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Directional Modelling of Extreme Wind Speeds

Author

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  • Stuart G. Coles
  • David Walshaw

Abstract

Extreme value models play an important role in assessing the risk to buildings caused by high wind speeds. However, standard procedures take no account of the directional behaviour of the wind. In this paper the extremal properties of the wind process are modelled as a function of direction. This requires the adaptation of techniques which have recently been developed for the study of spatial extremes. In particular, fitting techniques are required which account for dependence across directions, and, for joint probability statements, a model for the dependence itself is required.

Suggested Citation

  • Stuart G. Coles & David Walshaw, 1994. "Directional Modelling of Extreme Wind Speeds," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 139-157, March.
    • Handle: RePEc:bla:jorssc:v:43:y:1994:i:1:p:139-157
    DOI: 10.2307/2986118
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    Citations

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    Cited by:

    1. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
    2. Bi, Wenzhe & Tian, Li & Li, Chao & Ma, Zhen & Pan, Haiyang, 2023. "Wind-induced failure analysis of a transmission tower-line system with long-term measured data and orientation effect," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Łukasz Kuźmiński & Zdzisław Kes & Veselin Draskovic & Andrzej Gawlik & Marcin Rabe & Katarzyna Widera & Agnieszka Łopatka & Maciej Śniegowski, 2023. "Modelling of the Risk of Budget Variances of Cost Energy Consumption Using Probabilistic Quantification," Energies, MDPI, vol. 16(5), pages 1-16, March.
    4. Ahmed, Hanan & Einmahl, John & Zhou, Chen, 2021. "Extreme Value Statistics in Semi-Supervised Models," Discussion Paper 2021-007, Tilburg University, Center for Economic Research.
    5. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    6. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    7. Evandro Konzen & Cláudia Neves & Philip Jonathan, 2021. "Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    8. Madeleine Lopeman & George Deodatis & Guillermo Franco, 2015. "Extreme storm surge hazard estimation in lower Manhattan," 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. 78(1), pages 355-391, August.
    9. Eric Oliver & Jinyu Sheng & Keith Thompson & Jorge Blanco, 2012. "Extreme surface and near-bottom currents in the northwest Atlantic," 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. 64(2), pages 1425-1446, November.

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