Open Access

REVIEW

Utility of Graph Neural Networks in Short-to Medium-Range Weather Forecasting

Xiaoni Sun¹, Jiming Li², Zhiqiang Zhao², Guodong Jing², Baojun Chen², Jinrong Hu³, Fei Wang², Yong Zhang^1,*

1 Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China
2 Key Laboratory for Cloud Physics of China Meteorological Administration, China Meteorological Administration Weather Modification Centre, Beijing, 100081, China
3 School of Computers, Chengdu University of Information Technology, Chengdu, 610039, China

* Corresponding Author: Yong Zhang. Email:

(This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)

Computers, Materials & Continua 2025, 84(2), 2121-2149. https://doi.org/10.32604/cmc.2025.063373

Received 13 January 2025; Accepted 29 April 2025; Issue published 03 July 2025

Abstract

Weather forecasting is crucial for agriculture, transportation, and industry. Deep Learning (DL) has greatly improved the prediction accuracy. Among them, Graph Neural Networks (GNNs) excel at processing weather data by establishing connections between regions. This allows them to understand complex patterns that traditional methods might miss. As a result, achieving more accurate predictions becomes possible. The paper reviews the role of GNNs in short-to medium-range weather forecasting. The methods are classified into three categories based on dataset differences. The paper also further identifies five promising research frontiers. These areas aim to boost forecasting precision and enhance computational efficiency. They offer valuable insights for future weather forecasting systems.

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Keywords

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