ClimateLearn

Paper | Code

Summary

Aims at aiding climate forecasting, downscaling and projections.

It is a pytorch integrated dataset, composed mainly of CMIP6, ERA5 and PRISM data.

• Forecasting: close to medium range weather and climate prediction

• Downscaling: Due to large grid sizes, large cells are often used for reducing size of data. However, this leads to loss of information, and to a lower resolution predictions. Downscaling aims at correcting bias amd map results to higher resolution.

  \( C \times W \times H \leftarrow C' \times W \times H \) where \( H < H' \) and \( W < W' \)

• Projections: Obtaining long range predictions under different conditions (ex. greenhouse gasses emission or atmosphere composition).

The library also includes several baselines, pipelines for end-to-end training and evaluation, and a set of metrics.