Google DeepMind announced Thursday what it claims is a major breakthrough in hurricane forecasting, introducing an artificial intelligence system that can predict both the path and intensity of tropical cyclones with unprecedented accuracy — a longstanding challenge that has eluded traditional weather models for decades.

The company launched Weather Lab, an interactive platform showcasing its experimental cyclone prediction model, which generates 50 possible storm scenarios up to 15 days in advance. More significantly, DeepMind announced a partnership with the U.S. National Hurricane Center, marking the first time the federal agency will incorporate experimental AI predictions into its operational forecasting workflow.

“We are presenting three different things,” said Ferran Alet, a DeepMind research scientist leading the project, during a press briefing Wednesday. “The first one is a new experimental model tailored specifically for cyclones. The second one is, we’re excited to announce a partnership with the National Hurricane Center that’s allowing expert human forecasters to see our predictions in real time.”

The announcement marks a critical juncture in the application of artificial intelligence to weather forecasting, an area where machine learning models have rapidly gained ground against traditional physics-based systems. Tropical cyclones — which include hurricanes, typhoons, and cyclones — have caused $1.4 trillion in economic losses over the past 50 years, making accurate prediction a matter of life and death for millions in vulnerable coastal regions.

Why traditional weather models struggle with both storm path and intensity

The breakthrough addresses a fundamental limitation in current forecasting methods. Traditional weather models face a stark trade-off: global, low-resolution models excel at predicting where storms will go by capturing vast atmospheric patterns, while regional, high-resolution models better forecast storm intensity by focusing on turbulent processes within the storm’s core.

“Making tropical cyclone predictions is hard because we’re trying to predict two different things,” Alet explained. “The first one is track prediction, so where is the cyclone going to go? The second one is intensity prediction, how strong is the cyclone going to get?”

DeepMind’s experimental model claims to solve both problems simultaneously. In internal evaluations following National Hurricane Center protocols, the AI system demonstrated substantial improvements over existing methods. For track prediction, the model’s five-day forecasts were on average 140 kilometers closer to actual storm positions than ENS, the leading European physics-based ensemble model.

More remarkably, the system outperformed NOAA’s Hurricane Analysis and Forecast System (HAFS) on intensity prediction — an area where AI models have historically struggled. “This is the first AI model that we are now very skillful as well on tropical cyclone intensity,” Alet noted.

How AI forecasts beat traditional models on speed and efficiency

Beyond accuracy improvements, the AI system demonstrates dramatic efficiency gains. While traditional physics-based models can take hours to generate forecasts, DeepMind’s model produces 15-day predictions in approximately one minute on a single specialized computer chip.

“Our probabilistic model is now even faster than the previous one,” Alet said. “Our new model, we estimate, is probably around one minute” compared to the eight minutes required by DeepMind’s previous weather model.

This speed advantage allows the system to meet tight operational deadlines. Tom Anderson, a research engineer on DeepMind’s AI weather team, explained that the National Hurricane Center specifically requested forecasts be available within six and a half hours of data collection — a target the AI system now meets ahead of schedule.

National Hurricane Center partnership puts AI weather forecasting to the test

The partnership with the National Hurricane Center validates AI weather forecasting in a major way. Keith Battaglia, senior director leading DeepMind’s weather team, described the collaboration as evolving from informal conversations to a more official partnership allowing forecasters to integrate AI predictions with traditional methods.

“It wasn’t really an official partnership then, it was just sort of more casual conversation,” Battaglia said of the early discussions that began about 18 months ago. “Now we’re sort of working toward a kind of a more official partnership that allow us to hand them the models that we’re building, and then they can decide how to use them in their official guidance.”

The timing proves crucial, with the 2025 Atlantic hurricane season already underway. Hurricane center forecasters will see live AI predictions alongside traditional physics-based models and observations, potentially improving forecast accuracy and enabling earlier warnings.

Dr. Kate Musgrave, a research scientist at the Cooperative Institute for Research in the Atmosphere at Colorado State University, has been evaluating DeepMind’s model independently. She found it demonstrates “comparable or greater skill than the best operational models for track and intensity,” according to the company. Musgrave stated she’s “looking forward to confirming those results from real-time forecasts during the 2025 hurricane season.”

The training data and technical innovations behind the breakthrough

The AI model’s effectiveness stems from its training on two distinct datasets: vast reanalysis data reconstructing global weather patterns from millions of observations, and a specialized database containing detailed information about nearly 5,000 observed cyclones from the past 45 years.

This dual approach is a departure from previous AI weather models that focused primarily on general atmospheric conditions. “We are training on cyclone specific data,” Alet explained. “We are training on IBTracs and other types of data. So IBTracs provides latitude and longitude and intensity and wind radii for multiple cyclones, up to 5000 cyclones over the last 30 to 40 years.”

The system also incorporates recent advances in probabilistic modeling through what DeepMind calls Functional Generative Networks (FGN), detailed in a research paper released alongside the announcement. This approach generates forecast ensembles by learning to perturb the model’s parameters, creating more structured variations than previous methods.

Past hurricane predictions show promise for early warning systems

Weather Lab launches with over two years of historical predictions, allowing experts to evaluate the model’s performance across all ocean basins. Anderson demonstrated the system’s capabilities using Hurricane Beryl from 2024 and the notorious Hurricane Otis from 2023.

Hurricane Otis proved particularly significant because it rapidly intensified before striking Mexico, catching many traditional models off guard. “Many of the models were predicting that the storm would remain relatively weak throughout its lifetime,” Anderson explained. When DeepMind showed this example to National Hurricane Center forecasters, “they said that our model would have likely provided an earlier signal of the potential risk of this particular cyclone if they had it available at the time.”

What this means for the future of weather forecasting and climate adaptation

The development signals artificial intelligence’s growing maturation in weather forecasting, following recent breakthroughs by DeepMind’s GraphCast and other AI weather models that have begun outperforming traditional systems in various metrics.

“I think for a pretty early, you know, the first few years, we’ve been mostly focusing on scientific papers and research advances,” Battaglia reflected. “But, you know, as we’ve been able to show that these machine learning systems are rivaling, or even outperforming, the kind of traditional physics-based systems, having the opportunity to take them out of the sort of scientific context into the real world is really exciting.”

The partnership with government agencies is a crucial step toward operational deployment of AI weather systems. However, DeepMind emphasizes that Weather Lab remains a research tool, and users should continue relying on official meteorological agencies for authoritative forecasts and warnings.

The company plans to continue gathering feedback from weather agencies and emergency services to improve the technology’s practical applications. As climate change potentially intensifies tropical cyclone behavior, advances in prediction accuracy could prove increasingly vital for protecting vulnerable coastal populations worldwide.

“We think AI can provide a solution here,” Alet concluded, referencing the complex interactions that make cyclone prediction so challenging. With the 2025 hurricane season underway, the real-world performance of DeepMind’s experimental system will soon face its ultimate test.