  With the accumulation of meteorological big data, data-driven models for
short-term precipitation forecasting have shown increasing promise. We focus on
Koopman operator analysis, which is a data-driven scheme to discover governing
laws in observed data. We propose a method to apply this scheme to phenomena
accompanying advection currents such as precipitation. The proposed method
decomposes time evolutions of the phenomena between advection currents under a
velocity field and changes in physical quantities under Lagrangian coordinates.
The advection currents are estimated by kinematic analysis, and the changes in
physical quantities are estimated by Koopman operator analysis. The proposed
method is applied to actual precipitation distribution data, and the results
show that the development and decay of precipitation are properly captured
relative to conventional methods and that stable predictions over long periods
are possible.
