DeepMind Says Its AI Can Predict Hurricanes Earlier Than Everyone Else
DeepMind Says Its AI Can Predict Hurricanes Earlier Than Everyone Else
DeepMind 表示其人工智能预测飓风的时间比以往任何技术都更早
In October 2025, a storm brewed over the Caribbean Sea. Weather models differed on its trajectory. Would it remain weak and end up in Haiti, or would it intensify and head to Jamaica? Artificial intelligence model WeatherNext, developed by Google’s DeepMind and Google Research, went with the latter. Five days before landfall, it predicted with 80 percent confidence that the storm system would hit Jamaica as a Category 5 hurricane.
2025 年 10 月，加勒比海上空酝酿着一场风暴。气象模型对其轨迹的预测各不相同。它是会保持弱势并最终抵达海地，还是会增强并直奔牙买加？由 Google DeepMind 和 Google Research 开发的人工智能模型 WeatherNext 给出了后者的预测。在风暴登陆前五天，它以 80% 的置信度预测该风暴系统将以五级飓风的强度袭击牙买加。
Hurricane Melissa was catastrophic, causing flooding and landslides across Jamaica. But the AI model helped forecasters give an earlier warning to communities in its path, so they could better prepare.
“梅丽莎”飓风造成了灾难性后果，在牙买加全境引发了洪水和山体滑坡。但该人工智能模型帮助预报员提前向受影响地区的社区发出了预警，使他们能够做好更充分的准备。
In a paper published on Thursday in Nature, researchers show that the WeatherNext AI model can predict cyclones with unprecedented accuracy. On average, it gives forecasters a day more lead time than existing models; this means its predictions three days out are as accurate as previous models’ predictions two days out. On the ground, that extra day can mean a lot.
在周四发表于《自然》杂志的一篇论文中，研究人员展示了 WeatherNext 人工智能模型能够以史无前例的准确度预测气旋。平均而言，它比现有模型为预报员多争取了一天的提前量；这意味着它对三天后的预测，其准确度与以往模型对两天后的预测相当。在实际应用中，多出的一天意义重大。
“Even a few hours can make a difference,” says Mike Brennan, director of the US National Hurricane Center. Organizing evacuations, staging supplies, and moving resources to respond to a hurricane risk are all time-sensitive tasks—and making the wrong decision can have big consequences. “Time is really golden when it comes to those types of decisions, so the ability to push forecast accuracy out as much as a day beyond what we’ve previously been able to do is really valuable,” he says.
美国国家飓风中心主任迈克·布伦南（Mike Brennan）表示：“哪怕是几个小时的差别也至关重要。”组织疏散、储备物资以及调动资源以应对飓风风险，这些都是对时间要求极高的任务，而错误的决策可能会带来严重的后果。他说：“在处理这类决策时，时间确实是金钱，因此能够将预报准确度比以往水平再提前一天，是非常有价值的。”
Historically, bringing forecasts forward by a day would take a decade of work, the researchers say.
研究人员表示，从历史上看，将预报时间提前一天通常需要十年的努力。
Modeling extreme events can be challenging for AI. Machine learning requires ample training data in order to make future predictions, but extreme events are by nature rare occurrences. “We don’t have that much cyclone data, but we have a lot of weather data,” says Ferran Alet, a research scientist at Google DeepMind and one of the paper’s lead authors. “So what we did was train a model to be both good at weather as well as cyclones.”
对极端事件进行建模对人工智能来说极具挑战性。机器学习需要充足的训练数据才能做出未来预测，但极端事件本质上是罕见的。Google DeepMind 的研究科学家、该论文的主要作者之一费兰·阿莱特（Ferran Alet）说：“我们没有那么多的气旋数据，但我们有大量的气象数据。所以我们所做的是训练一个模型，使其既擅长预测天气，也擅长预测气旋。”
Hurricanes are particularly difficult to predict because they operate at multiple spatial scales, says Kate Musgrave, tropical cyclone group lead at the Cooperative Institute for Research in the Atmosphere, and an author on the paper. Predicting a storm’s track—which direction it’s traveling—requires data about weather on a global scale, taking in information such as the location of cold fronts and prevailing winds. Predicting a storm’s intensity, however, requires much smaller-scale data focused specifically on the local atmospheric and ocean conditions.
大气研究合作研究所（CIRA）热带气旋小组负责人、该论文作者之一凯特·马斯格雷夫（Kate Musgrave）表示，飓风之所以特别难以预测，是因为它们在多个空间尺度上运作。预测风暴的路径（即它移动的方向）需要全球尺度的天气数据，包括冷锋位置和盛行风等信息。然而，预测风暴的强度则需要更小尺度的数据，专门聚焦于局部的气象和海洋条件。
“That’s something we just don’t get from these global models,” Musgrave says. While earlier AI models have done well at predicting a storm’s track, “intensity they could not do well at all.”
马斯格雷夫说：“这是我们无法从这些全球模型中获得的信息。”虽然早期的人工智能模型在预测风暴路径方面表现良好，但“它们在强度预测方面表现得很差”。
It’s critical to predict both: A change in intensity can mean the difference between a relatively weak storm and a major hurricane. Sometimes—as in the case of Hurricane Melissa—a storm system can intensify rapidly, developing into an emergency situation overnight. Melissa marked the first time the National Hurricane Centre was able to predict a Category 5 hurricane when the storm was only at a Category 1 stage.
同时预测这两者至关重要：强度的变化可能意味着一场相对较弱的风暴与一场大型飓风之间的区别。有时——正如“梅丽莎”飓风的情况——风暴系统可能会迅速增强，一夜之间演变成紧急情况。梅丽莎飓风是国家飓风中心首次在风暴仅处于一级阶段时，就成功预测出其将发展为五级飓风的案例。
Before the WeatherNext model was used in live forecasts, researchers tested it on retrospective data. “The results were so good that we were skeptical that we would actually see that in the real-time demonstration,” Musgrave says. But when forecasters started adopting the model into their operations, this performance held true. “I think everybody was surprised at just how well it did,” Musgrave says.
在 WeatherNext 模型投入实时预报之前，研究人员利用回顾性数据对其进行了测试。马斯格雷夫说：“结果好到让我们怀疑在实时演示中是否真的能达到这种水平。”但当预报员开始将该模型应用于实际操作时，其表现依然出色。马斯格雷夫说：“我认为每个人都对它的表现感到惊讶。”
Even the DeepMind researchers working on the model don’t fully understand how the AI model produces such accurate predictions, given that it uses much lower-resolution atmospheric data than traditional models require to forecast storm intensity. “When we told the community that our model was only using relatively coarse resolution, they were shocked, because that means that the lower-resolution inputs capture more signal about what’s going to happen than previously believed,” Alet says.
即使是开发该模型的 DeepMind 研究人员，也并不完全理解该人工智能模型是如何做出如此准确预测的，因为它使用的气象数据分辨率远低于传统模型预测风暴强度所需的分辨率。阿莱特说：“当我们告诉业界我们的模型仅使用相对粗糙的分辨率时，他们感到震惊，因为这意味着低分辨率的输入捕捉到了比以往认为的更多的未来演变信号。”
The AI model must be picking up on something in the lower-resolution data that allows it to make predictions about storm intensity, but the researchers don’t know what. “It’s a black box at the end of the day, but that gives physicists a signal that something is happening that was not previously understood,” Alet says.
该人工智能模型一定是从低分辨率数据中捕捉到了某些信息，从而使其能够预测风暴强度，但研究人员尚不清楚具体是什么。阿莱特说：“归根结底，它是一个黑箱，但这向物理学家传递了一个信号，即某种此前未被理解的现象正在发生。”
The model doesn’t just spit out one prediction; it produces a range of potential scenarios for a developing storm. This helps to capture any potential “butterfly effect,” says Alet, where a small deviation from a trend could lead to much bigger changes down the line. Forecasters can use these outputs, alongside those of other models, to inform their predictions about how a storm system will likely unfold. Last year, the AI model created 50 scenarios per storm; now, it generates 1,000.
该模型不仅仅输出一个预测结果，它还会为正在发展的风暴生成一系列潜在情景。阿莱特说，这有助于捕捉任何潜在的“蝴蝶效应”，即趋势上的微小偏差可能会导致后续更大的变化。预报员可以将这些输出结果与其他模型的结果结合起来，为风暴系统可能的发展路径提供参考。去年，该人工智能模型每次风暴生成 50 种情景；现在，它可以生成 1,000 种。
“That’s something that, with our computing power, we simply can’t do with our existing numerical models,” Musgrave says.
马斯格雷夫说：“以我们目前的计算能力，这是我们现有的数值模型根本无法做到的。”
Brennan says DeepMind’s model is a great new tool in forecasters’ toolbox but emphasizes that it’s one of many. “There’s no guarantee that one model, because it did well last year or really did well for this particular storm, is necessarily going to be the best model for the next season or the next storm,” he says. The human element, he adds, is still critical. “A hurricane is not just a track or an intensity forecast,” he says. “It requires experts to translate that into what the impacts are going to be—and it’s the impacts that kill people.”
布伦南表示，DeepMind 的模型是预报员工具箱中一个很棒的新工具，但他强调这只是众多工具之一。他说：“没有任何模型能保证，因为它去年表现出色或在某次特定风暴中表现出色，就一定会在下一个季节或下一次风暴中成为最佳模型。”他补充说，人为因素仍然至关重要。“飓风不仅仅是路径或强度预报，”他说，“它需要专家将其转化为具体的影响评估——而正是这些影响才会造成人员伤亡。”
Google DeepMind also announced that it is open-sourcing the WeatherNext models used during hurricane season so that researchers can use and improve on them. Alet is hopeful that opening the models up to the research community could help uncover fresh insights into how cyclones work.
Google DeepMind 还宣布将开源飓风季节期间使用的 WeatherNext 模型，以便研究人员能够使用并改进它们。阿莱特希望向研究界开放这些模型，能够帮助揭示关于气旋运作机制的全新见解。
“I’m very excited about scientific discovery,” he says. “I think AI is giving us new tools to poke into the laws of the universe.”
“我对科学发现感到非常兴奋，”他说，“我认为人工智能正在为我们提供探索宇宙规律的新工具。”