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https://github.com/ai2cm/ace
Ai2 Climate Emulator
https://github.com/ai2cm/ace
Last synced: 3 months ago
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Ai2 Climate Emulator
- Host: GitHub
- URL: https://github.com/ai2cm/ace
- Owner: ai2cm
- License: apache-2.0
- Created: 2023-11-29T23:08:42.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-08-01T21:39:55.000Z (3 months ago)
- Last Synced: 2024-08-10T01:37:53.081Z (3 months ago)
- Language: Python
- Homepage:
- Size: 3.52 MB
- Stars: 29
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - ACE - A 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. (Atmosphere / Atmospheric Composition and Dynamics)
README
[![Docs](https://readthedocs.org/projects/ai2-climate-emulator/badge/?version=latest)](https://ai2-climate-emulator.readthedocs.io/en/latest/)
# ACE: Ai2 Climate Emulator
This repo contains code accompanying "ACE: A fast, skillful learned global atmospheric model for climate prediction" ([arxiv:2310.02074](https://arxiv.org/abs/2310.02074)).## Documentation
See complete documentation [here](https://ai2-climate-emulator.readthedocs.io/en/latest/).
## Quickstart
A quickstart guide may be found [here](https://ai2-climate-emulator.readthedocs.io/en/latest/quickstart.html).
## Model checkpoint
The trained ACE checkpoint and a 1-year subsample of the validation dataset is available in
[this Zenodo repository](https://doi.org/10.5281/zenodo.10791087).## Available datasets
Two versions of the complete dataset described in [arxiv:2310.02074](https://arxiv.org/abs/2310.02074)
are available on a requester pays Google Cloud Storage bucket:
```
gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-zarrs
gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-netCDFs
```
The `zarr` format is convenient for ad-hoc analysis. The netCDF version contains our
train/validation split which was used for training and inference.