Update README.md

README.md

@@ -1,29 +1,33 @@
----
-license: cc-by-nc-sa-4.0
-metrics:
-- mse
-pipeline_tag: graph-ml
-tags:
-- graphcast
-- weather
----
-
-# graphcast_amse
-
-This repository contains the model checkpoints trained as part of [Subich et al 2025](https://arxiv.org/abs/2501.19374), which introduces an adjusted mean squared error (AMSE) loss function to elimiante the "double penalty" problem in the training of weather models.  This loss function decomposes model error in spherical harmonic space and contains separate terms for amplitude and correlation errors, by total wavenumber.
-
-These models are based on the `graphcast-operational` model trained by Google DeepMind, a ¼°, 13-level version of GraphCast trained on the ERA5 dataset and fine-tuned on the HERS initial conditions dataset (both available from [WeatherBench 2](https://weatherbench2.readthedocs.io/en/latest/data-guide.html)).  The checkpoints here were fine-tuned on the HRES initial conditions dataset, with a batch size of 8 and the following training curriculum (cosine schedule, warmup 512 samples or 64 batches):
-
-Length | Batches | Peak LR | End LR
-:-:|:-:|:-:|:-:
-1 step (6h) | 25,000 | 2.5e-5 | 1.25e-7
-2 steps (12h) | 2,500 | 2.5e-6 | 7.5e-8
-4 steps (24h) | 2,500 | 2.5e-6 | 7.5e-8
-8 steps (48h) | 1,250 | 2.5e-6 | 7.5e-8
-12 steps (72h) | 1,250 | 2.5e-6 | 7.5e-8
-
-The model checkpoints are in the `params/ar{1,12}` directories, the former containing the checkpoints after the end of the first training stage and the latter containing the final checkpoints.  The models trained are:
-
-* amse.ckpt -- "the" model trained with the AMSE loss function
-* mse.ckpt -- A control model trained with the ordinary MSE loss function
-* mae.ckpt -- An ablative study, trained with the mean absolute error loss function
+---
+license: cc-by-nc-sa-4.0
+metrics:
+- mse
+pipeline_tag: graph-ml
+tags:
+- graphcast
+- weather
+---
+
+# graphcast_amse
+
+This repository contains the model checkpoints trained as part of [Subich et al 2025](https://arxiv.org/abs/2501.19374), which introduces an adjusted mean squared error (AMSE) loss function to elimiante the "double penalty" problem in the training of weather models.  This loss function decomposes model error in spherical harmonic space and contains separate terms for amplitude and correlation errors, by total wavenumber.
+
+These models are based on the `graphcast-operational` model trained by Google DeepMind, a ¼°, 13-level version of GraphCast trained on the ERA5 dataset and fine-tuned on the HERS initial conditions dataset (both available from [WeatherBench 2](https://weatherbench2.readthedocs.io/en/latest/data-guide.html)).  The checkpoints here were fine-tuned on the HRES initial conditions dataset, with a batch size of 8 and the following training curriculum (cosine schedule, warmup 512 samples or 64 batches):
+
+Length | Batches | Peak LR | End LR
+:-:|:-:|:-:|:-:
+1 step (6h) | 25,000 | 2.5e-5 | 1.25e-7
+2 steps (12h) | 2,500 | 2.5e-6 | 7.5e-8
+4 steps (24h) | 2,500 | 2.5e-6 | 7.5e-8
+8 steps (48h) | 1,250 | 2.5e-6 | 7.5e-8
+12 steps (72h) | 1,250 | 2.5e-6 | 7.5e-8
+
+The model checkpoints are in the `params/ar{1,12}` directories, the former containing the checkpoints after the end of the first training stage and the latter containing the final checkpoints.  The models trained are:
+
+* amse.ckpt -- "the" model trained with the AMSE loss function
+* mse.ckpt -- A control model trained with the ordinary MSE loss function
+* mae.ckpt -- An ablative study, trained with the mean absolute error loss function
+
+The model training code is available at [github](https://github.com/csubich/graphcast/tree/amse).
+
+Because these models are based on the `graphcast-operational` checkpoint, the checkpoints retain the CC-BY-ND-SA 4.0 license.