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With the accumulation of meteorological big data, data-driven models forshort-term precipitation forecasting have shown increasing promise. We focus onKoopman operator analysis, which is a data-driven scheme to discover governinglaws in observed data. We propose a method to apply this scheme to phenomenaaccompanying advection currents such as precipitation. The proposed methoddecomposes time evolutions of the phenomena between advection currents under avelocity field and changes in physical quantities under Lagrangian coordinates.The advection currents are estimated by kinematic analysis, and the changes inphysical quantities are estimated by Koopman operator analysis. The proposedmethod is applied to actual precipitation distribution data, and the resultsshow that the development and decay of precipitation are properly capturedrelative to conventional methods and that stable predictions over long periodsare possible.

Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-term Precipitation Forecasting