Spatiotemporal Precipitation Nowcasting for Different Rain Types Using C-Band Dual-Polarimetric Radar Observations in Indian Core Monsoon Zone

Weather radars are vital for precipitation nowcasting, offering high-resolution inputs. This study assesses radar-based rainfall nowcasting over the core monsoon zone (CMZ) using a dual-polarization C-band radar at Silkheda. Utilizing the Python framework of short-term ensemble prediction system (PySTEPS), we apply three optical flow algorithms-Lucas Kanade (LK), dynamic and adaptive radar tracking of storms (DARTS), and variational echo tracking (VET)-to assess nowcasting accuracy under five different rain categories based on daily rain accumulation: light, moderate, heavy, very heavy, and extremely heavy, as defined by the India Meteorological Department (IMD). Probabilistic nowcasts were generated using a set of 24 ensemble members. The relative operating characteristic (ROC) curve and fraction skill score (FSS) are used as verification methods. The ROC reveals almost similar values for all three optical methods, with DARTS outperforming for extremely heavy rainfall with a lead time of up to +3 hr. It is observed that the FSS, representing the nowcasting skill, reduces with increasing intensity and finer spatial resolution. In general, even for a spatial scale of 1 km and rain rate threshold of 0.1 mm/h, the nowcasting lead time for light and moderate rain days is up to 1 hr, while for other rain categories, it is up to 2-2.5 hr.