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    Spatial Bayesian hierarchical modelling of extreme sea states

    Clancy, Colm and O'Sullivan, John and Sweeney, Conor and Dias, Frederic and Parnell, Andrew (2016) Spatial Bayesian hierarchical modelling of extreme sea states. Ocean Modelling, 107. pp. 1-13. ISSN 1463-5003

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    A Bayesian hierarchical framework is used to model extreme sea states, incorporating a latent spatial process to more effectively capture the spatial variation of the extremes. The model is applied to a 34-year hindcast of significant wave height off the west coast of Ireland. The generalised Pareto distribution is fitted to declustered peaks over a threshold given by the 99.8th percentile of the data. Return levels of significant wave height are computed and compared against those from a model based on the commonly-used maximum likelihood inference method. The Bayesian spatial model produces smoother maps of return levels. Furthermore, this approach greatly reduces the uncertainty in the estimates, thus providing information on extremes which is more useful for practical applications.

    Item Type: Article
    Additional Information: This is the preprint version of the published article. Cite as: Citation: Clancy, Colm, John O’Sullivan, Conor Sweeney, Frédéric Dias, and Andrew C. Parnell. “Spatial Bayesian Hierarchical Modelling of Extreme Sea States.” Ocean Modelling 107 (2016): 1–13. doi: 10.1016/j.ocemod.2016.09.015.
    Keywords: Bayesian hierarchical modelling; Spatial modelling; Extreme value analysis; Ocean waves; Significant wave height;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 10268
    Identification Number:
    Depositing User: Andrew Parnell
    Date Deposited: 03 Dec 2018 18:12
    Journal or Publication Title: Ocean Modelling
    Publisher: Elsevier
    Refereed: Yes
    Funders: European Research Council, Science Foundation Ireland (SFI), Environmental Protection Agency (EPA)
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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