Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

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

doi:10.1029/2023GL103405

Title: Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

Journal Article · 03 April 2023 · Geophysical Research Letters

DOI: https://doi.org/10.1029/2023GL103405 · OSTI ID:1968518

^[1]; Yu, Yin ^[2]; Huang, Daning ^[2];

^[3];

^[4]

Discovery Partners Institute University of Illinois System IL Chicago USA
Department of Aerospace Engineering Pennsylvania State University PA University Park USA
School of Sustainable Engineering and the Built Environment Arizona State University AZ Tempe USA
Discovery Partners Institute University of Illinois System IL Chicago USA, Department of Atmospheric Sciences University of Illinois at Urbana‐Champaign IL Champaign USA, Environmental Science Division Argonne National Laboratory IL Lemont USA

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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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); The Walder Foundation; USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1968518

Journal Information:: Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 7 Vol. 50; ISSN 0094-8276

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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