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.”
