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气象:2026,52(5):513-526
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“风清”人工智能天气预报模型业务应用性能评估
李妮娜,金荣花,宫宇,代刊,曹勇,聂高臻,林建,韦青,吴海旭,罗华坤,龙明盛,王建民
(国家气象中心,北京 100081; 河北省气象人工智能重点实验室,雄安气象人工智能创新研究院,雄安新区 070001; 清华大学,北京 100084)
Performance Evaluation on Operational Application of the AI-Based Global Short- and Medium-Range Forecasting System—Fengqing Model
LI Nina,JIN Ronghua,GONG Yu,DAI Kan,CAO Yong,NIE Gaozhen,LIN Jian,WEI Qing,WU Haixu,LUO Huakun,LONG Mingsheng,WANG Jianmin
(National Meteorological Centre, Beijing 100081; Hebei Key Laboratory of Meteorological Artificial Intelligence, Xiong’an Institute of Meteorological Artificial Intelligence, Xiong’an New Area 070001; Tsinghua University, Beijing 100084)
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投稿时间:2025-04-30    修订日期:2026-03-15
中文摘要: 2024年,中国气象局与清华大学联合研发了基于“AI+物理”的“风清”人工智能天气预报模型(简称风清模型)。该模型采用多尺度隐空间投影架构与能量守恒损失函数等设计,实现了对全球短中期天气的智能预报,并已投入业务化运行。本研究基于预报准确率、偏差空间分布等指标,全面评估了2024年该模型在中国及周边区域的预报能力;并聚焦台风和降水两类典型天气过程,重点检验了其在灾害性天气业务预报中的性能表现。结果表明,风清模型500 hPa位势高度场的有效预报时效超过10 d,地面2 m气温及高空850 hPa温度预报的均方根误差较欧洲中期天气预报中心ECMWF-IFS模式显著偏小(最大改进幅度达37.66%);从典型天气过程来看,风清模型对台风路径的预报略优于ECMWF-IFS模式,但台风强度预报偏弱;同时,该模型对暴雨有较好的预报能力,对台风降水及梅雨雨带的落区预报误差较小,暴雨预报TS评分在73~168 h时效下较ECMWF-IFS模式提升了43.53%。总体而言,风清模型在业务预报中展现出较高潜力,但中长期时效下的活跃度与台风强度预报有待进一步提升。
Abstract:In 2024, China Meteorological Administration (CMA), in collaboration with Tsinghua University, developed the forecasting model following an innovative “AI-Physics” hybrid approach—Fengqing Model. With the designs such as a multi-scale latent space projection architecture and an energy-conservation loss function, the model has been equipped with global short- and medium-range weather forecasting capabilities and has been applied in forecasting operations. This paper comprehensively evaluates the forecasting ability of Fengqing Model in China and the surrounding areas in 2024 from several metrics such as forecasting accuracy and bias distribution. Two kinds of typical synoptic processes, i.e., typhoon and rainstorm, are also focused on deeply exploring the model’s performance in the forecasts of disastrous weather. The results show that the 500 hPa geopotential height forecasts by Fengqing Model maintain a predictive skill beyond 10 days. The root mean square errors of 2 m temperature and 850 hPa temperature are significantly lower than those from the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS), having a maximum improvement of 37.66%. In terms of typical weather processes, Fengqing Model demonstrates its superior performance in typhoon track forecasting to ECMWF-IFS, but it underestesmates the typhoon intensity. In addition, Fengqing Model has a good forecasting ability for rainstorm, with smaller forecast errors for the locations of typhoon rain and Meiyu front rainfall belt. The TS score of rainstorm forecasts in the medium-range (73-168 h lead time) is improved by 43.53% compared to that of ECMWF-IFS forecasts. Overall, the Fengqing Model presents considerable potential in operational forecasting, although further improvements are needed in activity level and typhoon intensity prediction at medium- and long-range lead times.
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基金项目:新疆维吾尔自治区重点研发专项(2022B03027-2)、国家自然科学基金项目(42405019)、中国气象局创新发展专项(CXFZ2025J086)及青年创新团队(CMA2023QN05)共同资助
引用文本:
李妮娜,金荣花,宫宇,代刊,曹勇,聂高臻,林建,韦青,吴海旭,罗华坤,龙明盛,王建民,2026.“风清”人工智能天气预报模型业务应用性能评估[J].气象,52(5):513-526.
LI Nina,JIN Ronghua,GONG Yu,DAI Kan,CAO Yong,NIE Gaozhen,LIN Jian,WEI Qing,WU Haixu,LUO Huakun,LONG Mingsheng,WANG Jianmin,2026.Performance Evaluation on Operational Application of the AI-Based Global Short- and Medium-Range Forecasting System—Fengqing Model[J].Meteor Mon,52(5):513-526.