Use-case of Deep Generative Models for Perfect Prognosis Climate Downscaling

Use-case of Deep Generative Models for Perfect Prognosis Climate Downscaling.Language: Python and R.Installation: A Dockerfile is available with all the libraries needed to run the experiment.Instructions: The notebook preprocessData.ipynb is available with the code and instructions to download and preprocess the data. To download the data an account in UDG-TAP may be required. By running the runModel.ipynb notebook, the CVAE model can be trained. A pre-trained model is also available for the user to directly generate conditioned samples.Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising results to reproduce the observed local variability, they are based on the estimation of independent distributions at each location, which leads to deficient spatial structures, especially when downscaling precipitation. We propose the use of generative models to improve the spatial consistency of the highresolution fields, very demanded by some sectoral applications (e.g., hydrology) to tackle climate change. To illustrate these points we develop a simple use-case of Perfect Prognosis Downscaling over Europe using a Generative Model, more specifically a Conditional Variational Autoencoder (CVAE).Peer reviewe