Improving Nowcasting of Convective Development by Incorporating Polarimetric Radar Variables Into a Deep-Learning Model

Nowcasting of convective storms is urgently needed yet rather challenging. Current nowcasting methods are mostly based on radar echo extrapolation, which suffer from the insufficiency of input information and ineffectiveness of model architecture. A novel deep-learning (DL) model, FURENet, is designed for extracting information from multiple input variables to make predictions. Polarimetric radar variables, K_DP and Z_DR, which provide extra microphysics and dynamic structure information of storms, are fed into the model to improve nowcasting. Two representative cases indicate that K_DP and Z_DR can help the DL model better forecast convective organization and initiation. Quantitative statistical evaluation shows using FURENet, K_DP, and Z_DR synergistically improve nowcasting skills (CSI score) by 13.2% and 17.4% for the lead time of 30 and 60 min, respectively. Further evaluation shows the microphysical information provided by the polarimetric variables can enhance the DL model in understanding the evolution of convective storms and making more trustable nowcasts.