Modeling high and low extremes with a novel dynamic spatio-temporal model

TL;DR

This paper introduces a novel dynamic spatio-temporal model that effectively captures both high and low environmental extremes, addressing limitations of existing models in understanding and predicting extreme events with uncertainty quantification.

Contribution

The paper presents a new class of models using mixture distributions with varying tail indices to better capture extremal dependence and independence in space and time.

Findings

Successfully modeled hourly particulate matter extremes in the Central US.

Demonstrated improved extremal dependence detection over existing models.

Supported missing data prediction with uncertainty quantification.

Abstract

Extreme environmental events such as severe storms, drought, heat waves, flash floods, and abrupt species collapse have become more prevalent in the earth-atmosphere dynamic system in recent years. In order to fully understand the underlying mechanisms and enhance informed decision-making, a flexible model capable of accommodating extremes is necessary. Existing dynamic spatio-temporal statistical models exhibit limitations in capturing extremes when assuming Gaussian error distributions, whereas the current models for spatial extremes mostly assume temporal independence and are focused on joint upper tails at two or more locations. Here, we introduce a new class of dynamic spatio-temporal models that capture both high and low extremes using a mixture of heavy- and light-tailed distributions with varying tail indices. Our framework flexibly identifies extremal dependence and independence in both space and time with uncertainty quantification and supports missing data prediction, as in other dynamic spatio-temporal models. We demonstrate its effectiveness using a large reanalysis dataset of hourly particulate matter in the Central United States.