Predicting how tropical cyclones (also known as hurricanes or typhoons) develop and move is notoriously tricky, and every single hour of advance warning counts.
Recently, we published a paper on WeatherNext, our AI model for global weather forecasting from @GoogleDeepmind and @GoogleResearch. To tackle extreme storms, we built WeatherNext Cyclones to seamlessly bridge the gap between global weather patterns and local storm details to completely revolutionize how we track cyclones.
This isn't just happening in a lab — WeatherNext Cyclones was put to the test during the 2025 hurricane season, marking the first time the U.S. National Hurricane Center used AI models in real-time operations. Meteorologists used it to make a historic forecast for Hurricane Melissa’s Category 5 landfall in Jamaica, giving local officials critical extra time to prepare.
Historically, meteorologists have faced a tough-trade off when attempting to track cyclones. To track a storm's path, they relied on massive, physics-based supercomputer models, which are great at capturing broad weather patterns sweeping across the planet. But to understand the intense, localized physics driving how strong the storm would get, they had to switch to entirely different "zoomed-in” regional models.
WeatherNext Cyclones can predict a storm's track, intensity, and size all at once, giving forecasters an extra full day of advance warning compared to previous systems. To put that in perspective, our three-day forecasts are now as accurate as older two-day forecasts. Historically, it took a full decade of meteorological progress to squeeze out that kind of improvement, and we’ve delivered it in a single modeling leap.
The model is also incredibly fast, generating up to 1,000 individual simulations per storm. Instead of just one “most-likely” path, forecasters get a much fuller picture of all possible outcomes, making it easier to spot dangerous events like rapid intensification (when a storm's maximum sustained winds increase by 30 knots or more in 24 hours).
Ready to learn more?
Take a deep dive into the research and technical details below:
Blog → goo.gle/4gkvEpy
@Nature article → goo.gle/4wYGzdC
Code and model weights now open source on @github → goo.gle/4qDm7xg