kashif/weathernext2

WeatherNext 2

WeatherNext 2 is a global medium-range weather forecasting model from Google DeepMind and Google Research. One forward pass advances the state of the atmosphere by 6 hours on a 0.25° latitude/longitude grid, predicting 13 pressure levels of temperature, geopotential, wind and humidity together with surface fields, precipitation, 100m winds and a set of tropical-cyclone diagnostics.

It is a Functional Generative Network (FGN): rather than injecting a noise field into the input or running a diffusion sampler, the model draws a single 32-dimensional noise vector per ensemble member and uses it to modulate the scale and offset of every normalization layer. One draw gives one self-consistent forecast, and an ensemble is simply several draws — which here is just the batch dimension.

Variant	Resolution	Mesh nodes	Params	100m winds
WeatherNext2	0.25° (721×1440)	40,962	183.8M	yes
WeatherNextCyclones	0.25° (721×1440)	40,962	183.8M	no

These weights correspond to WeatherNext2_<2025_model{1..4}, trained on data through 2024 and fine-tuned for initialization from operational ECMWF HRES analysis. All four independently trained members (model1–model4) are included; see Ensembles.

Usage

WeatherNext 2 support is not in a released version of Transformers yet. Until huggingface/transformers#47874 is merged, install from the branch:

pip install "git+https://github.com/kashif/transformers.git@add-weathernext2" torch scipy

The model works in a normalized space; WeatherNext2FeatureExtractor owns everything physical — the per-variable normalization statistics, the calendar-derived forcings, and the residual connection back to an atmospheric state.

import numpy as np
import torch
from transformers import WeatherNext2ForWeatherForecasting, WeatherNext2FeatureExtractor

model = WeatherNext2ForWeatherForecasting.from_pretrained("kashif/weathernext2").eval()
processor = WeatherNext2FeatureExtractor.from_pretrained("kashif/weathernext2")

# `state` maps each input variable to its values, e.g. from an xarray Dataset of HRES analysis.
# Time-varying variables are [batch, 2, (levels,) lat, lon]; static ones are [lat, lon].
state = {name: ... for name in processor.input_variables}
valid_time = np.array([np.datetime64("2024-10-07T06:00:00").astype("datetime64[s]").astype(np.int64)])

inputs = processor(state, seconds_since_epoch=valid_time)
with torch.no_grad():
    outputs = model(**inputs, generator=torch.Generator().manual_seed(0))

forecast = processor.postprocess(outputs.prediction, state)
print(forecast["2m_temperature"].shape)  # (1, 721, 1440)

Autoregressive rollout

Each 6-hour step draws fresh noise. advance_state drops the oldest frame, appends the forecast, recomputes the clock variables, and discards targets that are not also inputs (precipitation and the cyclone diagnostics).

step_seconds = processor.time_step_hours * 3600
for step in range(20):  # 5 days
    inputs = processor(state, seconds_since_epoch=valid_time)
    with torch.no_grad():
        outputs = model(**inputs)
    forecast = processor.postprocess(outputs.prediction, state)
    valid_time = valid_time + step_seconds
    state = processor.advance_state(state, forecast, valid_time)

Ensembles

There are two independent ensembles here, and the operational product combines both.

1. Noise ensemble (within one checkpoint)

This is the FGN mechanism: each member is one draw of the 32-dimensional noise vector through the same weights. Members ride on the batch axis and stay independent through an autoregressive rollout.

members = 8
inputs = processor(state, seconds_since_epoch=valid_time)
batched = {key: value.repeat(members, *([1] * (value.ndim - 1))) for key, value in inputs.items()}
with torch.no_grad():
    outputs = model(**batched, generator=torch.Generator().manual_seed(0))
# outputs.prediction is (members, channels, lat, lon)

At 0.25° a single member needs roughly 50 GB, so batching all of them at once will usually not fit on one device. Looping over draws gives identical results with a constant memory footprint:

single = processor(state, seconds_since_epoch=valid_time)   # batch of 1
predictions = []
for member in range(members):
    noise = torch.randn(1, model.config.noise_channels, generator=torch.Generator().manual_seed(member))
    with torch.no_grad():
        predictions.append(model(**single, noise=noise).prediction)

Seeding per member (rather than drawing from one stream) means the first N members are reproducible regardless of how many you end up running — the same property the original implementation gets from jax.random.fold_in.

2. Multi-model ensemble (across checkpoints)

The released product is four independently trained networks. Member 1 is at the repository root; all four are also available as subfolders, so you can loop uniformly.

REPO = "kashif/weathernext2"

def load_member(member: int, revision: str = "main"):
    return WeatherNext2ForWeatherForecasting.from_pretrained(
        REPO, subfolder=f"model{member}", revision=revision
    ).eval()

subfolder composes with revision, and works the same way for WeatherNext2FeatureExtractor and AutoConfig — each subfolder carries its own config.json and preprocessor_config.json. The processors are identical across members, so loading one is enough.

Putting them together

The full ensemble is num_models × num_noise_draws trajectories. Loading one member at a time keeps peak memory at roughly one model:

import numpy as np
import torch

processor = WeatherNext2FeatureExtractor.from_pretrained(REPO)
inputs = processor(state, seconds_since_epoch=valid_time)

forecasts = []
for member in range(1, 5):
    model = load_member(member)
    for draw in range(4):
        noise = torch.randn(
            1, model.config.noise_channels,
            generator=torch.Generator().manual_seed(1000 * member + draw),
        )
        with torch.no_grad():
            prediction = model(**inputs, noise=noise).prediction
        forecasts.append(processor.postprocess(prediction, state)["2m_temperature"])
    del model  # free before loading the next member

stack = np.concatenate(forecasts, axis=0)   # (16, lat, lon)
ensemble_mean = stack.mean(axis=0)
ensemble_spread = stack.std(axis=0)

For multi-step forecasts each trajectory carries its own state, so keep one state per member and advance them separately (or keep members on the batch axis, which advance_state handles for you).

Model details

• Architecture: encode–process–decode graph network. The lat/lon grid is encoded, projected onto an icosahedral mesh by a graph network (ball-query connectivity), processed by a 24-layer transformer whose attention is restricted to a 32-hop neighbourhood on the mesh, projected back (in-triangle connectivity), and decoded.
• Hidden size / layers / heads: 768 / 24 / 6, feed-forward 3072.
• Mesh: icosahedron refined 6 times → 40,962 nodes; ~1.6M grid→mesh and ~3.1M mesh→grid edges.
• Positional information: none learned. Position is carried entirely by the mesh geometry and the attention mask, both of which are rebuilt deterministically from the config at load time and cached on disk.
• Inputs: two frames 6h apart, 13 pressure levels, plus static fields and calendar forcings.
• Time step: 6 hours.

Evaluation

For scorecards see the technical report and WeatherBench 2.

Note that this variant takes 100m winds as inputs, which the publicly published sample datasets do not contain, so the port was verified end-to-end using the sibling WeatherNextCyclones checkpoint — identical architecture, same resolution, no 100m winds. Weight conversion for this checkpoint was verified structurally (all 488 parameter arrays mapped, 183.8M parameters, no missing or unexpected keys).

Hardware

A single ensemble member at 0.25° needs roughly 50 GB. The forward pass above took 96 s on CPU; a modern GPU is much faster. The mesh and graph construction takes a few minutes the first time and is then cached under HF_HOME.

Limitations

This is a research model, not an operational warning system. It does not replace official alerts from national meteorological agencies. It was trained on ERA5 and fine-tuned on HRES analysis, and is designed to be initialized from HRES initial conditions rather than reanalysis.

License

The weights are released by Google DeepMind under CC-BY-4.0. The original code is Apache-2.0. Original repository: google-deepmind/weathernext.

Acknowledgements

Data and products of the European Centre for Medium-range Weather Forecasts (ECMWF), as modified by Google. Modified Copernicus Climate Change Service information 2023. 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.

Citation

@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}
}