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