This repo contains the inference code accompanying "ACE: A fast, skillful learned global atmospheric model for climate prediction" (arxiv:2310.02074).
Clone this repository. Then assuming conda is available, run
make create_environment
to create a conda environment called fme with dependencies and source
code installed. Alternatively, a Docker image can be built with make build_docker_image.
You may verify installation by running pytest fme/.
The checkpoint and a 1-year subsample of the validation data are available at this Zenodo repository. Download these to your local filesystem.
Alternatively, if interested in the complete dataset, this is available via a public requester pays Google Cloud Storage bucket. For example, the 10-year validation data (approx. 190GB) can be downloaded with:
gsutil -m -u YOUR_GCP_PROJECT cp -r gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-netCDFs/validation .
It is possible to download a portion of the dataset only, but it is necessary to have
enough data to span the desired prediction period. The checkpoint is also available on GCS at
gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/checkpoints/ace_ckpt.tar.
Update the paths in the example config. Then in the
fme conda environment, run inference with:
python -m fme.ace.inference fme/docs/inference-config.yaml
Two versions of the dataset described in arxiv:2310.02074 are available:
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.