Comparison of rainfall microphysics characteristics derived by numerical weather prediction modelling and dual‐frequency precipitation radar

The understanding of large-scale rainfall microphysical characteristics plays a significant role in meteorology, hydrology and natural hazards managements. Traditional instruments for estimating raindrop size distribution (DSD), including disdrometers and ground dual-polarization radars, are available only in limited areas. However, the development of space-based radars and mesoscale numerical weather prediction models would allow for DSD estimation on a large scale. This study investigated the performance of the weather research and forecasting (WRF) model and the global precipitation measurement mission (GPM) dual-frequency precipitation radar for DSD retrieval under different conditions. The DSD parameters (D_m and N_w), rain rate (R), rainfall kinetic energy (KE) and radar reflectivity (Z) were estimated in Chilbolton, United Kingdom, by using long-term disdrometer observations for validation. The rainfall kinetic energy–rain rate (KE–R) and radar reflectivity–rain rate (Z–R) relationships were explored using a disdrometer, the WRF model and GPM. It was found that the DSD parameter distribution trends of the three approaches are similar although the WRF model has larger D_m and smaller N_w values. In terms of the rainfall microphysical relationship, GPM performs better when both Ku- and Ka-band precipitation radars (KuPR and KaPR) observe precipitation simultaneously (R > 0.5 mm h⁻¹), while the WRF model shows high accuracy in light rain (R < 0.5 mm h⁻¹). The fusion of GPM and WRF model is recommended for the improved understanding of rainfall microphysical characteristics in ungauged areas.