Many small-scale and complex physical processes in general 
circulation models (GCMs) cannot be explicitly resolved due to 
limited computational resources. Processes on scales smaller 
than the spatial resolution of the model need to be 
parameterized. Parameterizations have been known to be major 
sources of uncertainties in GCMs, and various approaches have 
been proposed to deduce the influence of the under-resolved 
and unresolved processes.

Generative adversarial networks (GANs) are a class of 
unsupervised machine learning methods that can generate 
realistic data from a target distribution. They are well-
suited to build emulators for complex physical processes, and 
hence poised to serve as building blocks for 
parameterizations. Super-resolution GAN (SRGAN) and its 
variants were introduced in recent years for obtaining photo-
realistic images using a novel loss function, which is a 
weighted sum of adversarial loss and pixel-to-pixel content 
loss.
We develop a data-driven approach using SRGAN and its variants 
drawing parallels from the development of super-parameterized 
CAM (SP-CAM). For simplicity and model consistency, the GANs 
are trained using cloud resolving model (CRM) outputs from the 
near-global CRM simulations ( 
https://doi.org/10.1002/2015MS000499 ), with the input 
distribution being a low-resolution coarse-grained version of 
the original high-resolution CRM data. The GAN aims to 
reconstruct the original high-resolution CRM data. We test the 
performance of these GANs using several reconstruction losses, 
including some motivated by physical constraints of importance 
to the domain of cloud physics. Our results show that these 
GANs are able to produce realistic high-resolution data from 
their low-resolution counterparts, whilst satisfying some of 
the physical constraints. Our next step is to incorporate 
physical constraints more rigorously into the training and 
inference of these GANs, so they may be used for constructing 
realistic subgrid scale parameterizations for convection.