple gust parameterization is proposed for the mesonet based on the discovery that the network-averaged gust factor is independent of weather conditions and results in unbiased forecasts of gusts at individual stations and the mesonet as a whole. Combined with atmospheric humidity and fuel moisture information, gust forecasts can help in the assessment of wildfire risks.
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      Downslope Windstorms of San Diego County. Part II: Physics Ensemble Analyses and Gust Forecasting

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261407
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    contributor authorCao, Yang
    contributor authorFovell, Robert G.
    date accessioned2019-09-19T10:05:26Z
    date available2019-09-19T10:05:26Z
    date copyright2/13/2018 12:00:00 AM
    date issued2018
    identifier otherwaf-d-17-0177.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261407
    description abstractAbstractThe ?Santa Ana? winds of Southern California represent a high-impact weather event because their dry, fast winds can significantly elevate the wildfire threat. This high-resolution numerical study of six events of moderate or greater strength employs physics parameterization and stochastic perturbation ensembles to determine the optimal model configuration for predicting winds in San Diego County, with verification performed against observations from the San Diego Gas and Electric (SDG&E) mesonet. Results demonstrate model physics can have a material effect on the strength, location, and timing of the winds, with the land surface model playing an outsized role via its specification of surface roughness lengths. Even when bias in the network-averaged sustained wind forecasts is minimized, systematic biases remain in that many stations are consistently over- or underforecasted. The argument is made that this is an ?unavoidable? error that represents localized anemometer exposure issues revealed through the station gust factor. A very simple gust parameterization is proposed for the mesonet based on the discovery that the network-averaged gust factor is independent of weather conditions and results in unbiased forecasts of gusts at individual stations and the mesonet as a whole. Combined with atmospheric humidity and fuel moisture information, gust forecasts can help in the assessment of wildfire risks.
    publisherAmerican Meteorological Society
    titleDownslope Windstorms of San Diego County. Part II: Physics Ensemble Analyses and Gust Forecasting
    typeJournal Paper
    journal volume33
    journal issue2
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF-D-17-0177.1
    journal fristpage539
    journal lastpage559
    treeWeather and Forecasting:;2018:;volume 033:;issue 002
    contenttypeFulltext
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