Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data
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.
- Authors:
-
[1];
[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
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF); The Walder Foundation
- OSTI Identifier:
- 1968518
- Alternate Identifier(s):
- OSTI ID: 1971694; OSTI ID: 2329402
- Grant/Contract Number:
- DE‐AC02‐06CH11357; AC02-06CH11357; 139316; 2230772
- Resource Type:
- Published Article
- Journal Name:
- Geophysical Research Letters
- Additional Journal Information:
- Journal Name: Geophysical Research Letters Journal Volume: 50 Journal Issue: 7; Journal ID: ISSN 0094-8276
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Li, Peiyuan, Yu, Yin, Huang, Daning, Wang, Zhi‐Hua, and Sharma, Ashish. Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data. United States: N. p., 2023.
Web. doi:10.1029/2023GL103405.
Li, Peiyuan, Yu, Yin, Huang, Daning, Wang, Zhi‐Hua, & Sharma, Ashish. Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data. United States. https://doi.org/10.1029/2023GL103405
Li, Peiyuan, Yu, Yin, Huang, Daning, Wang, Zhi‐Hua, and Sharma, Ashish. Tue .
"Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data". United States. https://doi.org/10.1029/2023GL103405.
@article{osti_1968518,
title = {Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data},
author = {Li, Peiyuan and Yu, Yin and Huang, Daning and Wang, Zhi‐Hua and Sharma, Ashish},
abstractNote = {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.},
doi = {10.1029/2023GL103405},
journal = {Geophysical Research Letters},
number = 7,
volume = 50,
place = {United States},
year = {Tue Apr 04 00:00:00 EDT 2023},
month = {Tue Apr 04 00:00:00 EDT 2023}
}
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