Precipitation Nowcasting Using Deep Learning on Radar Data Augmented with Satellite Data

Wikom Tosiri, Nutnaree Kleawsirikul, Patamawadee Leepaisomboon, Natnapat Gaviphatt, Hidetomo Sakaino, Peerapon Vateekul

Abstract: Precipitation nowcasting with ground-based weather radars and satellite-based precipitation data based on deep learning method will open a new avenue of weather prediction. However, it is limited to regions where ground-based weather radars can operate for nowcasting. We propose an improved deep learning precipitation prediction by integrating the precipitation data from Japan Aerospace Exploration Agency (JAXA)’s Global Rainfall Watch (GSMAP) with the precipitation data from WEATHERNEWS Co., Ltd., which provides precipitation data with Type C Doppler radars that detect precipitation in the atmosphere. It has been demonstrated that our proposed method can improve precipitation data coverage areas and the efficiency of precipitation nowcasting by the proposed deep learning technique in many extreme weather cases, i.e., typhoons.