Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization

Code for the paper "Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization".

We provide here code to run forecast experiments on:

• y coordinate of 3-dimensional Lorenz63 model.

• 8-dimensional x variables for the Lorenz96 model integrated using the parametrized model.

• WeatherBench; we use the Z500 variable at the coarsest resolution (32x64). See here for Download instructions

  Additionally, the file additional_results.pdf contains additional figures for the Weatherbench dataset.

Scripts

We have 5 Python scripts:

• generate_data.py needs to be run to generate datasets for Lorenz63 and Lorenz96
• train_nn.py trains the generative networks with the different methods
• predict_test_plot.py computes performance metrics and creates plots
• predict_test_plot_comparison.py creates comparison plots between three selected methods, for Lorenz63 and Lorenz96
• plot_weatherbench.py creates the plots for WeatherBench data

Additionally, we provide 3 bash scripts which show how to run experiments on the three models.

• run_lorenz63.sh runs some experiments on the Lorenz63 model and allows to reproduce Figure 2a in the paper
• run_lorenz96.sh runs some experiments on the Lorenz96 model and allows to reproduce Figure 2b in the paper
• run_WeatherBench.sh runs some experiments on the WeatherBench model. To use this, the data needs to be downloaded as mentioned above. Also, these experiments require a GPU to run.

Dependencies

Pip

The dependencies can be installed with pip install -r requirements.txt. However that is not enough to use the plotting features for WeatherBench (using the library cartopy). for that need to use Conda, see below.

Conda

conda create --name <env-name>
conda install --file requirements_conda.txt

but also need to install two other packages from pip:

pip install torchtyping typeguard einops

If you want to use GPU, Pytorch has to be installed with the following, instead of the above
conda install pytorch cudatoolkit=10.2 -c pytorch

Paper citation

If you find this code useful, please cite the following paper:

@article{Pacchiardi2024Probabilistic,
  author  = {Lorenzo Pacchiardi and Rilwan A. Adewoyin and Peter Dueben and Ritabrata Dutta},
  title   = {Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization},
  journal = {Journal of Machine Learning Research},
  year    = {2024},
  volume  = {25},
  number  = {45},
  pages   = {1--64},
  url     = {http://jmlr.org/papers/v25/23-0038.html}
}