利用极端依赖性分解欧洲热浪和干旱的空间模式和指数

Q1 Mathematics
Svenja Szemkus, Petra Friederichs
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引用次数: 1

摘要

摘要我们提出了一种分析和紧凑描述大尺度多变量极端天气的方法。利用库利和蒂博(2019)提出的尾部成对依赖矩阵(TPDM)来识别极端事件的空间模式。我们还引入了交叉 TPDM,以识别两个变量中共同的极端事件模式。我们开发了极端模式指数(EPI),以提供基于模式的气温聚合。基于 EPI 的热浪定义能够检测出欧洲最重要的热浪。作为考虑两个变量中同时出现的极端事件的扩展,我们提出了基于阈值的极端模式指数(TEPI),该指数能够捕捉空间极端事件的复合特征。我们研究了不同累积时间的日最高气温和降水不足,发现有证据表明之前的降水不足对热浪的发展有重大影响,而且热浪往往与短期干旱状况同时出现。我们以 2003 年和 2010 年的欧洲热浪为例,说明 TEPI 适合描述热浪的大尺度复合特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency
Abstract. We present a method for the analysis and compact description of large-scale multivariate weather extremes. Spatial patterns of extreme events are identified using the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019). We also introduce the cross-TPDM to identify patterns of common extremes in two variables. An extremal pattern index (EPI) is developed to provide a pattern-based aggregation of temperature. A heat wave definition based on EPI is able to detect the most important heat waves over Europe. As an extension for considering simultaneous extremes in two variables, we propose the threshold-based EPI (TEPI) that captures the compound character of spatial extremes. We investigate daily temperature maxima and precipitation deficits at different accumulation times and find evidence that preceding precipitation deficits have a significant influence on the development of heat waves and that heat waves often co-occur with short-term drought conditions. We exemplarily show for the European heat waves of 2003 and 2010 that TEPI is suitable for describing the large-scale compound character of heat waves.
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来源期刊
Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography Earth and Planetary Sciences-Atmospheric Science
CiteScore
4.80
自引率
0.00%
发文量
9
审稿时长
26 weeks
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