  We introduce a conditional Generative Adversarial Network (cGAN) approach to
generate cloud reflectance fields (CRFs) conditioned on large scale
meteorological variables such as sea surface temperature and relative humidity.
We show that our trained model can generate realistic CRFs from the
corresponding meteorological observations, which represents a step towards a
data-driven framework for stochastic cloud parameterization.
