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The simulation of atmospheric flows by means of traditional discretizationmethods remains computationally intensive, hindering the achievement of highforecasting accuracy in short time frames. In this paper, we apply threereduced order models that have successfully reduced the computational time fordifferent applications in computational fluid dynamics while preservingaccuracy: Dynamic Mode Decomposition (DMD), Hankel Dynamic Mode Decomposition(HDMD), and Proper Orthogonal Decomposition with Interpolation (PODI). Thethree methods are compared in terms of computational time and accuracy in thesimulation of two well-known benchmarks for mesoscale flow. The accuracy of theDMD and HDMD solutions deteriorates rather quickly as the forecast time windowexpands, although these methods are designed to predict the dynamics of asystem. The reason is likely the strong nonlinearity in the benchmark flows.The PODI solution is accurate for the entire duration of the time interval ofinterest thanks to the use of interpolation with radial basis functions. Thisholds true also when the model features a physical parameter expected to varyin a given range, as is typically the case in weather prediction.

A Comparison of Data-Driven Reduced Order Models for the Simulation of Mesoscale Atmospheric Flow