DeepSphere-Weather - Deep Learning on the sphere for weather/climate applications.

The code in this repository provides a scalable and flexible framework to apply convolutions on spherical unstructured grids for weather/climate applications.

ATTENTION: The code is subject to changes in the coming weeks / months.

The folder experiments (will) provide examples for:

• Weather forecasting using autoregressive CNN models
• Weather field downscaling (aka superesolution) [in preparation].
• Classication of atmospheric features (i.e. tropical cyclone and atmospheric rivers) [in preparation].

The folder tutorials (will) provide jupyter notebooks describing various features of DeepSphere-Weather.

The folder docs (will) contains slides and notebooks explaining the DeepSphere-Weather concept.

Installation

For a local installation, follow the below instructions.

1. Clone this repository.

   git clone https://github.com/deepsphere/deepsphere-weather.git
   cd deepSphere-weather

2. Install the dependencies.

   conda env create -f environment.yml
   pip install git+https://github.com/epfl-lts2/[email protected]

3. If you don't have a GPU and you plan to work on CPU, please install the follow:

   conda install pytorch torchvision torchaudio cpuonly -c pytorch
   pip install torch-scatter torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html

Tutorials

• spherical_grids.ipynb: get a taste of samplings/pixelizations of the sphere.
• interpolation_pooling.ipynb: get to understand our generalized pooling based on interpolation between samplings.

Reproducing our results

Contributors

• Gionata Ghiggi
• Michaël Defferrard
• Wentao Feng [code, slides]
• Yann Yasser Haddad [code, slides]
• Natalie Bolón Brun [code]
• Icíar Lloréns Jover [code, report & slides]

License

The content of this repository is released under the terms of the MIT license.