Operational Climate Prediction in the Era of Big Data in China：Reviews and Prospects

• Published in: Acta meteorologica Sinica Vol. 30; no. 3; pp. 444 - 456
• Main Author: 王昕 宋连春 王国复 任宏利 吴统文 贾小龙 吴焕萍 吴捷
• Format: Journal Article
• Language: English
• Published: Beijing The Chinese Meteorological Society 01.06.2016; College of Computer Science and Technology,Jilin University,Changchun 130012%National Climate Center,China Meteorological Administration,Beijing 100081
• Subjects: Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics and Environmental Physics; IT行业; Meteorology; 中国; 作战; 展望; 应用程序; 技术革命; 气候预测; 计算机; China, People's Rep; climate prediction; big data; operational development; prediction method
• ISSN: 2095-6037; 0894-0525; 2198-0934; 2191-4788
• DOI: 10.1007/s13351-016-6081-3

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things.With the development of climate observing systems,particularly satellite meteorological observation and high-resolution climate models,and the rapid growth in the volume of climate data,climate prediction is now entering the era of big data.The application of big data will provide new ideas and methods for the continuous development of climate prediction.The rapid integration,cloud storage,cloud computing,and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively,thus predicting the future climate more accurately.This paper describes the application status of big data in operational climate prediction in China;it analyzes the key big data technologies,discusses the future development of climate prediction operations from the perspective of big data,speculates on the prospects for applying climatic big data in cloud computing and data assimilation,and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.