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A Multivariate Index for Ranking and Classifying Severe Weather Outbreaks

机译:用于对严重天气暴发进行排名和分类的多元指数

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In previous work, severe weather outbreaks have been classified either as major tornado outbreaks or as primarily nontornadic outbreaks, but the large majority of such events are of a mixed character.? This study proposes a reproducible method for ranking all types of severe weather outbreaks from the period 1960-2006.? Numerous nonmeteorological artifacts exist in the severe weather reports archived during this period, and many of the variables used to formulate the multivariate indices had to be detrended to reduce the effect of secular trends.? The resulting outbreak rankings indicate that the methodology presented herein is able to distinguish the most significant severe weather outbreaks from intermediate outbreak days and days with a large amount of geographic scatter in the severe reports.? The rankings of the most severe outbreaks and those outbreak days with a large degree of spatial scatter exhibit only limited variability when the selection of parameters and their weights are modified, but a relatively high degree of volatility is noted with the intermediate cases.? This result suggests there is relatively little difference in the severity of these intermediate events.? However, the particular modes of severe weather in these events can be quite different.? A k-means cluster analysis of the outbreak days, using a four-dimensional representation of the multivariate indices developed, indicates that outbreak days can be separated into five groups:? major tornado, wind-dominated, hail-dominated, multi-modal, and days with considerable spatial scatter of the severe reports.
机译:在以前的工作中,恶劣的天气暴发被分类为主要的龙卷风暴发或主要为非暴发性暴发,但大多数此类事件具有混杂特征。这项研究提出了一种可重现的方法,用于对1960-2006年期间所有类型的严重天气暴发进行排名。在此期间存档的恶劣天气报告中存在许多非气象文物,为减少长期趋势的影响,必须对用于制定多元指数的许多变量进行趋势处理。由此产生的暴发排名表明,此处介绍的方法能够区分最严重的暴发性暴发与中等暴发天数以及在严重报告中具有大量地理分布的几天。当修改参数的选择及其权重时,最严重的暴发和那些具有较大空间分散性的暴发日的排名仅表现出有限的可变性,但是在中间情况下,波动性相对较高。该结果表明这些中间事件的严重程度差异相对较小。但是,这些事件中恶劣天气的特殊模式可能会大不相同。对爆发天数的k-均值聚类分析,使用所开发多元指数的四维表示,表明爆发天数可分为五组:主要的龙卷风,风为主,冰雹为主,多式联运,以及几天的时间,严重报告的空间分散。

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