A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities

Abstract

A good short-period forecast of heavy rainfall is essential for many meteorological and hydrological applications. Traditional deterministic and stochastic nowcasting methodologies have been inadequate in their characterization of pixelwise rainfall reflectivity propagation, intensity, and uncertainty. The methodology presented herein uses an approach that efficiently parameterizes spatio-temporal dynamic models in terms of integro-difference equations within a hierarchical framework. The approach accounts for the uncertainty in the prediction and provides relevant distributional information concerning the nowcast. An application is presented that shows the effectiveness of the technique and its potential for nowcasting weather radar reflectivities.