A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts.

ABSTRACT: 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, that is, learning to add fine-scale structure to coarse images. Leinonen et al. (2020, https://doi.org/10.1109/TGRS.2020.3032790) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging pro

SUBMITTER: Harris L 

PROVIDER: S-EPMC9788314 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature