地球科学进展 ›› 2020, Vol. 35 ›› Issue (6): 551 -560. doi: 10.11867/j.issn.1001-8166.2020.053

综述与评述    下一篇

天气预报中的人工智能技术进展
马雷鸣( )   
  1. 上海中心气象台,上海 200030
  • 收稿日期:2020-04-12 修回日期:2020-05-19 出版日期:2020-06-10
  • 基金资助:
    国家自然科学基金项目“边界层顶大气过程影响深对流强度变化机制及参数化闭合研究”(41975069);上海市科委重点项目“灾害天气(强对流)数字模拟器关键技术研究及应用”(18DZ1200400)

Development of Artificial Intelligence Technology in Weather Forecast

Leiming Ma( )   

  1. Shanghai Central Meteorological Observatory, Shanghai 200030, China
  • Received:2020-04-12 Revised:2020-05-19 Online:2020-06-10 Published:2020-07-06
  • About author:Ma Leiming (1975-), male, Shihezi City, Xinjiang Uygur Autonomous Region, Professor. Research areas include typhoon, numerical prediction, artificial intelligence technology. E-mail: malm@typhoon.org.cn
  • Supported by:
    the National Natural Science Foundation of China "The mechanism influencing deep convection intensity variation associated with PBL top and closure scheme for convective parameterization"(41975069);The Key Project of Shanghai Science and Technology Commission "Disaster weather (strong convection) digital simulator key technology research and application"(18DZ1200400)

以数值预报技术为主的天气预报由于存在大气运动规律认识和表达、观测资料同化应用、模式物理参数化等方面的不足,导致误差。人工智能技术基于大数据学习的优势为天气预报的改进和技术革命提供了新的可能。从人工智能的发展背景、人工智能技术在天气预报中的应用现状以及未来的发展趋势等方面进行了重点阐述,提出了人工智能技术与数值预报相融合的天气预报技术发展思路。特别指出,未来的天气预报人工智能算法需要着眼于导致预报不确定性的非线性、混沌性的大气运动特点,不仅要完善基于数据驱动的输入—输出映射的框架,更要从数学与物理学的本质出发,实现数学、物理的混合建模,在实现人工智能理论突破的同时,推动天气预报瓶颈问题的解决。

Numerical weather prediction, which is the major basis of current weather forecast, has some shortcomings, such as the understanding of the law of atmospheric motion, the assimilation and application of observation data, the expression of model physics, etc., leading to the forecast error of weather. The rapid development of artificial intelligence technology in recent years provides a new possibility for the advancement and innovation of weather forecast. In this paper, the background of the development of artificial intelligence, the current situation of the application of artificial intelligence technology to weather forecast and the future development trend are mainly described to account for this possibility. After that, the idea for development of weather forecast technology based on the integration of artificial intelligence and numerical forecast is put forward. Particularly, this study stresses that, in order to advance the AI algorithm of weather forecast in the future, it is requested to focus on the nonlinear and chaotic characteristics of atmospheric motion leading to the uncertainty of forecast. Starting from the essence of mathematics and physics, we need to realize the hybrid modeling of mathematics and physics, not only to establish the framework of input-output mapping, but also to provide solutions to the bottleneck problems of weather forecast.

中图分类号: 

图1 “蓄水池”式的数据输入—输出(Input Layer-Output Layer)在深度学习中的应用[ 30 ]
Fig.1 Application of reservoir-only prediction network (Input Layer-Output Layer) in Machine Learning[ 30 ]
图2 覆盖美国大陆逐5分钟、空间分辨率5 km经偏差订正的智能手机地面气压观测[ 31 ]
Fig.2 Surface pressure in horizontal resolution of 5 km observed by mobile phones over US[ 31 ]
图3 上海中心气象台基于AI模型订正后的降水预报准确率评分与其他客观预报方法的比较
EC:ECMWF模式;GFS:NCEP/GFS模式;WARMS:华东区域WRF模式;机器学习订正:OBA模型;EC、WARMS频率订正:经频率订正后的模式预报结果
Fig.3 Comparison of accuracy for different objective prediction methods
EC:ECMWF model;GFS:NCEP/GFS model;WARMS:WRF model for East China;Correction with Machine Learning:OBA modual developed by Shanghai Central Meteorological Observatory;EC,WARMS frequency correction:Prediction results from frequency correction
图4 AI灾害天气预报模型优化流程
实线箭头表示主流程,虚线表示可选和循环过程
Fig.4 The optimized AI forecast flow chart for disastrous weather
Solid arrows for the main flow, dashed arrows for the optional and cycled flow
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