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摘要: 随着人工智能、大数据分析等新一代信息技术的快速发展, 气象领域正经历着从传统模式向智能化的革命性转变. 气象科学的创新需求持续推动自动化技术的迭代升级, 而自动化技术的深度应用也为气象服务智能化转型提供了核心驱动力. 本文总结近几年应用于气象中的自动化新技术, 并从气象感知、气象预报以及决策服务三个方面进行介绍. 在气象感知方面, 总结智能组网观测、分布式协同感知与无人集群技术的应用, 并分析控制理论、系统建模等技术对提升高时空分辨率观测能力的作用. 在气象预报方面, 梳理深度学习、强化学习等方法在短临预报和模式优化中的应用, 以及流程自动化与系统工程在提升预报效率中的价值. 在决策服务方面, 探讨知识图谱、智能推理以及自主决策等算法在灾害预警和应急响应中的应用, 并阐述AI技术助力气象服务和决策支持的作用. 目前, 无人集群气象观测、AI大模型预报与智能决策正推动气象科学与自动化深度融合, 为构建高效智能的气象服务体系提供关键技术支撑.Abstract: With the rapid advancement of new-generation information technologies such as artificial intelligence and big data analytics, the meteorological field is undergoing a revolutionary transformation from traditional models to intelligent systems. The continuous innovation demands of meteorological science are driving the iterative upgrading of automation technologies, while their deep application provides a core driving force for the intelligent transformation of meteorological services. This paper summarizes the new automation technologies applied in meteorology in recent years and introduces them from three aspects: Meteorological sensing, meteorological forecasting, and decision-making services. In terms of meteorological sensing, the applications of intelligent networked observation, distributed collaborative sensing, and unmanned cluster technology are summarized, and the role of control theory and system modeling in improving high spatio-temporal resolution observation capabilities is analyzed. In terms of meteorological forecasting, the applications of deep learning and reinforcement learning methods in short-term forecasting and model optimization are reviewed, as well as the value of process automation and system engineering in improving forecasting efficiency. In terms of decision-making services, the applications of knowledge graphs, intelligent reasoning, and autonomous decision-making algorithms in disaster warning and emergency response are discussed, and the role of AI technology in assisting meteorological services and decision support is explained. Currently, unmanned swarm meteorological observation, AI foundation-model-based forecasting, and intelligent decision-making are emerging as key drivers of the convergence between meteorological science and automation, providing critical technological support for the development of efficient and intelligent meteorological service systems.
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图 2 气象科学技术三要素之间的逻辑关系
Fig. 2 The logical relationship between the three elements of meteorological science and technology
图 3 自动化与大气科学学科交叉技术关联图
Fig. 3 Interdisciplinary technology correlation map between automation and atmospheric sciences
图 4 自动化科学支撑的现代气象感知系统关键技术架构
Fig. 4 Key technical architecture of the modern meteorological sensing system supported by automation science
图 5 自动化科学支撑的现代气象预报系统关键技术架构
Fig. 5 Key technical architecture of the modern meteorological forecasting system supported by automation science
图 6 自动化科学支撑的现代气象决策系统关键技术架构
Fig. 6 Key technical architecture of the modern meteorological decision-making system supported by automation science
图 7 气象自动化未来发展总体框架
Fig. 7 Overall framework for the future development of meteorological automation
表 1 国际与国内气象预报自动化体系比较
Table 1 Comparison of international and domestic automated meteorological forecasting systems
对比维度 国际代表体系
(ECMWF、GraphCast等)我国代表体系(CMA-GFS、Pangu-Weather等) 差距与不足 我国特色与潜在优势 核心模型与技术路线 高分辨率数值模式与先进资料同化技术成熟, AI预报快速发展 CMA-GFS持续升级, Pangu-Weather等AI模型快速发展 AI预报稳定性、可解释性及极端天气适应能力有待提升 数值模式与AI融合发展, 本地化适配优势明显 算力与计算架构 依托大型超算平台支撑全球业务运行 国家超算中心与国产AI芯片形成多元算力体系 专用算力规模及软硬件协同能力仍有差距 自主可控算力体系持续完善, 体系配置灵活 业务化与服务体系 集合预报与概率预报体系成熟, 支持全球风险评估 “监测—预报—预警—服务”一体化体系持续智能化发展 数据共享与跨部门协同机制有待加强 区域化、行业化服务响应快速, 地方需求对接紧密 科研与开放生态 数据开放程度高, 国际协同科研生态活跃(如Copernicus等) 自主研发体系为主, 共享机制逐步完善 模型接口的标准化与模块化水平仍需提升 科研、业务与产业链结合紧密, 成果转化效率较高 
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