Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data
Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data
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DOI：
10.1016/j.csda.2018.07.004
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
J. French;P. Kokoszka;Stilian A. Stoev;Lauren Hall
中科院分区：
文献类型：
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作者：
J. French;P. Kokoszka;Stilian A. Stoev;Lauren Hall
热浪和其他极端天气事件由于其社会经济影响和与气候变化的关系而引起了极大的关注。热浪是通过一个通用的损失函数来定义的，该函数捕捉了它的振幅、时间持续性和空间范围。拟议的统计框架是极值理论（EVT）和功能数据分析（FDA）的纽带，并能够计算尚未观察到的罕见事件的概率，这些事件在历史记录中看不到。使用了北美区域气候变化评估计划的数据，该计划已经对北美大部分地区的当前和未来温度进行了计算机模型预测。该方法允许计算任何预先指定的时间持续时间，空间范围和整体幅度的热浪的概率。它可以应用于计算其他极端天气事件的概率，包括寒冷和干旱。
Heat waves and other extreme weather events have attracted a great deal of attention due to their socioeconomic impacts and relation to climate change. A heat wave is defined through a general loss function that captures its amplitude, temporal persistence, and spatial extent. The proposed statistical framework is at the nexus of extreme value theory (EVT) and functional data analysis (FDA) and enables computation of probabilities of yet unobserved rare events that are not seen in historical records. Data from the North American Regional Climate Change Assessment Program, which has produced computer model predictions of current and future temperatures across much of North America, are used. The approach allows for the computation of probabilities for heat waves of any pre-specified temporal duration, spatial extent, and overall magnitude. It can be applied to the computation of probabilities of other extreme weather events, including cold spells and droughts.