README

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_ [!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.:

bash
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.

Thes