NeuralGCM

weights

open(CC-BY-SA-4.0 checkpoints on an anonymously readable Google Cloud bucket)

data

documented-not-released(ERA5 inputs, forcings and pressure levels specified with ARCO-ERA5 named as the source

code

partial(inference API, regridding utilities, and reference_code carrying per-checkpoint training configs and loss code, but its entry point imports google_proprietary_code, a package that is not published, so the training path cannot run)

license

CC-BY-SA-4.0(permissive non-OSI)

Apache-2.0 covers the code, but the trained weights carry CC-BY-SA-4.0, a share-alike, non-OSI license, and that is the license that applies to the released model. The training code is only partly public: the reference code ships per-checkpoint configs, training utilities and loss code, but the experiment script imports an unpublished Google-internal module, so the training path cannot be run.

• https://neuralgcm.readthedocs.io/en/latest/ recorded 2026-09-10

  "Pseudo-code for training NeuralGCM models can be found in the reference_code subdirectory", the documentation "focuses mostly on our API for inference", and the team notes it would like to support training, modifying and fine-tuning but the present codebase makes it trickier than it needs to be.

• https://neuralgcm.readthedocs.io/en/latest/checkpoint_modifications.html recorded 2026-09-10

  Loads checkpoints from gs://neuralgcm/models with an anonymous token, offering v1/deterministic_0_7_deg.pkl, v1/deterministic_1_4_deg.pkl, v1/deterministic_2_8_deg.pkl and v1/stochastic_1_4_deg.pkl.

• https://neuralgcm.readthedocs.io/en/latest/data_preparation.html recorded 2026-09-10

  Specifies the ERA5 pressure levels, input/output variables and sea-surface forcings the models take, and uses the ARCO-ERA5 Zarr copy of ERA5 as the worked example; no training corpus is distributed.

• https://raw.githubusercontent.com/neuralgcm/neuralgcm/main/README.md recorded 2026-09-10

  States the split license: code under Apache License 2.0, trained model weights under Creative Commons Attribution-ShareAlike 4.0 International.

The project's own quickstart installs the package with pip, making PyPI downloads of the neuralgcm package the product's own primary channel, so adoption is measured from PyPI rather than falling back to GitHub stars.

• https://api.github.com/repos/neuralgcm/neuralgcm recorded 2026-09-10

  stargazers_count: 1014; archived: false; fork: false — recorded so the superseded stars signal stays visible.

• https://pypistats.org/api/packages/neuralgcm/recent recorded 2026-09-10

  last_month = 8,480 downloads of the neuralgcm package.

Two attributes. Adaptation is explicitly uncounted because the documentation says training and fine-tuning are not supported and ship only as pseudo-code; the checkpoint-modification recipe changes outputs, not learned parameters.

• https://neuralgcm.readthedocs.io/en/latest/ recorded 2026-09-10

  Describes NeuralGCM as a hybrid ML and physics atmospheric model for weather and climate simulation built on the Dinosaur dynamical core, and says the maintainers "would love to support training, modifying and fine-tuning NeuralGCM models" but that the Haiku and Gin codebase makes it "much trickier than it needs to be", with pseudo-code for training in reference_code.

• https://neuralgcm.readthedocs.io/en/latest/checkpoint_modifications.html recorded 2026-09-10

  Distributes deterministic checkpoints at 0.7, 1.4 and 2.8 degrees plus a stochastic 1.4-degree model, and discusses long integrations and the stability of the simulated global mean surface pressure.