WeatherNext: AI model achieves breakthrough in forecasting cyclones

WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model.

Predicting how dangerous cyclones develop is a longstanding challenge where every hour counts. Tropical cyclones — also known as hurricanes or typhoons — are among the most destructive weather phenomena on Earth, responsible for more than 700,000 deaths and $1.4 trillion in economic losses globally over the past 50 years. For forecasters, issuing timely, accurate warnings is a constant race against time.

Today, in a paper published in Nature , we show that our WeatherNext AI model achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, our model gives forecasters an extra day’s worth of predictive accuracy: our three-day forecasts are as good as what prior models were able to provide for only the next two days. This scale of improvement corresponds roughly to a decade’s worth of meteorological progress.

Our WeatherNext model bridges this gap by improving forecasting for global weather overall as well as cyclones. It is a single AI model that predicts a tropical cyclone’s track, intensity, and wind structure with state-of-the-art accuracy. It achieves this breakthrough through a unique combination of its training, architecture and approach to low resolution inputs.

Alongside our Nature paper, we are open sourcing the code and model weights, making them freely available for anyone to build on. This includes academic research, operational forecasting, or developing more specialized, localized models. We hope to accelerate progress across the global weather community and empower meteorological agencies, researchers, and nonprofits to better predict weather events of all kinds and make key decisions to protect lives and infrastructure.

Our research has already had real-world impact. During the 2025 hurricane season, our model helped the NHC to make a historic forecast for Hurricane Melissa by predicting the storm’s rapid intensification and landfall in Jamaica. This enabled the NHC to issue an advance warning, giving teams on the ground critical time to prepare. This year, we continue to work together and are now predicting 1,000 possible scenarios for each cyclone to help support forecasters in their decision-making.

We have achieved a historic breakthrough by gaining more than a full day of lead time for predicting cyclones — delivering an advance equivalent to a decade of meteorological progress. As we prepare for future storm seasons, we invite researchers, meteorological agencies, and experts to partner with us, build on our open source models, and explore our forecasts on Weather Lab. By combining advanced machine learning with the indispensable real-world expertise of human forecasters, we aim to create a collaborative weather forecasting ecosystem that can save lives and help communities adapt to a changing climate.

This collaborative work brought together AI researchers and engineers at Google DeepMind and Google Research, with expert forecasters at the National Hurricane Center (NHC), the Cooperative Institute for Research in the Atmosphere (CIRA), the UK Met Office , and weather agencies around the world.

Weather affects everyone. Given this broad impact, we are now open sourcing our WeatherNext 2 and WeatherNext Cyclones models used during the hurricane season. By making this technology openly available, we hope to empower the research community and amplify AI's impact in building more resilient communities – whether that be providing local forecasters with the tools they need to prepare for natural disasters , supporting the growth of renewable energy, or anticipating extreme weather.

Starting from global atmospheric conditions during Hurricane Milton (October 2024), WeatherNext Cyclones iteratively predicts both global weather patterns and fine-scale cyclone tracks up to 15 days in advance. Running a 1,000-member ensemble generates localised probability maps of tropical storm to hurricane-force winds.