Modelling sub-daily precipitation extremes with the blended generalised extreme value distribution

TL;DR

This paper introduces a Bayesian hierarchical model using a blended GEV distribution and a novel two-step inference procedure to efficiently model sub-daily precipitation extremes and generate high-resolution spatial return level maps.

Contribution

It presents a new two-step Bayesian approach with INLA for modeling precipitation extremes using bGEV, improving inference efficiency and numerical stability.

Findings

Model accurately maps sub-daily precipitation extremes.

Two-step procedure enhances model fit over standard methods.

Fast, high-resolution spatial return level maps produced.

Abstract

A new method is proposed for modelling the yearly maxima of sub-daily precipitation, with the aim of producing spatial maps of return level estimates. Yearly precipitation maxima are modelled using a Bayesian hierarchical model with a latent Gaussian field, with the blended generalised extreme value (bGEV) distribution used as a substitute for the more standard generalised extreme value (GEV) distribution. Inference is made less wasteful with a novel two-step procedure that performs separate modelling of the scale parameter of the bGEV distribution using peaks over threshold data. Fast inference is performed using integrated nested Laplace approximations (INLA) together with the stochastic partial differential equation (SPDE) approach, both implemented in R-INLA. Heuristics for improving the numerical stability of R-INLA with the GEV and bGEV distributions are also presented. The model is fitted to yearly maxima of sub-daily precipitation from the south of Norway, and is able to quickly produce high-resolution return level maps with uncertainty. The proposed two-step procedure provides an improved model fit over standard inference techniques when modelling the yearly maxima of sub-daily precipitation with the bGEV distribution.