O'Sullivan, John and Sweeney, Conor and Parnell, Andrew (2019) Bayesian spatial extreme value analysis of maximum temperatures in County Dublin, Ireland. Environmetrics, 31 (e2621). ISSN 1180-4009

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Abstract

In this study, we begin a comprehensive characterisation of temperature extremes in Ireland for the period 1981-2010. We produce return levels of anomalies of daily maximum temperature extremes for an area over Ireland, for the 30-year period 1981-2010. We employ extreme value theory (EVT) to model the data using the generalised Pareto distribution (GPD) as part of a three-level Bayesian hierarchical model. We use predictive processes in order to solve the computationally difficult problem of modelling data over a very dense spatial field. To our knowledge, this is the first study to combine predictive processes and EVT in this manner. The model is fit using Markov chain Monte Carlo (MCMC) algorithms. Posterior parameter estimates and return level surfaces are produced, in addition to specific site analysis at synoptic stations, including Casement Aerodrome and Dublin Airport. Observational data from the period 2011- 2018 is included in this site analysis to determine if there is evidence of a change in the observed extremes. An increase in the frequency of extreme anomalies, but not the severity, is observed for this period. We found that the frequency of observed extreme anomalies from 2011-2018 at the Casement Aerodrome and Phoenix Park synoptic stations exceed the upper bounds of the credible intervals from the model by 20% and 7% respectively.

Item Type:	Article
Additional Information:	This is the preprint version of the published article, which is available at O'Sullivan, J, Sweeney, C, Parnell, AC. Bayesian spatial extreme value analysis of maximum temperatures in County Dublin, Ireland. Environmetrics. 2020; 31:e2621. https://doi.org/10.1002/env.2621. This version can be cited as arXiv:1906.06744
Keywords:	Bayesian; Gaussian Processes; Predictive Processes; Ireland; spatial; extreme value analysis; climate extremes; GPD;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Science and Engineering > Mathematics and Statistics
Item ID:	14029
Identification Number:	https://doi.org/10.1002/env.2621
Depositing User:	Andrew Parnell
Date Deposited:	16 Feb 2021 17:10
Journal or Publication Title:	Environmetrics
Publisher:	Wiley
Refereed:	Yes
URI:	Publisher
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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