Google DeepMind and Research release WeatherNext 3, global AI weather model with hourly forecasts and 5-km resolution
On September 3, 2026, Google DeepMind and Google Research released WeatherNext 3, described as the most advanced global AI weather model to date. The model ingests real-time satellite observations, regenerates global forecasts hourly, and improves spatial resolution for some surface variables to about 5 kilometers. This builds on prior AI weather models like GraphCast, which generated 10-day global forecasts in about 1 minute and outperformed ECMWF's HRES on over 90% of evaluation metrics at the time.
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编辑丨% 每天早晨，人们出门之前通常只关心一个问题：今天到底会不会下雨。 但对于农业、电力调度、航空和防灾来说，天气预报远不只是「晴还是雨」。一场雷雨提前几十分钟出现，一片云层移动几公里，或者风速突然发生变化，都可能改变接下来数小时的决策。 不过大气本身就是一个变化极快、尺度极大的复杂系统。传统天气预报依靠数值天气预报（NWP），通过超级计算机求解描述大气运动的物理方程，再不断更新未来天气状态。这套方法已经相当成熟，却也意味着巨大的计算成本。 过去几年，谷歌 DeepMind 开始尝试用 AI 走另一条路。 从 GraphCast 到 GenCast，再到 WeatherNext，Google 一直在探索一个问题： 如果让 AI 直接从大量天气数据中学习大气如何变化，天气预报能不能变得更快、更细，而且覆盖整个地球？ 现在，答案又向前推进了一步。2026 年 9 月 3 日，谷歌 DeepMind 和谷歌 Research 发布了 WeatherNext 3，并称其为目前最先进的全球 AI 天气模型。它最明显的变化并不是简单地「换了一个更大的模型」，模型能够直接吸收 实时卫星观测 ，每小时重新生成全球预报，并将部分地表变量的空间分辨率提高到约 5 公里。 原文链接： https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/ 以前所未有的分辨率快速预报 传统 NWP 系统的优势在于物理机制清晰，但它需要巨大的计算资源。与此同时，全球天气数据也并不是随时、随地、以相同精度存在的。而这给了谷歌一个切入口。 2023年，谷歌 DeepMind 发布 GraphCast 时，AI 天气预报第一次大规模展示出令人印象深刻的速度优势。GraphCast 能够在约 1 分钟内生成未来 10 天的全球天气预测，而传统高水平数值预报系统则需要超级计算机运行数小时。它在当时的测试中，在超过 90% 的评估指标上优于 ECMWF 的高分辨率预报系统 HRES。 而这次推出的 WeatherNext 3 能以多种空间分辨率生成每小时预报，保持从全球宽广风向模式到局部地形的物理一致性。 具体来说，它能以 5 公里的分辨率可视化关键地表变量——如温度和湿度
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