  Here we introduce the artificial intelligence-based cloud distributor (AI-CD)
approach to generate two-dimensional (2D) marine low cloud reflectance fields.
AI-CD uses a conditional generative adversarial net (cGAN) framework to model
distribution of 2-D cloud reflectance in nature as observed by the MODerate
resolution Imaging Spectrometer (MODIS). Specifically, the AI-CD models the
conditional distribution of cloud reflectance fields given a set of large-scale
environmental conditions such as instantaneous sea surface temperature,
estimated inversion strength, surface wind speed, relative humidity and
large-scale subsidence rate together with random noise. We show that AI-CD can
not only generate realistic cloudy scenes but also capture known, physical
dependence of cloud properties on large-scale variables. AI-CD is stochastic in
nature because generated cloud fields are influenced by random noise.
Therefore, given a fixed set of large-scale variables, an ensemble of cloud
reflectance fields can be generated using AI-CD. We suggest that AI-CD approach
can be used as a data driven framework for stochastic cloud parameterization
because it can realistically model sub-grid cloud distributions and their
sensitivity to meteorological variables.
