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A hierarchical model for extreme wind speeds

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  • Lee Fawcett
  • David Walshaw

Abstract

Summary. A typical extreme value analysis is often carried out on the basis of simplistic inferential procedures, though the data being analysed may be structurally complex. Here we develop a hierarchical model for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location. A Gaussian model for the random effects exploits the meteorological structure in the data, enabling increased precision for inferences at individual sites and in individual seasons. The Bayesian framework that is adopted is also exploited to obtain predictive return level estimates at each site, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes that are involved.

Suggested Citation

  • Lee Fawcett & David Walshaw, 2006. "A hierarchical model for extreme wind speeds," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(5), pages 631-646, November.
  • Handle: RePEc:bla:jorssc:v:55:y:2006:i:5:p:631-646
    DOI: 10.1111/j.1467-9876.2006.00557.x
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    Cited by:

    1. Ferreira, Ana & de Haan, Laurens & Zhou, Chen, 2012. "Exceedance probability of the integral of a stochastic process," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 241-257.
    2. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.
    3. Tadele Akeba Diriba & Legesse Kassa Debusho, 2020. "Modelling dependency effect to extreme value distributions with application to extreme wind speed at Port Elizabeth, South Africa: a frequentist and Bayesian approaches," Computational Statistics, Springer, vol. 35(3), pages 1449-1479, September.
    4. Petrissa Eckle & Peter Burgherr, 2013. "Bayesian Data Analysis of Severe Fatal Accident Risk in the Oil Chain," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 146-160, January.
    5. Lee Fawcett & David Walshaw, 2014. "Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 959-976, May.
    6. Rose, Stephen & Apt, Jay, 2016. "Quantifying sources of uncertainty in reanalysis derived wind speed," Renewable Energy, Elsevier, vol. 94(C), pages 157-165.
    7. Duca, Victor E.L.A. & Fonseca, Thais C.O. & Cyrino Oliveira, Fernando Luiz, 2022. "Joint modelling wind speed and power via Bayesian Dynamical models," Energy, Elsevier, vol. 247(C).
    8. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.

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