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Simulation of turbulent flows, especially at the edges of clouds in theatmosphere, is an inherently challenging task. Hitherto, the best possiblecomputational method to perform such experiments is the Direct NumericalSimulation (DNS). DNS involves solving non-linear partial differentialequations for fluid flows, also known as Navier-Stokes equations, ondiscretized grid boxes in a three-dimensional space. It is a valuable paradigmthat has guided the numerical weather prediction models to compute rainfallformation. However, DNS cannot be performed for large domains of practicalutility to the weather forecast community. Here, we introduce DeepClouds.ai, a3D-UNET that simulates the outputs of a rising cloud DNS experiment. Theproblem of increasing the domain size in DNS is addressed by mapping an inner3D cube to the complete 3D cube from the output of the DNS discretized gridsimulation. Our approach effectively captures turbulent flow dynamics withouthaving to solve the complex dynamical core. The baseline shows that the deeplearning-based simulation is comparable to the partial-differentialequation-based model as measured by various score metrics. This framework canbe used to further the science of turbulence and cloud flows by enablingsimulations over large physical domains in the atmosphere. It would lead tocascading societal benefits by improved weather predictions via advancedparameterization schemes.

DeepClouds.ai: Deep learning enabled computationally cheap direct numerical simulations