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README.md
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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.
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The model's training code is available at [https://github.com/csubich/graphcast]
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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.
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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.
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The model's training code is available at [https://github.com/csubich/graphcast](https://github.com/csubich/graphcast/tree/graphcast_train).
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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.
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