Probabilistic Precipitation Nowcast Using Dual-Polarization Radar Measurements

Heavy precipitation may lead to severe natural disasters such as flood and debris flows, which often cause substantial socioeconomic losses. Compared to traditional single-polarization radar, the dual-polarization radar has already proven to have better performance for precipitation identification and quantification through the polarimetric measurements including differential reflectivity Z_DR, differential phase Ψ_DP, and the copular correlation coefficient . This paper develops an adaptive polarimetric radar rainfall nowcast system, which has three main modules: 1) hydrometeor identification; 2) dual-polarization based rainfall estimation; and 3) probabilistic rainfall nowcast. The polarimetric measurements from two S-band radars in Southern China during several extreme precipitation events in 2017 are utilized to demonstrate the designed rainfall nowcast system. In particular, the performance of polarimetric radar rainfall estimates is quantified using a local rain gauge network. The probabilistic nowcasting models based on the Lagrangian persistence of radar rainfall field are detailed. The nowcast performance is assessed as a function of lead time. The results show that the proposed algorithm is very reliable for nowcasting up to 60 minutes, and it can still produce reasonable nowcast up to 180 minutes. In addition, the ensemble spread corresponds well to the observed uncertainties in the nowcasts field, and the spatial and temporal structures of the stochastic ensemble members remain realistic at all forecast lead times.