DeepMind's WeatherNext AI gives forecasters an extra day to predict deadly cyclones

DeepMind Says Its AI Can Predict Hurricanes Earlier Than Everyone Else

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. 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. In a paper published on Thursday in Nature, researchers show 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. "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. Historically, bringing forecasts forward by a day would take a decade of work, the researchers say. 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." 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. "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." 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. 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. Even the DeepMind researchers working on the model don't fully understand how the AI model produces such accurate predictions, given 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. 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. 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. "That's something that, with our computing power, we simply can't do with our existing numerical models," Musgrave says. 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." 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. "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."

DeepMind AI gives an extra day of warning ahead of deadly cyclones | New Scientist

DeepMind has developed an AI model that can predict deadly cyclones one day further out than existing methods, potentially saving lives by allowing more accurate and timely decisions about evacuations. Many existing weather forecasts are based on physics simulations run on powerful supercomputers that model and extrapolate weather patterns as accurately as possible. AI promises a faster and less costly solution. WeatherNext is able to generate a 15-day forecast in less than a minute on one of Google's custom Tensor Processing Unit chips. Meanwhile, physics-based models can consume days of high-powered computer time. The new model works on a simulation of the atmosphere where the smallest cell is 28 square kilometres - making it a hundred times coarser than traditional models. It was trained on nearly 20 terabytes of global atmospheric data, as well as data on 5000 historical storms, which Ferran Alet at DeepMind says was crucial to improving WeatherNext's accuracy at predicting the behaviour of new cyclones. The DeepMind team compared its WeatherNext AI model with Google's GenCast model, the European Centre for Medium-Range Weather Forecasts's ENS model and the National Oceanic and Atmospheric Administration's Hurricane Forecast Analysis System. It found that WeatherNext was able to produce accurate predictions three days out that could only be matched by current models two days out, in terms of maximum wind speed and the distance error between a cyclone's predicted location and actual eventual location. "The longer time goes [on], the worse you're going to do, because weather is chaotic. So the question is, how much can we push this chaos barrier? How much can we see into the future?" says Alet. Because of the time taken to publish the work in an academic journal, he says that things have moved on since, and that DeepMind is actively working on building even faster and more accurate models. The firm has a long history of attempting to improve weather forecasting. In 2021, it developed an extremely short-term and local model that could predict whether it would rain or not in the next 90 minutes more accurately than existing models. In 2023, it released a model that could provide accurate 10-day weather forecasts, and in 2024, it launched a tool that dramatically slashed the time and energy needed to make forecasts. Hannah Cloke at the University of Reading, UK, who wasn't involved in the research, says she has never experienced a faster and more dramatic shake-up in meteorology than the effect of AI in the past couple of years. "This is one of the most exciting fields to work in at the moment, and one of the reasons is the rise in machine-learning forecasting and the absolute speed and power with which we're moving forward in this field," she says. "You go on holiday for a week... and find out something else has happened." Cloke says that advances are far outpacing the speed of academic journals, meaning that the cutting edge is actually 18 months ahead of what we read in papers like this one. Global forecasting agencies have tested AI models alongside their traditional physics models in recent years, but there is increasing consensus that AI models are faster, cheaper and, in many cases, more effective than physics-based methods. However, it is still important to maintain meteorological expertise, says Cloke. "A lot of the new people that I encounter working in this field have come through data science and they don't necessarily have the meteorological background that you do need in order to to understand whether the models are producing something sensible or not," says Cloke. "So there is a real tension there between the new techniques that are really exciting and making sure that we are able to take the correct decisions in terms of deploying these models." But despite their rapid success in meteorology, not everyone is convinced that AI models are the future of forecasting - or, at least, they need to be tested far more rigorously before we ditch traditional models. Tim Palmer at the University of Oxford is concerned that models will struggle to predict truly anomalous, one-off events because, by their nature, they will have no analogue in the training data. For that reason, he has advocated for a new kind of test that removes these events from training data, then assesses AI models on whether or not they can successfully predict them. He believes that this ability will become crucial as climate change makes weather patterns increasingly chaotic. "We don't test for that," he says. "Relying on an AI which has been trained on 40 or 50 years of past data could lead us up the garden path." Another concern for Palmer is that weather models and climate change models share as much as 90 per cent of their code. Giving up numerical, physics-based models in weather forecasting would be detrimental to climate modellers, forcing them to work without the knowledge and data gleaned from weather forecasting, or to replicate all the work that used to take place in weather forecasting in order to continue their work as before. "The code is virtually identical," says Palmer. "If we start to lose our capability of doing weather forecasting with physics-based models, the climate predictions will suffer."

Our WeatherNext 2 AI model demonstrated a massive leap forward in predicting cyclones.

Predicting how hurricanes and cyclones develop is a longstanding challenge, and every hour of warning counts. Tropical cyclones are among the most destructive weather events on Earth, and for meteorologists, issuing accurate, timely warnings is a constant race against time. Today, in a paper published in Nature, Google researchers show that our WeatherNext AI model achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. This represents a massive leap forward -- roughly a decade of meteorological progress in one model. To help build climate resilience worldwide, we are now open-sourcing the WeatherNext 2 model to the global research community. For more information, check the Google DeepMind blog.