  Processes related to cloud physics constitute the largest remaining
scientific uncertainty in climate models and projections. This uncertainty
stems from the coarse nature of current climate models and relatedly the lack
of understanding of detailed physics. We train a generative adversarial network
to generate realistic cloud fields conditioned on meterological reanalysis data
for both climate model outputs as well as satellite imagery. While our network
is able to generate realistic cloud fields, especially their large-scale
patterns, more work is needed to refine its accuracy to resolve finer textural
details of cloud masses to improve its predictions.
