深度学习在气象数据订正中的应用综述  

Survey of application of deep learning in meteorological data correction

作　　者：蒋鸿儒 方巍[1,2,3] JIANG Hongru;FANG Wei(School of Computer Science,Nanjing University of Information Science and Technology,Nanjing Jiangsu 210044,China;Key Open Laboratory of Transportation Meteorology of China Meteorological Administration(Nanjing Joint Institute for Atmospheric Sciences),Nanjing Jiangsu 210041,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(Nanjing University of Information Science and Technology),Nanjing Jiangsu 210044,China)

机构地区：[1]南京信息工程大学计算机学院,南京210044 [2]中国气象局交通气象重点开放实验室(南京气象科技创新研究院),南京210041 [3]江苏省大气环境与装备技术协同创新中心(南京信息工程大学),南京210044

出　　处：《计算机应用》2024年第12期3930-3940,共11页journal of Computer Applications

基　　金：国家自然科学基金资助项目(42075007);中国气象局流域强降水重点开放实验室开放研究基金资助项目(2023BHR-Y14);中国气象局交通气象重点开放实验室开放研究基金资助项目(BJG202306)。

摘　　要：数据订正是资料同化的核心过程之一,即通过修正和校准数据提高资料同化的效果。针对气象观测存在多种误差导致气象数据存在偏差的问题,综述深度学习在气象数据订正中的应用,应用场景包括气象模式订正、天气预报和气候预测。首先,介绍气象数据订正的重要性,同时回顾传统的气象数据订正方法,如统计学、传统机器学习等,并分析它们的优点和局限性;其次,详细介绍基于深度学习的数据订正在3个场景中的应用,深度学习方法主要包括卷积神经网络(CNN)、循环神经网络(RNN)和Transformer,并且通过归纳总结当前的研究进展,讨论数据订正中深度学习方法与传统方法的优劣;最后,总结深度学习在数据订正中存在的局限性,同时指出深度学习在气象数据订正中的优化方式和未来发展方向。Data correction is one of the core processes in data assimilation,which aims to improve the assimilation effect of data by correcting and calibrating the data.Aiming at the issue of multiple errors in meteorological observations leading to biases in meteorological data,the application of deep learning in meteorological data correction was reviewed,and the application scenarios include meteorological model correction,weather forecast,and climate prediction.Firstly,the importance of meteorological data correction was introduced,and traditional meteorological data correction methods such as statistics and traditional machine learning were looked back with advantages and limitations of the methods analyzed.Secondly,the application of deep learning in data correction in three scenarios was detailed,the deep learning methods include Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),and Transformer.At the same time,by summarizing the current research progress,the strengths and weaknesses of deep learning methods and traditional methods in data correction were discussed.Finally,the limitations of deep learning in data correction were summed up,and the optimization approaches and future development directions of deep learning in meteorological data correction were pointed out.

关 键 词：深度学习 数据订正 资料同化 气候预测 天气预报 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]