Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere2023GL103405
JournalGeophysical Research Letters
Volume50
Issue number7
DOIs
StatePublished - Apr 16 2023

Keywords

  • Graph Neural Network
  • causal influence
  • extreme events
  • heatwave prediction
  • weather stations

ASJC Scopus subject areas

  • Geophysics
  • General Earth and Planetary Sciences

Fingerprint

Dive into the research topics of 'Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data'. Together they form a unique fingerprint.

Cite this