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

机译:圣地亚哥县的下坡风风暴。 第二部分:物理集合分析和阵风预测

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The "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.
机译:南加州的“圣诞老人Ana”风代表着一个高影响的天气事件,因为他们的干燥,快风可以显着提升野火威胁。这一高度或更大强度六场比例的高分辨率数值研究采用物理参数化和随机扰动确定用于预测SAN Diego County的风的最佳模型配置,验证针对San Diego Gas和Electric(SDG&E)Mesonet的观察结果进行了验证。结果证明了模型物理可以对强度,位置和时机具有材料影响随着陆地面积模型通过其表面粗糙度长度的规范来发挥外出作用。即使网络平均持续的风预测中的偏差最小化,仍然存在系统的偏差,即许多车站一直在或不适合。论证是表示这是一个“不可避免的”错误,代表局部风速计曝光问题通过站阵风因素透露。基于网络平均阵风因子与天气条件无关的发现,提出了一个非常简单的阵风参数化,并导致各个站点和媒体上的阵风预测。结合大气湿度和燃油湿度信息,阵风预测可以帮助评估野火风险。

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