Survey on Deep Learning-based Meteorological Forecasting Models

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.