This repository contains the code for the training a cycle consistent generative adversarial network on Earth system model output data for bias correction.
The dependencies are installed in a Singularity container that can be pulled from
singularity pull --arch amd64 library://phess/pytorch-stack/stack.sif:v3
- The W5E5 reanalysis data can be downloaded at Link.
- The CMIP6 data can be downloaded at WCRP Coupled Model Intercomparison Project (Phase 6).
- Define the parameters and file paths in src/configuration.py
- run:
singularity run --nv --bind /path/to/current/directory /path/to/container/stack_v3.sif python main.py
To evaluate the results define parameters and paths in src/configuration.py and use the Jupyther notebooks:
- Evaluation of the GAN model checkpoints:
notebooks/summary-statistics.ipynb - Comparison of the GAN model and baselines:
notebooks/analysis-combined-results.ipynb - Evaluation of spectral densities:
notebooks/analysis-spectral-density.ipynb - Evaluation of fractals:
notebooks/analysis-fractal-dimension.ipynb
To start Jupyter Lab run:
singularity run --nv --bind /path/to/current/directory /path/to/container/stack_v3.sif jupyter-lab