Joint Estimation of Extreme Spatially Aggregated Precipitation at Different Scales through Mixture Modelling

TL;DR

This paper introduces a novel spatial extreme value model that captures the mixture of convective and frontal rainfall processes, improving the estimation of extreme aggregated precipitation across multiple spatial scales.

Contribution

It proposes a mixture model combining different marginal and dependence structures to better represent extremal behaviour of diverse rainfall processes.

Findings

Model accurately captures extremal behaviour of convective and frontal rainfall.

Accounting for mixture structure improves inference on spatial aggregate extremes.

Model effectively applies to high-dimensional gridded precipitation data.

Abstract

Although most models for rainfall extremes focus on point-wise values, it is aggregated precipitation over areas up to river catchment scale that is of the most interest. To capture the joint behaviour of precipitation aggregates evaluated at different spatial scales, parsimonious and effective models must be built with knowledge of the underlying spatial process. Precipitation is driven by a mixture of processes acting at different scales and intensities, e.g., convective and frontal, with extremes of aggregates for typical catchment sizes arising from extremes of only one of these processes, rather than a combination of them. High-intensity convective events cause extreme spatial aggregates at small scales but the contribution of lower-intensity large-scale fronts is likely to increase as the area aggregated increases. Thus, to capture small to large scale spatial aggregates within a single approach requires a model that can accurately capture the extremal properties of both convective and frontal events. Previous extreme value methods have ignored this mixture structure; we propose a spatial extreme value model which is a mixture of two components with different marginal and dependence models that are able to capture the extremal behaviour of convective and frontal rainfall and more faithfully reproduces spatial aggregates for a wide range of scales. Modelling extremes of the frontal component raises new challenges due to it exhibiting strong long-range extremal spatial dependence. Our modelling approach is applied to fine-scale, high-dimensional, gridded precipitation data. We show that accounting for the mixture structure improves the joint inference on extremes of spatial aggregates over regions of different sizes.