| license: cc-by-nc-sa-4.0 | |
| metrics: | |
| - mse | |
| pipeline_tag: graph-ml | |
| tags: | |
| - graphcast | |
| - weather | |
| # graphcast\_finetune\_2019\_2021 | |
| This model contains the GraphCast checkpoints created as part of [(Subich 2024)](https://arxiv.org/abs/2408.14587), which fine-tunes the "standard" GraphCast ¼°/37-level model on the 2019-2021 period. The primary goal of the study was to adapt the model to the Canadian GDPS analysis, but another product produced along the way was a "control" version trained on ERA5 data, which is more widely available. | |
| The model's training code is available at [https://github.com/csubich/graphcast](https://github.com/csubich/graphcast/tree/graphcast_train). | |
| The model checkpoints are in the `params/ar{1,2,4,8,12}` directories, each directory noting the number of autoregressive forecast steps completed. See the arxiv paper for details about the training schedule. The respective `era5.ckpt` files are the model versions trained on ERA5 data, and the `gdps.ckpt` files are those trained on the GDPS analysis data. The `ar12` checkpoints are the final result of training, and the earlier ones are provided for research \& reference. | |
| The GDPS-tuned model was trained with an adjusted set of normalization weights, which are located in `stats/gdps`. For symmetry, the corresponding ERA5 weights are at `stats/era5`, but those are unmodified from the normalization weights used for the unmodified GraphCast models. | |
| Also as noted in (Subich 2024), the models were trained with an alternate set of vertical (pressure level) weights for the loss function, which are included here in the various `error_weights/*.pickle` files. `deepmind.pickle` just reproduces pressure-proportional weighting, and it is included for completeness. | |
| As these models are all derivative of the published 37-level GraphCast weights, these models also carry the CC-BY-NC-SA-4.0 (attribution, noncommercial, sharealike) license. | |
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| license: cc-by-nc-sa-4.0 | |
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