WeatherNext Use Cases: 5 Real-World Applications for AI Weather Forecasting

WeatherNext is primarily used for operational-style medium-range weather forecasting, tropical cyclone tracking, research prototyping, cloud-based forecast services, and educational demonstrations.

WeatherNext is a family of research-grade neural-network models developed by Google DeepMind for global atmospheric and tropical-cyclone forecasting. Its architecture combines global conditioning, icosahedral mesh-based graph neural networks, and lat-lon grid encoders to produce predictions ranging from days to weeks ahead. Below are the five typical WeatherNext use cases that researchers, meteorologists, and developers rely on.

Operational-Style Medium-Range Forecasting

WeatherNext's flagship application is generating deterministic weather predictions up to approximately 15 days in advance. The model outputs temperature, wind, geopotential height, and humidity on a 0.25° (~30 km) resolution grid.

To execute this use case:

1. Load pre-trained weights — Download from the public Google Cloud bucket, e.g., WeatherNext2_<2025_model1.npz
2. Initialize the predictor — Use fgn.construct_predictor() from weathernext2/fgn.py with the stored configuration
3. Run auto-regressive rollout — Execute via rollout.auto_regressive_rollout() in weathernext/utils/rollout.py on TPU or GPU hardware

The rollout.py module implements the inference loop that repeatedly calls the predictor to generate multi-step forecasts. Each step typically represents 6 or 24 hours of simulated time.

import weathernext.weathernext2.fgn as fgn
import weathernext.utils.rollout as rollout
import weathernext.utils.checkpoint as ckpt
import xarray as xr

# Load checkpoint from Google Cloud Storage

ckpt_path = "gs://dm_graphcast/WeatherNext2_<2025_model1>.npz"
checkpoint = ckpt.load_checkpoint(ckpt_path)

# Build predictor from stored config

predictor = fgn.construct_predictor(checkpoint.config)

# Prepare initial conditions from ERA5 HRES

init_ds = xr.open_zarr("gs://weatherbench2/hres/2024-01-01.zarr")

# Generate 5-day forecast (120 hours) on TPU

forecast = rollout.auto_regressive_rollout(
    predictor=predictor,
    inputs=init_ds,
    num_steps=5,           # 5 steps × 24 hours = 120 hours

    step_size="24h",
    device="tpu",
)

# Visualize 850 hPa temperature

forecast["t850"].isel(time=0).plot()

Tropical Cyclone Tracking

WeatherNext provides probabilistic forecasts of cyclone position, intensity, and 100-meter wind speeds using the same Functional Generative Network (FGN) architecture as the global model.

Key implementation details:

• The "Cyclones Mini" variant runs at 1° resolution on a single GPU or TPU
• After rollout completion, weathernext/cyclones/direct_tracker.py extracts storm tracks from output fields
• The cyclone tracker processes surface pressure and wind fields to identify vortex centers and propagate tracks through time

This specialized use case demonstrates WeatherNext's modular design—the core FGN predictor in weathernext2/fgn.py serves both global forecasting and targeted cyclone applications through configuration adjustments rather than architectural changes.

Research and Model Development

WeatherNext's codebase is structured for extensibility, enabling researchers to experiment with:

Component	Location	Customization Potential
Graph-based encoders	weathernext1_graph/	Modify mesh connectivity, message-passing rules
Generative models	weathernext1_gen/	Replace diffusion generators, adjust noise schedules
Loss functions	weathernext2/fgn.py	Implement custom training objectives

The sub-packages weathernext1_graph and weathernext1_gen expose core components like graphcast.py and gencast.py for rapid prototyping. Researchers can swap individual modules without reimplementing the entire pipeline.

In weathernext2/architecture.py, the forward pass explicitly separates mesh GNN operations, grid encoders, and global conditioning layers—making targeted modifications straightforward.

Cloud-Based Data-as-a-Service

Organizations requiring forecast data without local infrastructure can access WeatherNext outputs through Google Cloud services:

• BigQuery — Structured access to historical and current forecast archives
• Vertex AI — Managed inference endpoints for on-demand predictions
• Earth Engine — Geospatial analysis and visualization of forecast fields
• Weather Lab portal — Web interface for browsing forecast products
• OpenMeteo API — REST API for programmatic data retrieval

This use case eliminates the need to manage TPUs, store model weights, or implement rollout logic. The cloud services expose the same model outputs generated by the local inference pipeline, ensuring consistency across deployment modes.

Educational Demos and Tutorials

The interactive notebook docs/weathernext2/wn2_demo.ipynb provides a complete walkthrough of WeatherNext fundamentals:

• Model initialization and weight loading
• Configuring the FGN predictor
• Executing short rollouts
• Visualizing temperature and wind fields with matplotlib

This Colab-ready demonstration requires no local setup—users execute cells in a cloud environment with pre-configured dependencies. The notebook serves as both learning resource and verification tool, confirming that local installations reproduce expected outputs.

Summary

• Medium-range forecasting — WeatherNext generates 15-day deterministic predictions on 0.25° grids using rollout.auto_regressive_rollout()
• Cyclone tracking — The Cyclones Mini variant plus direct_tracker.py produces probabilistic storm forecasts
• Research prototyping — Modular sub-packages enable experimentation with graph encoders, generators, and loss functions
• Cloud services — Google Cloud integrations provide forecast access without local model execution
• Education — wn2_demo.ipynb offers hands-on introduction to the complete inference pipeline

Frequently Asked Questions

What hardware is required to run WeatherNext locally?

Full-scale WeatherNext 2 inference requires TPU accelerators or high-end GPUs due to the model's size and the computational demands of auto-regressive rollout. The Cyclones Mini variant can execute on a single GPU or TPU. The wn2_demo.ipynb notebook provides free TPU access through Google Colab for evaluation purposes.

How does WeatherNext differ from traditional numerical weather prediction?

WeatherNext replaces physics-based simulation with learned neural dynamics. In weathernext2/architecture.py, the forward pass uses graph neural networks on an icosahedral mesh rather than solving discretized fluid equations. This enables faster inference—minutes versus hours for traditional methods—while maintaining competitive accuracy over medium-range horizons.

Can WeatherNext be fine-tuned on regional or specialized datasets?

Yes. The modular structure in weathernext1_graph/ and weathernext1_gen/ allows researchers to adapt components for specific domains. The predictor construction in fgn.construct_predictor() accepts configuration overrides for input variables, output pressures levels, and spatial resolution, supporting regional customization without full retraining.

Where are the pre-trained model weights hosted?

Official WeatherNext 2 checkpoints are distributed through gs://dm_graphcast/ and other Google Cloud Storage buckets. The ckpt.load_checkpoint() function in weathernext/utils/checkpoint.py handles authentication and caching automatically when running in Google Cloud environments.

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