A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. (arXiv:2204.02028v2 [physics.ao-ph] UPDATED)

Despite continuous improvements, precipitation forecasts are still not as
accurate and reliable as those of other meteorological variables. A major
contributing factor to this is that several key processes affecting
precipitation distribution and intensity occur below the resolved scale of
global weather models. Generative adversarial networks (GANs) have been
demonstrated by the computer vision community to be successful at
super-resolution problems, i.e., learning to add fine-scale structure to coarse
images. Leinonen et al. (2020) previously applied a GAN to produce …

arxiv deep learning learning physics precipitation stochastic