Integrated Physics-Based Nowcasting Model for Precipitation Radar Image Sequence

This paper proposes an image-based prediction method that can physically predict near-future spatio-temporal image changes, i.e., nowcasting, using fluid-like image sequences from precipitation image sequences. Since motion estimation is one of the most influential factors for very short, i.e., 30 mins or 1 hr, to short term, i.e., 3 hrs or longer, mix of local and global motion estimation methods, i.e., multiple optical flow models, has been proposed to apply. A physically motivated advection equation is used to predict the next time image sequence from the past time image sequence, where previous image degradation issue of losing heavy rainfall regions has been improved by the Cubic Interpolation Profile. Navier-Stokes equation also conducts to provide a time-varying and physical future motion field. A proposed switching prediction model integrates different prediction models in order to spatio-temporally adapt different radar image property during a few hours or longer durations. Using heavy rainfall events, i.e., squall lines and typhoons, it has been proven that the proposed nowcasting method has outperformed the world best nowcasting method, i.e., Rainymotion of open source, in terms of accuracy of precipitation amount.