Where to Find WeatherNext Documentation: Complete Guide to Google's AI Weather Models

WeatherNext documentation is located entirely within the google-deepmind/weathernext GitHub repository, spanning the root README, model-specific READMEs in docs/, runnable Colab notebooks, and inline code comments in the architecture files.

• Root README.md – high-level overview, model family summary, quick-start instructions, and licensing
• docs/ directory – model-specific guides for WeatherNext 1 Graph (GraphCast), WeatherNext 1 Gen (GenCast), and WeatherNext 2
• Colab notebooks – end-to-end runnable demos in docs/weathernext2/, docs/weathernext1_graph/, and docs/weathernext1_gen/
• Source code – extensively documented architecture files and utility modules

Primary Entry Points

Root README: Your Starting Point

The repository's [README.md](https://github.com/google-deepmind/weathernext/blob/main/README.md) serves as the central hub. It contains:

• Summary of all three WeatherNext model variants
• Installation instructions via pip install git+https://github.com/google-deepmind/weathernext.git
• Links to pretrained weights hosted on Google Cloud Storage
• Citation information and license details

Model-Specific READMEs in docs/

Each WeatherNext variant maintains dedicated documentation:

Model	Location
WeatherNext 2 (latest)	[docs/weathernext2/README.md](https://github.com/google-deepmind/weathernext/blob/main/docs/weathernext2/README.md)
WeatherNext 1 Graph (GraphCast)	[docs/weathernext1_graph/README.md](https://github.com/google-deepmind/weathernext/blob/main/docs/weathernext1_graph/README.md)
WeatherNext 1 Gen (GenCast)	[docs/weathernext1_gen/README.md](https://github.com/google-deepmind/weathernext/blob/main/docs/weathernext1_gen/README.md)

These READMEs detail architecture specifics, checkpoint file names, and hardware requirements.

Interactive Documentation: Colab Notebooks

The WeatherNext documentation includes three fully runnable notebooks that demonstrate end-to-end workflows:

• docs/weathernext2/wn2_demo.ipynb – Complete WeatherNext 2 demo covering data loading, model initialization, autoregressive rollout, and visualization
• docs/weathernext1_graph/graphcast_demo.ipynb – GraphCast deterministic forecast walkthrough
• docs/weathernext1_gen/gencast_mini_demo.ipynb – Probabilistic ensemble generation with GenCast

Each notebook can be opened directly in Google Colab from the GitHub interface.

Architecture Documentation in Source Code

Core Model Implementations

The most detailed WeatherNext documentation lives in the architecture source files themselves:

File Path	Purpose
weathernext/weathernext2/architecture.py	FGN (Forecast-Generation Network) – WeatherNext 2's fully-convolutional graph architecture
weathernext/weathernext1_graph/graphcast.py	One-step GraphCast GNN implementation
weathernext/weathernext1_gen/gencast.py	Diffusion-based ensemble forecasting engine

These files contain extensive docstrings explaining:

• Tensor shapes and dimensions
• Mesh transformation operations
• Autoregressive step functions

Shared Utilities (weathernext/utils/)

Common functionality used across all models is documented in:

• rollout.py – Autoregressive inference loop with run_autoregressive() function
• normalization.py – Input/output scaling for physical variables
• icosahedral_mesh.py – Spherical mesh construction and indexing
• autoregressive.py – Core stepping logic for multi-forecast generation

External Developer Guides

Google provides supplementary WeatherNext documentation at developers.google.com/weathernext:

• Model Guide & API Documentation – cloud deployment patterns
• Forecast Data Feed Access – Google Cloud, WeatherLab, and OpenMeteo integration

These external resources focus on operational usage rather than research implementation.

Code Example: Navigating the Documentation

# Verifying your WeatherNext installation and locating documentation

import weathernext

# Inspect the package structure to find available modules

print(dir(weathernext))

# Expected output includes: 'weathernext1_graph', 'weathernext1_gen', 

#                          'weathernext2', 'utils'

# Accessing WeatherNext 2 architecture with inline documentation

from weathernext.weathernext2 import architecture as wn2_arch

# View docstring for the main FGN class

help(wn2_arch.FGN)

# Load pretrained weights (paths documented in README)

params = wn2_arch.load_weights(
    "gs://dm_graphcast/WeatherNext2_<model_version>.npz"
)

# Run inference using documented utilities

from weathernext.utils import rollout

forecast = rollout.run_autoregressive(
    model=wn2_arch.FGN(params),
    init_state=initial_conditions,  # ERA5 or HRES data

    steps=10,                       # 60-hour forecast (10 × 6hr)

    dt_hours=6
)

Key Documentation Files Reference

Documentation Type	File Path	URL
Root overview	README.md	GitHub
WeatherNext 2 guide	docs/weathernext2/README.md	GitHub
GraphCast guide	docs/weathernext1_graph/README.md	GitHub
GenCast guide	docs/weathernext1_gen/README.md	GitHub
WeatherNext 2 demo	docs/weathernext2/wn2_demo.ipynb	GitHub
FGN architecture	weathernext/weathernext2/architecture.py	GitHub

Summary

• WeatherNext documentation is repository-native – all essential guides, examples, and API references live in google-deepmind/weathernext
• Start with the root README for installation, model selection, and checkpoint access
• Consult model-specific READMEs in docs/ for architecture details and hardware requirements
• Run Colab notebooks for hands-on learning without local setup
• Read source code directly – the architecture files (architecture.py, graphcast.py, gencast.py) contain definitive implementation details
• Reference weathernext/utils/ for shared inference patterns and data handling

Frequently Asked Questions

Where is the official WeatherNext API documentation?

The official WeatherNext documentation resides entirely within the GitHub repository. There is no separate documentation site. The root [README.md](https://github.com/google-deepmind/weathernext/blob/main/README.md) provides installation and setup, while model-specific guides in docs/ cover detailed usage. For cloud deployment patterns, Google Developers hosts supplementary guides at developers.google.com/weathernext.

How do I run WeatherNext examples without installing anything?

Open the Colab notebooks directly from GitHub. Navigate to docs/weathernext2/wn2_demo.ipynb or the GraphCast/GenCast equivalents, then click the "Open in Colab" badge. These notebooks run in Google's free tier and include pip installation cells, so no local environment is required.

What hardware do I need to run WeatherNext models?

Requirements vary by model as documented in their respective READMEs. WeatherNext 2 (FGN) runs on TPU v4 or v5e according to [docs/weathernext2/README.md](https://github.com/google-deepmind/weathernext/blob/main/docs/weathernext2/README.md). GraphCast requires less memory and can run on single GPUs for short rollouts. The Colab notebooks include fallback configurations for limited hardware.

Where are the pretrained WeatherNext model weights documented?

Checkpoint paths and filenames are listed in each model's README within docs/. The root README provides the base Google Cloud Storage bucket (gs://dm_graphcast/) and naming conventions. The [weathernext2/architecture.py](https://github.com/google-deepmind/weathernext/blob/main/weathernext/weathernext2/architecture.py) load_weights() function accepts these paths directly and handles download caching.

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