Computationally efficient spatial modeling of annual maximum 24 hour precipitation. An application to data from Iceland

TL;DR

This paper introduces a computationally efficient spatial statistical model for analyzing annual maximum 24-hour precipitation over Iceland, integrating meteorological data and advanced spatial modeling techniques.

Contribution

It develops a latent Gaussian model with SPDE spatial components and an efficient MCMC sampler, enabling fast, continuous spatial predictions of extreme precipitation.

Findings

Effective modeling of precipitation extremes over Iceland.

Incorporation of meteorological model outputs improves predictions.

Fast spatial predictions achieved through specialized MCMC sampling.

Abstract

We propose a computationally efficient statistical method to obtain distributional properties of annual maximum 24 hour precipitation on a 1 km by 1 km regular grid over Iceland. A latent Gaussian model is built which takes into account observations, spatial variations and outputs from a local meteorological model. A covariate based on the meteorological model is constructed at each observational site and each grid point in order to assimilate available scientific knowledge about precipitation into the statistical model. The model is applied to two data sets on extreme precipitation, one uncorrected data set and one data set that is corrected for phase and wind. The observations are assumed to follow the generalized extreme value distribution. At the latent level, we implement SPDE spatial models for both the location and scale parameters of the likelihood. An efficient MCMC sampler which exploits the model structure is constructed, which yields fast continuous spatial predictions for spatially varying model parameters and quantiles.