Simulation of operational typhoon rainfall nowcasting using radar reflectivity combined with meteorological data

Abstract

In this study, a practical typhoon effective rainfall nowcasting (TERN) model was developed for use in real-time forecasting. The TERN model was derived from a data-driven adaptive network-based fuzzy inference system (ANFIS). The model inputs include meteorological data and radar reflectivity data. The model simulation process begins with an online typhoon warning issued by the Central Weather Bureau (CWB) of Taiwan. It is then determined whether the typhoon approaches the study area according to the typhoon track predicted by the CWB. When a typhoon hits Taiwan, various data are received from sensor instruments, including the ground precipitation data, typhoon climatological data, and radar reflectivity factor by using Weather Surveillance Radar, 1988, Doppler (WSR-88D) products. The study site was Shihmen Catchment. A maximum of 10 typhoon events from 2000 to 2010 were collected. Regarding the model construction, the input combinations of the ground precipitations and reflectivity factors over the catchment functioned as optimal input variables. To verify the practicability of the ANFIS-based TERN model, Typhoon Krosa, which hit Taiwan in 2007, was simulated. The results demonstrated that the proposed methodology of real-time rainfall forecasts during typhoon warning periods yielded favorable performance levels, reliably predicting results regarding 1 h to 6 h forecasting horizons.