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Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data

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  • French, Joshua
  • Kokoszka, Piotr
  • Stoev, Stilian
  • Hall, Lauren

Abstract

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.

Suggested Citation

  • French, Joshua & Kokoszka, Piotr & Stoev, Stilian & Hall, Lauren, 2019. "Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 176-193.
    • Handle: RePEc:eee:csdana:v:131:y:2019:i:c:p:176-193
    DOI: 10.1016/j.csda.2018.07.004
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    References listed on IDEAS

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    1. Anthony Zullo & Mathieu Fauvel & Frédéric Ferraty, 2018. "Experimental comparison of functional and multivariate spectral-based supervised classification methods in hyperspectral image," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(12), pages 2219-2237, September.
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    Cited by:

    1. Dechao Chen & Xinliang Xu & Zongyao Sun & Luo Liu & Zhi Qiao & Tai Huang, 2019. "Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China," Sustainability, MDPI, vol. 12(1), pages 1-15, December.
    2. Paul L. Anderson & Farzad Sabzikar & Mark M. Meerschaert, 2021. "Parsimonious time series modeling for high frequency climate data," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 442-470, July.
    3. Horváth, Lajos & Kokoszka, Piotr & Wang, Shixuan, 2020. "Testing normality of data on a multivariate grid," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    4. Nurulkamal Masseran & Muhammad Aslam Mohd Safari, 2022. "Statistical Modeling on the Severity of Unhealthy Air Pollution Events in Malaysia," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    5. Xiaohan Wu & Yongming Xu & Huijuan Chen, 2020. "Study on the Spatial Pattern of an Extreme Heat Event by Remote Sensing: A Case Study of the 2013 Extreme Heat Event in the Yangtze River Delta, China," Sustainability, MDPI, vol. 12(11), pages 1-16, May.

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