Spatial Bayesian hierarchical modelling of extreme sea states

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Title: Spatial Bayesian hierarchical modelling of extreme sea states
Authors: Clancy, ColmO'Sullivan, JohnSweeney, ConorDias, FrédéricParnell, Andrew C.
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Date: Nov-2016
Online since: 2019-07-11T09:17:31Z
Abstract: 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.
Funding Details: Environmental Protection Agency
European Research Council
Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Ocean Modelling
Volume: 107
Start page: 1
End page: 13
Copyright (published version): 2016 Elsevier
Keywords: Bayesian hierarchical modellingSpatial modellingExtreme value analysisOcean wavesSignificant wave height
DOI: 10.1016/j.ocemod.2016.09.015
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Earth Institute Research Collection
Insight Research Collection

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