Conditional simulations of Brown-Resnick processes

TL;DR

This paper introduces a Markov chain Monte-Carlo method for efficient conditional simulation of Brown-Resnick processes, enabling better modeling of spatial extremes like rainfall, with demonstrated accuracy on simulated and real data.

Contribution

It develops a novel MCMC algorithm to sample from the complex conditional distribution of Brown-Resnick processes, overcoming computational challenges.

Findings

The method accurately simulates spatial extremes.

It handles real-sized datasets effectively.

Application to Zurich rainfall demonstrates practical utility.

Abstract

Since many environmental processes such as heat waves or precipitation are spatial in extent, it is likely that a single extreme event affects several locations and the areal modeling of extremes is therefore essential if the spatial dependence of extremes has to be appropriately taken into account. Although some progress has been made to develop a geostatistic of extremes, conditional simulation of max-stable processes is still in its early stage. This paper proposes a framework to get conditional simulations of Brown-Resnick processes. Although closed forms for the regular conditional distribution of Brown-Resnick processes were recently found, sampling from this conditional distribution is a considerable challenge as it leads quickly to a combinatorial explosion. To bypass this computational burden, a Markov chain Monte-Carlo algorithm is presented. We test the method on simulated data and give an application to extreme rainfall around Zurich. Results show that the proposed framework provides accurate conditional simulations of Brown-Resnick processes and can handle real-sized problems.