A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge

Anna Barlow; Christian Rohrbeck; Paul Sharkey; Robert Shooter; Emma Simpson

doi:10.1007/s10687-018-0330-z

A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge

Anna Barlow, Christian Rohrbeck, Paul Sharkey, Robert Shooter, Emma Simpson

Department of Mathematical Sciences

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Abstract

This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose a spatio-temporal structure in the model parameters via an autoregressive prior. Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark.

Original language	English
Pages (from-to)	431-439
Number of pages	9
Journal	Extremes
Volume	21
Issue number	3
Early online date	18 Jun 2018
DOIs	https://doi.org/10.1007/s10687-018-0330-z
Publication status	Published - 30 Sept 2018

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This is a post-peer-review, pre-copyedit version of an article published in Extremes. The final authenticated version is available online at: https://doi.org/10.1007/s10687-018-0330-z
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A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge

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