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. This study proposes the use of generative models to improve the spatial consistency of the high-resolution fields, very demanded by some sectoral applications (e.g., hydrology) to tackle climate change.
@article{arxiv.2305.00974,
title = {On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling},
author = {Jose González-Abad and Jorge Baño-Medina and Ignacio Heredia Cachá},
journal= {arXiv preprint arXiv:2305.00974},
year = {2023}
}
Comments
Accepted at the NeurIPS 2021 Tackling Climate Change with Machine Learning Workshop