globalchange  > 气候变化事实与影响
DOI: 10.1016/j.csda.2018.07.004
WOS记录号: WOS:000450384700015
论文题名:
Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data
作者: French, Joshua1; Kokoszka, Piotr2; Stoev, Stilian3; Hall, Lauren1
通讯作者: Kokoszka, Piotr
刊名: COMPUTATIONAL STATISTICS & DATA ANALYSIS
ISSN: 0167-9473
EISSN: 1872-7352
出版年: 2019
卷: 131, 页码:176-193
语种: 英语
英文关键词: Extreme events ; Functional data ; Elsevier ; Heat waves ; Spatio-temporal analysis
WOS关键词: MODEL
WOS学科分类: Computer Science, Interdisciplinary Applications ; Statistics & Probability
WOS研究方向: Computer Science ; Mathematics
英文摘要:

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. (C) 2018 Elsevier B.V. All rights reserved.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/131570
Appears in Collections:气候变化事实与影响

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作者单位: 1.Univ Colorado, Boulder, CO 80309 USA
2.Colorado State Univ, Ft Collins, CO 80523 USA
3.Univ Michigan, Ann Arbor, MI 48109 USA

Recommended Citation:
French, Joshua,Kokoszka, Piotr,Stoev, Stilian,et al. Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2019-01-01,131:176-193
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