IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v131y2019icp176-193.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947318301701
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2018.07.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Horváth, Lajos & Kokoszka, Piotr & Wang, Shixuan, 2020. "Testing normality of data on a multivariate grid," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:131:y:2019:i:c:p:176-193. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.