Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network

TL;DR

This paper introduces a transformer-based model for seasonal forecasting of extreme precipitation, demonstrating improved prediction of high-risk events up to six months ahead compared to traditional methods.

Contribution

The study presents the application of a temporal fusion transformer model for seasonal extreme precipitation forecasting, outperforming baseline models in predicting quantile risks.

Findings

TFT significantly outperforms ECMWF S5 ensemble in quantile risk prediction at six months.

TFT shows small but consistent improvements over climatology.

The model responds effectively to deviations from normal precipitation patterns.

Abstract

An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at seasonal scales remains an outstanding challenge. Here, we present an approach to forecasting the quantiles of the maximum daily precipitation in each week up to six months ahead using the temporal fusion transformer (TFT) model. Through experiments in two regions, we compare TFT predictions with those of two baselines: climatology and a calibrated ECMWF SEAS5 ensemble forecast (S5). Our results show that, in terms of quantile risk at six month lead time, the TFT predictions significantly outperform those from S5 and show an overall small improvement compared to climatology. The TFT also responds positively to departures from normal that climatology cannot.