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.