Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

Peiyuan Li, Yu Yin, Daning Huang, Zhi‐Hua Wang, Ashish S. Sharma

Geophysical Research Letters · 04 April 2023 · Argonne National Laboratory

In one sentence. A graph neural network predicts regional heatwaves from weather station data, enabling real-time warnings at lower computational and data cost.

Abstract

Abstract Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.

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