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 manually the following dependencies:

   • Install first pytorch and its extensions on GPU:

     conda install -c conda-forge pytorch-gpu  

   • If you don't have GPU available install it on CPU:

     conda install -c conda-forge pytorch-cpu  

   • Install the other required packages:

   conda create --name weather python=3.8
   conda install xarray dask cdo h5py h5netcdf netcdf4 zarr numcodecs rechunker xskillscore
   conda install notebook jupyterlab
   conda install matplotlib-base cartopy pycairo seaborn cycler
   conda install numpy pandas numba scipy bottleneck
   conda install yaml tabulate tqdm deepdiff
   conda install healpy igl shapely      
   pip install git+https://github.com/epfl-lts2/pygsp@sphere-graphs
   pip install torchinfo

3. Alternatively install the dependencies using one of the appropriate below environment.yml files:

   conda env create -f environment_python3.8.5.yml
   conda env create -f environment_python3.9.yml

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.