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Environmental Covariate Representation of Seasonal US Tornado Frequency

机译:环境协变量季节性美国龙卷风频率代表

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摘要

The significant tornado parameter is a widely used meteorological composite index that combines several variables known to favor tornadic supercell thunderstorms. This research examines the spatial relationship between U.S. tornado frequency and the significant tornado parameter (the predictor covariate) across four seasons in order to establish a spatial-statistical model that explains significant amounts of variance in tornado occurrence (the predictand). U.S. tornadoes are highly dependent on the significant tornado parameter in a climatological sense. The strength of this dependence is seasonal, with greatest dependence found during December-February and least dependence during June-August. Additionally, the strength of this dependence has not changed significantly through the 39-yr study period (1979-2017). Results herein represent an important step forward for the creation of a predictive spatial-statistical model to aid in tornado prediction at seasonal time scales.
机译:重要的龙卷风参数是一种广泛使用的气象复合指标,该指数结合了已知的几个变量,以支持龙王超级雷暴。 该研究探讨了美国龙卷风频率与四季的重要龙卷风参数(预测因子协变量)之间的空间关系,以建立空间统计模型,解释了龙卷风发生的大量方差(预测和)。 美国龙卷风高度依赖于气候感的重要龙卷风参数。 这一依赖的力量是季节性的,在12月至2月至8月期间发现最大的依赖性和最不依赖的依赖。 此外,通过39年的研究期(1979-2017),这种依赖的强度并没有显着变化。 这里的结果代表了创建预测空间统计模型的重要一步,以帮助在季节性时间尺度达到龙卷风预测。

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