An Efficient Workflow for Modelling High-Dimensional Spatial Extremes

TL;DR

This paper presents a comprehensive, computationally efficient Bayesian workflow for modeling high-dimensional spatial extremes, effectively capturing extremal dependence structures and enhancing robustness against model misspecification.

Contribution

It introduces a novel methodological workflow combining the spatial conditional extremes model with R-INLA for fast inference and a post hoc adjustment for robustness.

Findings

Efficient Bayesian modeling of high-dimensional spatial extremes achieved.

Model successfully captures extremal dependence in precipitation data.

Robustness improved through post hoc adjustment for model misspecification.

Abstract

A successful model for high-dimensional spatial extremes should, in principle, be able to describe both weakening extremal dependence at increasing levels and changes in the type of extremal dependence class as a function of the distance between locations. Furthermore, the model should allow for computationally tractable inference using inference methods that efficiently extract information from data and that are robust to model misspecification. In this paper, we demonstrate how to fulfil all these requirements by developing a comprehensive methodological workflow for efficient Bayesian modelling of high-dimensional spatial extremes using the spatial conditional extremes model while performing fast inference with R-INLA. We then propose a post hoc adjustment method that results in more robust inference by properly accounting for possible model misspecification. The developed methodology is applied for modelling extreme hourly precipitation from high-resolution radar data in Norway. Inference is computationally efficient, and the resulting model fit successfully captures the main trends in the extremal dependence structure of the data. Robustifying the model fit by adjusting for possible misspecification further improves model performance.