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Predicting the Inland Penetration of Long-Lake-Axis-Parallel Snowbands

机译:预测长湖轴平行雪段的内陆渗透

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Predicting the inland penetration of lake-effect long-lake-axis-parallel (LLAP) snowbands is crucial to public safety because LLAP bands can produce hazardous weather well downwind of the parent lake. Accordingly, hypotheses for the variation in inland penetration of LLAP-band radar echoes (InPen) are formulated and tested. The hypothesis testing includes an examination of statistical relationships between environmental variables and InPen for 34 snapshots of LLAP bands observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign. Several previously proposed predictors of LLAP-band formation or InPen demonstrate weak correlations with InPen during OWLeS. A notable exception is convective boundary layer (CBL) depth, which is strongly correlated with InPen. In addition to CBL depth, InPen is strongly correlated with cold-air advection in the upper portion of the CBL, suggesting that boundary layer destabilization produced by vertically differential cold-air advection may be an important inland power source for preexisting LLAP bands. This power production is quantified through atmospheric energetics and the resulting variable, differential thermal advection power (DTAP), yields reasonably skillful predictions of InPen. Nevertheless, an InPen model developed using DTAP is outperformed by an empirical model combining CBL depth and potential temperature advection in the upper portion of the CBL. This two-variable model explains 76% of the observed InPen variance when tested on independent data. Finally, implications for operational forecasting of InPen are discussed.
机译:预测湖泊效应的内陆渗透长湖轴平行(LLAP)雪段对公共安全至关重要,因为LLAP频段可以在父母湖的危险性风中产生危险的天气。因此,配制并测试内陆渗透雷达回波(Inpen)的内陆渗透变化的假设。假设检测包括检查环境变量与34个在安大略湖效应系统(OWLES)现场运动期间观察到的LLAP带的34个快照之间的统计关系。据前提出的LLAP带形成或inpen的预测因子表现出与猫头鹰期间与inpen的弱相关性。值得注意的例外是对流边界层(CBL)深度,其与INPEN强烈相关。除了CBL深度之外,INPEN与CBL的上部的冷空气平流牢固地相关,表明通过垂直差分冷空气平流产生的边界层不稳定可能是用于预先存在的LLAP带的重要内陆电源。通过大气能量,通过大气能量和所得到的变量,差动热平面功率(DTAP)来量化该电力生产,从而产生合理熟练的inpen预测。然而,使用DTAP开发的INPEN模型通过组合CBL深度和CBL的上部中的潜在温度平流的经验模型而表现优异。此两个变量模型在在独立数据上测试时,介绍了观察到的inpen方差的76%。最后,讨论了对inpen的操作预测的影响。

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