Gaussian Framework and Optimal Projection of Weather Fields for Prediction of Extreme Events

TL;DR

This paper introduces a Gaussian-based framework for studying and predicting extreme weather events, effectively addressing data scarcity and providing interpretable forecasts, demonstrated through heatwave case studies.

Contribution

It proposes a novel Gaussian framework for extreme event prediction that leverages the entire dataset and offers interpretable projections, outperforming neural networks on reanalysis data.

Findings

Optimal projection highlights soil moisture deficit as a key precursor.

Method performs competitively with neural networks on long datasets.

Framework reveals similarities between extreme and less extreme event maps.

Abstract

Extreme events are the major weather-related hazard for humanity. It is then of crucial importance to have a good understanding of their statistics and to be able to forecast them. However, lack of sufficient data makes their study particularly challenging. In this work, we provide a simple framework for studying extreme events that tackles the lack of data issue by using the entire available dataset, rather than focusing on the extremes of the dataset. To do so, we make the assumption that the set of predictors and the observable used to define the extreme event follow a jointly Gaussian distribution. This naturally gives the notion of an optimal projection of the predictors for forecasting the event. We take as a case study extreme heatwaves over France, and we test our method on an 8000-year-long intermediate complexity climate model time series and on the ERA5 reanalysis dataset. For a-posteriori statistics, we observe and motivate the fact that composite maps of very extreme events look similar to less extreme ones. For prediction, we show that our method is competitive with off-the-shelf neural networks on the long dataset and outperforms them on reanalysis. The optimal projection pattern, which makes our forecast intrinsically interpretable, highlights the importance of soil moisture deficit and quasi-stationary Rossby waves as precursors to extreme heatwaves.