@article{a4463212c9f744b9b9f2a3f477957ff0,
title = "Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data",
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.",
keywords = "causal influence, extreme events, Graph Neural Network, heatwave prediction, weather stations",
author = "Peiyuan Li and Yin Yu and Daning Huang and Wang, {Zhi Hua} and Ashish Sharma",
note = "Funding Information: This research is supported by the Walder Foundation, NSF awards #139316 and #2230772. This work was also supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, under contract DE‐AC02‐06CH11357. The GNN model is implemented using PyTorch Geometric (PyG) (Fey & Lenssen, 2019 ), an open‐source machine learning framework with Graph Network architectures built upon PyTorch (Paszke et al., 2019 ). We would like to acknowledge the National Center of Environmental Information (NCEI) for providing the data used in this study. We also thank Ms. Xueli Yang for sharing the research data reported in Yang et al. ( 2022 ). Funding Information: This research is supported by the Walder Foundation, NSF awards #139316 and #2230772. This work was also supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, under contract DE-AC02-06CH11357. The GNN model is implemented using PyTorch Geometric (PyG) (Fey & Lenssen, 2019), an open-source machine learning framework with Graph Network architectures built upon PyTorch (Paszke et al., 2019). We would like to acknowledge the National Center of Environmental Information (NCEI) for providing the data used in this study. We also thank Ms. Xueli Yang for sharing the research data reported in Yang et al. (2022). Publisher Copyright: {\textcopyright} 2023 The Authors.",
year = "2023",
month = apr,
day = "16",
doi = "10.1029/2023GL103405",
language = "English (US)",
volume = "50",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
number = "7",
}