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热带气旋的路径、强度和结构受到不同尺度大气过程共同影响,往往难以由同一套模型同时准确预测。谷歌DeepMind团队开发的人工智能(AI)模型WeatherNext Cyclones(WN-C),在多项业务化测试中改善了热带气旋路径和强度预报,并将达到同等路径预报精度的时间平均提前1天以上。2026年8月6日,相关研究成果在线发表于《Nature》。

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(图片来源:美国宇航局地球观测站/Michala Garrison)

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    出版时间: 2026-08-13
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    热带气旋的路径、强度和结构受到不同尺度大气过程共同影响,往往难以由同一套模型同时准确预测。谷歌DeepMind团队开发的人工智能(AI)模型WeatherNext Cyclones(WN-C),在多项业务化测试中改善了热带气旋路径和强度预报,并将达到同等路径预报精度的时间平均提前1天以上。2026年8月6日,相关研究成果在线发表于《Nature》。

    . AI让台风预警提前1天以上. 科技导报, 2026 , 44 (15) : 2 -2 .
    . Science & Technology Review, 2026 , 44 (15) : 2 -2 .
    热带气旋的路径、强度和结构受到不同尺度大气过程共同影响,往往难以由同一套模型同时准确预测。谷歌DeepMind团队开发的人工智能(AI)模型WeatherNext Cyclones(WN-C),在多项业务化测试中改善了热带气旋路径和强度预报,并将达到同等路径预报精度的时间平均提前1天以上。2026年8月6日,相关研究成果在线发表于《Nature》。
    全球数值天气预报模型擅长模拟大尺度环流,通常更有利于判断风暴路径;高分辨率区域模型能够刻画气旋核心,却可能牺牲部分路径预报能力。WN-C模型使用约28 km分辨率的全球大气资料,同时学习近5000个历史热带气旋的路径、强度和结构信息,试图在一套模型中兼顾这2类任务。
    在5天预报期内,WN-C的路径预测比顶尖全球数值模型平均提前30多小时达到同等精度;其3天强度预报的平均绝对误差,比专门的高分辨率区域模型低3.75节。研究结果还表明,准确预测气旋强度并不必然依赖极高空间分辨率,历史气旋资料与全球大气状态的联合训练同样能够提供有效约束。
    WN-C还提高了对“快速增强”的概率预报能力。快速增强通常指热带气旋最大持续风速在24 h内增加至少30节。模型的临界成功指数由不足0.3提高到约0.5。其函数生成网络每次预报只需要调用1次神经网络,在单张TPU v5p芯片上生成15天完整天气轨迹耗时不到1 min,可用于构建规模更大的集合预报,估计低概率但高影响的风险。
    自2025年6月起,WN-C开始业务化运行,预测结果通过谷歌天气实验室Weather Lab实时发布。2025年大西洋飓风季的评估显示,将其加入官方共识集合后,路径预测准确率平均提高28%,强度预测准确率提高6%。
    (综合:《Nature》、Google DeepMind、美国国家海洋和大气管理局)

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    2种不同金属材料的力学参数

    Family
    属数
    Number of
    genus
    种数
    Number of
    species
    占总种数比例
    Percentage of
    total species (%)

    Genus
    种数
    Number of
    species
    占总种数比例
    Percentage of total
    species (%)
    鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
    小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
    多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
    红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
    小菇属 Mycena 11 5.26
    光柄菇属 Pluteus 5 2.39
    红菇属 Russula 17 8.13
    栓菌属 Trametes 5 2.39
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