TY  - EJOU
AU  - Sun, Xiaoni 
AU  - Li, Jiming 
AU  - Zhao, Zhiqiang 
AU  - Jing, Guodong 
AU  - Chen, Baojun 
AU  - Hu, Jinrong 
AU  - Wang, Fei 
AU  - Zhang, Yong 

TI  - Utility of Graph Neural Networks in Short-to Medium-Range Weather Forecasting
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Graph neural networks; weather forecasting; meteorological datasets

DO  - 10.32604/cmc.2025.063373