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

weather forecast

https://user-images.githubusercontent.com/19285200/132585484-8202b624-e487-440f-8ed6-7ed182a1f31b.mp4

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.

Clone this repository.

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

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 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

Clone the required repository:

git clone git@github.com:ghiggi/xverif.git
git clone git@github.com:ghiggi/xscaler.git
git clone git@github.com:ghiggi/xsphere.git
git clone git@github.com:ghiggi/xforecasting.git

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

and clone the required repository:

git clone git@github.com:ghiggi/xverif.git
git clone git@github.com:ghiggi/xscaler.git
git clone git@github.com:ghiggi/xsphere.git
git clone git@github.com:ghiggi/xforecasting.git

Add the PYTHONPATH (i.e. in the .bashrc) for the following packages. Here is an example:

export PYTHONPATH="${PYTHONPATH}:/home/ghiggi/Python_Packages/xscaler"
export PYTHONPATH="${PYTHONPATH}:/home/ghiggi/Python_Packages/xverif"
export PYTHONPATH="${PYTHONPATH}:/home/ghiggi/Python_Packages/xsphere"
export PYTHONPATH="${PYTHONPATH}:/home/ghiggi/Python_Packages/xforecasting"  
export PYTHONPATH="${PYTHONPATH}:/home/ghiggi/Projects/deepsphere-weather"

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.

Data structure

Here we are going to document the data structure

The data required for model training and evaluation are stored with the following folder hierarchy.

preprocessed
└───ERA5_HRES
|   └─── <sampling>
|         └─── Data
|         └─── Scalers
|         └─── Benchmarks 
|         └─── Climatology 
| 
└───IF5_HRES
|    └─── <sampling>
|         └─── Data
|    
└───IF5_ENS
|   └─── <sampling>
         └─── Data

On the LTE servers, data are available in the directory /ltenas3/data/DeepSphere

The data are available upon request to the authors.

Reproducing our results

Contributors

Gionata Ghiggi slides video
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.

deepsphere / deepsphere-weather

readme