weathernext

WeatherNext

WeatherNext 2

This repo contains the code for WeatherNext 2 (WN2), the global, medium-range atmospheric and cyclone forecasting model developed by Google DeepMind and Google Research.

It also contains code for prior generation models GraphCast and GenCast.

Accessing Forecast Data Feeds

If you are interested in directly accessing daily data feeds of WN2 model outputs rather than running the model yourself, we provide them across multiple platforms:

• Google Cloud (including Earth Engine, BigQuery, and Vertex AI).
• WeatherLab (including cyclone tracks).
• OpenMeteo (including an API and interactive builder).

Learn More

• Model Guide & Documentation: Google Developers WeatherNext Guide
• WeatherNext Cyclones Paper: Operational tropical cyclone forecasting with AI
• FGN/WN2 Technical Report: Skillful joint probabilistic weather forecasting from marginals (arXiv:2506.10772)
• WeatherNext 2 Blog Post: WeatherNext 2: Our most advanced weather forecasting model
• WeatherNext Cyclones Blog Post: WeatherNext: AI model achieves breakthrough in forecasting cyclones

Older Models

This repository serves as the primary home for the WeatherNext family models. Alongside WN2, this repository also hosts the code and documentation for our legacy and specialized models:

• WeatherNext Graph: Deterministic medium-range weather forecasting using graph neural networks. Published as GraphCast.
• WeatherNext Gen: Diffusion-based ensemble forecasting for medium-range weather. Published as GenCast.

Provided Pretrained Models

This repository provides code to run the different versions of WeatherNext 2 and WeatherNext Cyclones. The only difference between them is that WN2 can also predict 100m wind. In particular, WN2 also forecasts cyclones with the exact same algorithm as WN Cyclones. Their weights are different due to independent training runs.

WeatherNext 2

1. WeatherNext2_<2025 (Used Operationally): 0.25° resolution (~30km). Fine-tuned on ECMWF HRES data and designed to be initialized directly from operational HRES initial conditions rather than ERA5 reanalysis. Trained on data through 2024. Corresponding weights files: WeatherNext2_<2025_model{1,2,3,4}.npz.

WeatherNext Cyclones - models which reproduce the results in paper

1. WeatherNextCyclones_<2025 (Used Operationally): 0.25° resolution. The model that ran live during the 2025 Atlantic hurricane season, publicly referred to as FNV3 (NHC’s postprocessed version was called GDMI). Trained on data through 2024. The paper appendix contains a partial evaluation of 2025 in NHC basins for this model checkpoint, and how the tracker improvement in September 2025 improved results. Corresponding weights files: WeatherNextCyclones_<2025_model{1,2,3,4}.npz.
2. WeatherNextCyclones_<2024: 0.25° resolution. Reproduces results from the paper on 2024. Trained on data through 2023. Corresponding weights files: WeatherNextCyclones_<2024_model{1,2,3,4}.npz.
3. WeatherNextCyclones_<2023: 0.25° resolution. Reproduces results from the paper on 2023. Trained on data through 2022. Corresponding weights files: WeatherNextCyclones_<2023_model{1,2,3,4}.npz.

WeatherNext Cyclones Mini

1. WeatherNextCyclones_Mini_<2024: 1° resolution. A lightweight version suitable for lower memory and compute constraints (e.g., local testing or single TPUs or GPUs). Not expected to match the performance of the larger versions. Forecasts the same things as WeatherNext2_<2025, including cyclones. Trained on data through 2023. Corresponding weights file: WeatherNextCyclones_Mini_<2024.npz.
2. WeatherNextCyclones_Mini_<2023: As above, but only trained on data through 2022. Corresponding weights file: WeatherNextCyclones_Mini_<2023.npz.

Evaluation results for WeatherNextCyclones_Mini can be found in the appendix of the WeatherNext Cyclones Paper.

Quick Start Guide

The easiest way to get started with WeatherNext 2 is by running our interactive Colab Notebook, which can be opened from Colaboratory. This notebook defaults to WeatherNext Cyclones Mini, which we recommend running using the v5e-1 runtime, available for free as a Colab runtime. However, the notebook can also be used to run the other models enumerated above (but these will require a v5p accelerator).

In general, we recommend running WeatherNext 2 on TPU where possible, since its implementation has been optimised for it. However, if choosing to run on GPU, the attention implementation must be switched, as shown in the demo notebook. The non-Mini models require H100 for sufficient VRAM. The Mini models should manage inference on a P100.

Pre-trained weights and sample data are available on our Google Cloud Bucket.

Inside the notebook, you will learn how to:

1. Automatically load the required model weights from our storage bucket.
2. Load initial state weather data (e.g., HRES initial conditions).
3. Initialize the WN2 (FGN) architecture.
4. Run auto-regressive rollout steps to generate a forecast prediction.
5. Visualize the outputs (e.g., temperature, wind speed, geopotential height).
6. Run the direct tracker on model outputs to obtain track data for cyclones.
7. Compute the training loss on model predictions and targets, and take a gradient step.

Setup

Installation

[!NOTE] This is research code provided as-is for the purpose of running and experimenting with the published models. There are no guarantees of API stability and future updates may introduce breaking changes without notice. We recommend pinning to a specific release.

E.g.:

pip install git+https://github.com/google-deepmind/[email protected]

Model Weights

To run WeatherNext 2 or WeatherNext Cyclones, you will need to download the pre-trained model weights. You can access the weights on Google Cloud Bucket.

Shared Utilities

The utils/ directory contains shared libraries used by multiple WeatherNext models, providing common infrastructure for autoregressive rollouts, input normalization, graph building blocks, loss computation, and JAX-compatible xarray utilities. See the per-model READMEs for model-specific code.

Training Data

Full model training requires downloading the ERA5 dataset from ECMWF, best accessed as Zarr via WeatherBench2.

Operational fine-tuning data is available via WeatherBench2’s HRES data.

These datasets may be governed by separate terms and conditions. Check that you can comply with any applicable restrictions before use.

License

Copyright 2026 Google LLC.

The Colab notebooks and the associated code are licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use these materials except in compliance with the Apache 2.0 license. You may obtain a copy of the License at: https://www.apache.org/licenses/LICENSE-2.0.

All other materials are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). You may obtain a copy of the License at: https://creativecommons.org/licenses/by/4.0/.

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY 4.0 licenses are distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

Disclaimers

This is not an officially supported Google product.

The WeatherNext models are part of an experimental research project. You are solely responsible for determining the appropriateness of using or distributing these models or any outputs they generate, and you assume all risks associated with such use or distribution and your exercise of rights and permissions granted by Google under the relevant license. Use discretion before relying on, publishing, downloading, or otherwise using these models or any of their outputs.

The WeatherNext models have not been produced in collaboration with nor endorsed by any government meteorological agency or department, and in no way replaces official alerts, warnings or notices published by such agencies.

Citations

If you use any of the WeatherNext family of models in your research, please cite our papers:

WeatherNext 2 or WeatherNext Cyclones

@article{Alet2026,
  title={Operational Tropical Cyclone Forecasting with AI},
  author={Alet, Ferran and Andersson, Tom R. and Price, Ilan and Markou, Stratis and El-Kadi, Andrew and Masters, Dominic and Li, Amy and Merchant, Samier and Williams, Natalie and Thornton, Gregory and MacKay, Ken and Graham, Olivia and Uddin, Akib and Gaiarin, Ben and Shah, Devaja and Kruse, Elinor and Hogsett, Wallace and Zelinsky, David and Cangialosi, John and Martinez, Jonathan and Franklin, James and DeMaria, Mark and Musgrave, Kate and Bain, Caroline L. and Titley, Helen and Stott, Jacklynn and Lam, Remi and Bell, Aaron and Komarek, Paul and Willson, Matthew and Sanchez-Gonzalez, Alvaro and Battaglia, Peter},
  journal={Nature},
  year={2026},
  issn={1476-4687},
  doi={10.1038/s41586-026-10953-2},
  url={https://doi.org/10.1038/s41586-026-10953-2}
}

@article{alet2025skillful,
  title={Skillful joint probabilistic weather forecasting from marginals},
  author={Alet, Ferran and Price, Ilan and El-Kadi, Andrew and Masters, Dominic and Markou, Stratis and Andersson, Tom R and Stott, Jacklynn and Lam, Remi and Willson, Matthew and Sanchez-Gonzalez, Alvaro and Battaglia, Peter},
  journal={arXiv preprint arXiv:2506.10772},
  year={2025}
}

WeatherNext 1 Gen (GenCast)

@article{price2024gencast,
  title={Probabilistic weather forecasting with machine learning},
  author={Price, Ilan and Sanchez-Gonzalez, Alvaro and Alet, Ferran and Andersson, Tom R and El-Kadi, Andrew and Masters, Dominic and Ewalds, Timo and Stott, Jacklynn and Mohamed, Shakir and Battaglia, Peter and Lam, Remi and Willson, Matthew},
  journal={Nature},
  volume={637},
  number={8044},
  year={2024},
  publisher={Nature Publishing Group},
  doi={10.1038/s41586-024-08252-9}
}

WeatherNext 1 Graph (GraphCast)

@article{lam2023learning,
  title={Learning skillful medium-range global weather forecasting},
  author={Lam, Remi and Sanchez-Gonzalez, Alvaro and Willson, Matthew and Wirnsberger, Peter and Fortunato, Meire and Alet, Ferran and Ravuri, Suman and Ewalds, Timo and Eaton-Rosen, Zach and Hu, Weihua and others},
  journal={Science},
  volume={382},
  number={6677},
  pages={1416--1421},
  year={2023},
  publisher={American Association for the Advancement of Science}
}

Acknowledgements

The WeatherNext models communicate with the following separate libraries and packages:.

• Data and products of the European Centre for Medium-range Weather Forecasts (ECMWF), as modified by Google.
• Modified Copernicus Climate Change Service information 2023.
• NOAA’s International Best Track Archive for Climate Stewardship (IBTrACS) data, first accessed on 1 Dec 2022.

Additionally, the colab notebooks include a few examples of ECMWF’s ERA5 and HRES data that can be used as input to the models.

Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. ECMWF HRES datasets Copyright statement: Copyright “© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)”. Source: www.ecmwf.int License Statement: ECMWF open data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/ Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.

Use of the third-party materials referred to above may be governed by separate terms and conditions or license provisions. Your use of the third-party materials is subject to any such terms and you should check that you can comply with any applicable restrictions or terms and conditions before use.

Contact

For feedback and questions regarding the codebase or models, contact us at [email protected].

Any information collected via email will be used in accordance with Google’s privacy policy.