Authors: Francesco Fedele, Joseph Brennan, Sonia…
Spatial Bayesian hierarchical modelling of extreme sea states
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 basedon 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 extremeswhich is more useful for practical applications.