Precipitation Nowcasting by a Spectral-Based Nonlinear Stochastic Model

Abstract

A novel rainfall nowcasting method based on the combination of an empirical nonlinear transformation of measured precipitation fields and the stochastic evolution in spectral space of the transformed fields is introduced. The power spectrum and the amplitude distribution of precipitation are kept constant during the forecast, and a Langevin-type model is used to evolve the Fourier phases. The application of the method to a study case is illustrated, and it is shown that, with this procedure, a forecast skill can be obtained that is superior to those provided by Eulerian or Lagrangian persistence for a lead time of up to two hours.