Baseline Models

Baseline Models#

We differentiate between physics-based and data-driven models.

Model Definition#

  • Physics-Based Models (including control/perturbed forecasts):

    • UKMO: UK Meteorological Office

    • NCEP: National Centers for Environmental Prediction

    • CMA: China Meteorological Administration

    • ECMWF: European Centre for Medium-Range Weather Forecasts

  • Data-Driven Models:

    • Lagged-Autoencoder

    • Fourier Neural Operator (FNO)

    • ResNet

    • UNet

    • ViT/ClimaX

    • PanguWeather

    • GraphCast

    • Fourcastnetv2

Model Checkpoints#

Checkpoints for data-driven models are accessible from here.

  • Data-driven models are indicated by the _s2s suffix (e.g., unet_s2s).

  • The hyperparameter specifications are located in version_xx/lightning_logs/hparams.yaml.

NOTE: You will notice that for each data-driven model, there are 4 checkpoints.

  1. Version 0 - Task 1; autoregressive up to 1-day ahead

  2. Version 1 - Task 1; autoregressive up to 5-day ahead

  3. Version 2 - Task 2; autoregressive up to 1-day ahead

  4. Version 3 - Task 2; autoregressive up to 5-day ahead

Only for unet_s2s do we have many more checkpoints. This is to check for the effect of direct vs. autoregressive training approach described in the paper. In particular, the direct models have the following version numbers,

  1. Version {0, 4, 5, 6, 7, 8, 9, 10, 11, 12} - Task 1 (Full optimization)

  2. Version {2, 13, 14, 15, 16, 17, 18, 19, 20, 21} - Task 2 (Sparse optimization)

Each element in the array corresponds to checkpoints optimized for each \(\Delta T \in \{1, 5, 10, 15, 20, 25, 30, 35, 40, 44\}\).

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