Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data
Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data
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使用图神经网络和气象站数据进行区域热浪预测
DOI：
10.1029/2023gl103405
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发表时间：
2023-04
影响因子：
5.2
通讯作者：
Peiyuan Li;Yin Yu;Daning Huang;Zhi-hua Wang;Ashish Sharma
中科院分区：
文献类型：
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作者：
Peiyuan Li;Yin Yu;Daning Huang;Zhi-hua Wang;Ashish Sharma
热浪对公共卫生和经济造成灾难性后果。准确和及时地预测区域热浪可以改善气候准备并促进决策，以减轻气候变化造成的负担。在本文中，我们提出了一种基于新型深度学习模型的热浪预测算法，即图神经网络（GNN）。这种新的GNN框架可以提供真实的时间警告的区域热浪的突然发生，以较低的计算和数据收集成本的高精度。此外，其可解释的结构解开了区域热浪的时空模式，并有助于丰富我们对一般气候动态和位置之间的因果影响的理解。GNN框架可以应用于其他极端或复合气候事件的检测和预测，这需要进一步的研究。
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.