A spherical CNN for weather forecasting

Overview

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

weather forecast

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

Reproducing our results

Contributors

License

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

Owner
DeepSphere
Learning on the sphere (with a graph-based ConvNet). Used so far for cosmology, geophysics, 3D object recognition.
DeepSphere
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