  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 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 problem of increasing the accuracy and resolution of comparatively
low-resolution input from a weather forecasting model, using high-resolution
radar measurements as a "ground truth". The neural network must learn to add
resolution and structure whilst accounting for non-negligible forecast error.
We show that GANs and VAE-GANs can match the statistical properties of
state-of-the-art pointwise post-processing methods whilst creating
high-resolution, spatially coherent precipitation maps. Our model compares
favourably to the best existing downscaling methods in both pixel-wise and
pooled CRPS scores, power spectrum information and rank histograms (used to
assess calibration). We test our models and show that they perform in a range
of scenarios, including heavy rainfall.
