WeatherNext: AI model achieves breakthrough in forecasting cyclones

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.

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.

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.

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.

How WeatherNext predicts weather and cyclones

Predicting cyclones has typically forced a trade-off requiring two distinct modeling techniques. A cyclone's track (where it goes) is steered by massive, global atmospheric currents, which before now have been best modeled by coarser global models. However, a cyclone’s intensity (how strong it gets) is driven by highly localized, fine-scale thermodynamic physical processes around its core, which are best modeled by specialized, higher resolution, local models.

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.

The model was co-trained on two distinct data modalities: global weather dynamics and expert-curated historical cyclone observations. By training end-to-end on nearly 20 terabytes of global atmospheric data and the historical IBTrACS database spanning nearly 5,000 historical storms, the model learns complex atmospheric patterns and how to model extreme weather.

Our model uses Functional Generative Networks (FGNs) to efficiently produce ensembles of different predictions, which captures the inherent uncertainty of the weather. We can now generate a single 15-day forecast in less than a minute on a TPU, empowering forecasters to quickly evaluate the probability distribution of potentially devastating tail-risks. Last year, our system produced 50 predictions at a time, matching global physics models. This year we scaled our ensemble size to 1,000 members, capturing rare but consequential scenarios like rapid intensification events, as occurred during Hurricane Melissa in 2025.

Up until now, operating at very high spatial resolution has been considered the main driver for making accurate intensity forecasts. However, WeatherNext Cyclones only needs data with a resolution of 28x28km, 100x coarser than traditional models. A smaller version of the model, WeatherNext 2-mini, which operates at a coarser 111x111km resolution, also shows great performance. This has surprised scientists, and it remains an open research question to fully understand how our models produce such accurate predictions at this resolution. We hope that, together with the research community, we can find out.

Opening up WeatherNext to the research community

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.

We are also releasing two sets of similar models: WeatherNext Cyclones, which ran during the hurricane season (results can be seen in the paper); and WeatherNext 2, a later update that we operationalized in October. Additionally, we are releasing WeatherNext 2-mini, a compact version of the model that can run on a single TPU in a free public Colab notebook.

You can explore our latest cyclone forecasts on Weather Lab, which we recently refreshed with a new interface and expanded to include global weather forecasts alongside cyclone tracks. Weather Lab now lets you visualize WeatherNext predictions for temperature, precipitation, wind speed, and more, all in a single view. Both Weather Lab and WeatherNext models are a part of Google Earth AI.

Pushing the frontiers of AI for weather forecasting

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.

Note: For official weather forecasts and warnings, refer to your local meteorological agency or national weather service.

Acknowledgements

This research was co-developed by Google DeepMind and Google Research teams.

We’d like to thank our collaborators NOAA/NWS/NCEP National Hurricane Center, Cooperative Institute for Research in the Atmosphere (CIRA) and the UK Met Office for their partnership and contributions to the paper.

This work reflects the contributions of the paper’s co-authors: Ferran Alet, Tom Andersson, Ilan Price, Stratis Markou, Andrew El-Kadi, Dominic Masters, Amy Li, Samier Merchant, Natalie Williams,Gregory Thornton, Ken MacKay, Olivia Graham, Akib Uddin, Ben Gaiarin, Devaja Shah, Elinor Kruse, Wallace Hogsett, David Zelinsky, John Cangialosi, Jonathan Martinez, James Franklin, Mark DeMaria, Kate Musgrave, Caroline L. Bain, Helen Titley, Jacklynn Stott, Remi Lam, Aaron Bell, Paul Komarek, Matthew Willson, Alvaro Sanchez-Gonzalez, and Peter Battaglia.