Space-time extremes of severe US thunderstorm environments

TL;DR

This paper develops a space-time max-stable model to analyze the extremal dependence of severe thunderstorm indicators in the US, accounting for seasonality and ENSO effects, with good out-of-sample performance.

Contribution

It introduces a novel max-stable space-time model incorporating ENSO and seasonal effects, along with a bootstrap-based model selection criterion for severe thunderstorm environment extremes.

Findings

Extremes of PROD, CAPE, and SRH are more localized in summer.

ENSO phases influence the spatial localization of storm extremes.

The model demonstrates good out-of-sample predictive performance.

Abstract

Severe thunderstorms cause substantial economic and human losses in the United States. Simultaneous high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are favorable to severe weather, and both they and the composite variable $\mathrm{PROD}=\sqrt{\mathrm{CAPE}} \times \mathrm{SRH}$ can be used as indicators of severe thunderstorm activity. Their extremal spatial dependence exhibits temporal non-stationarity due to seasonality and large-scale atmospheric signals such as El Ni\~no-Southern Oscillation (ENSO). In order to investigate this, we introduce a space-time model based on a max-stable, Brown--Resnick, field whose range depends on ENSO and on time through a tensor product spline. We also propose a max-stability test based on empirical likelihood and the bootstrap. The marginal and dependence parameters must be estimated separately owing to the complexity of the model, and we develop a bootstrap-based model selection criterion that accounts for the marginal uncertainty when choosing the dependence model. In the case study, the out-sample performance of our model is good. We find that extremes of PROD, CAPE and SRH are generally more localized in summer and, in some regions, less localized during El Ni\~no and La Ni\~na events, and give meteorological interpretations of these phenomena.