| # Definition of terms and hyperparameters in configs | |
| Data-driven models are indicated by the `_s2s` suffix (e.g., `unet_s2s`). In addition, within each data-driven model, the checkpoints and hyperparameter specifications are located within the `version_xx/lightning_logs`. The hyperparameters specify the following: | |
| - `lead_time` (default: 1): arbitrary delta_t to finetune the model, for direct approach | |
| - `n_step` (default: 1): number of autoregressive step, s, for autoregressive approach | |
| - `only_headline`: if false, optimize for task 1; if true for task 2 | |
| - `batch_size`: the batch size used for training | |
| - `train_years`: list of years used for training | |
| - `val_years`: list of years used for validation | |
| - `epochs`: number of epoch | |
| - `input_size`: number of input channel | |
| - `learning_rate`: update step at each iteration | |
| - `model_name`: the name of the model used for consistency | |
| - `num_workers`: number of workers used in dataloader | |
| - `output_size`: number of output channel | |
| - `t_max`: number of cosine learning rate scheduler cycle | |
| In addition, in all models, there is a folder named `eval`. This contains individual `.csv` files for each metric (e.g., SpecDiv, RMSE). Within each file, it contains scores for all channels in question (e.g., the entire 60 for task 1, arbitrary n for task 2, or 48 for physics-based models) across 44-day lead time. | |