A Max-Stable Spatial Model for Extreme Precipitation

• Activity Number: 505
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: International Indian Statistical Association
• Author(s): Brian J. Reich*+ and Ben Shaby
• Companies: North Carolina State University and UC - Berkeley
• Keywords: Bayesian ; Climate change ; Extreme value analysis ; Markov chain Monte Carlo

Abstract

Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for more than a trivial number of spatial locations, preventing, in particular, Bayesian analyses. In this paper, we propose a new random effects model to account for spatial dependence. We show that our specification of the random effect distribution leads to a max-stable process that has the popular Gaussian extreme value process (GEVP) as a limiting case. The proposed model is used to analyze the yearly maximum precipitation from a regional climate model.