@Article{cmc.2025.063373,
AUTHOR = {Xiaoni Sun, Jiming Li, Zhiqiang Zhao, Guodong Jing, Baojun Chen, Jinrong Hu, Fei Wang, Yong Zhang},
TITLE = {Utility of Graph Neural Networks in Short-to Medium-Range Weather Forecasting},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {2},
PAGES = {2121--2149},
URL = {http://www.techscience.com/cmc/v84n2/62869},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.063373}
}