Flexible Modeling of Multivariate Spatial Extremes

TL;DR

This paper introduces a flexible multi-factor copula model for multivariate spatial extremes, capturing complex dependence structures within and across spatial processes, with Bayesian inference demonstrated on temperature data from Alabama.

Contribution

The paper presents a novel multi-factor copula model that captures diverse extremal dependence structures in multivariate spatial data, with an efficient Bayesian inference approach.

Findings

Model accurately captures tail dependence in temperature extremes.

Temperature processes show strong spatial and moderate cross-dependence.

Joint modeling improves understanding of spatial risk measures.

Abstract

We develop a novel multi-factor copula model for multivariate spatial extremes, which is designed to capture the different combinations of marginal and cross-extremal dependence structures within and across different spatial random fields. Our proposed model, which can be seen as a multi-factor copula model, can capture all possible distinct combinations of extremal dependence structures within each individual spatial process while allowing flexible cross-process extremal dependence structures for both upper and lower tails. We show how to perform Bayesian inference for the proposed model using a Markov chain Monte Carlo algorithm based on carefully designed block proposals with an adaptive step size. In our real data application, we apply our model to study the upper and lower extremal dependence structures of the daily maximum air temperature (TMAX) and daily minimum air temperature (TMIN) from the state of Alabama in the southeastern United States. The fitted multivariate spatial model is found to provide a good fit in the lower and upper joint tails, both in terms of the spatial dependence structure within each individual process, as well as in terms of the cross-process dependence structure. Our results suggest that the TMAX and TMIN processes are quite strongly spatially dependent over the state of Alabama, and moderately cross-dependent. From a practical perspective, this implies that it may be worthwhile to model them jointly when interest lies in a computing spatial risk measures that involve both quantities.