基于深度学习的气象预报模型研究综述  

Survey on Deep Learning-based Meteorological Forecasting Models

作　　者：王嫄 霍鹏 韩毅[2] 陈暾 汪祥 温辉 WANG Yuan;HUO Peng;HAN Yi;CHEN Tun;WANG Xiang;WEN Hui(College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China;College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]天津科技大学人工智能学院,天津300457 [2]国防科技大学气象海洋学院,长沙410073

出　　处：《计算机科学》2025年第3期112-126,共15页Computer Science

基　　金：国家自然科学基金(62372460);湖南省自然科学基金(2024JJ4042);国防科技大学青年自主创新科学基金(ZK24-53)。

摘　　要：实时准确的气象预报关乎人民生计、环境生态以及军事决策,受到各界人士的广泛关注和重点研究。数值气象预报是当前的主流预报方法,经过长期发展,其预报精确性和可靠性不断提高,但仍然面临系统误差无法避免、历史观测数据难以利用,以及计算开销巨大等重大挑战。随着深度学习技术的快速兴起,数据驱动的人工智能方法逐渐应用于气象预报领域,为应对上述挑战提供了全新技术手段。基于上述背景,文中全面总结了数值气象预报和深度学习气象预报的研究现状,系统梳理了深度学习气象预报模型的相关概念和输入数据,详细阐述了应用于各类气象预报任务的代表性模型,深入对比了不同模型的技术架构和性能指标,并且分析讨论了该领域目前面临的挑战和未来发展的方向,旨在为相关研究提供参考。Accurate and timely weather forecasting is crucial for people’s livelihoods,environmental ecology,and military decision-making,attracting extensive attention and focused research from various sectors.Numerical weather prediction(NWP)is currently the mainstream forecasting method.Over long-term development,the accuracy and reliability of NWP have continuously improved.However,it still faces significant challenges,such as unavoidable systematic errors,ineffective utilization of historical observation data,and substantial computational costs.With the rapid rise of deep learning,data-driven artificial intelligence me-thods are gradually being applied to the field of weather forecasting,offering novel techniques to overcome these challenges.Against this backdrop,this paper comprehensively summarizes the current research status of NWP and deep learning-based weather forecasting.It systematically reviews the relevant concepts and input data for deep learning-based weather forecasting models,tho-roughly explains representative models applied to various weather forecasting tasks,and provides a detailed comparison of the technical architectures and performance metrics of different models.Additionally,it analyzes and discusses the existing challenges and the future directions in this field.The ultimate purpose of this survey is to provide reference information for related research.

关 键 词：气象预测 深度学习 大模型 AI4Science 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]