Retrieving raindrop size distributions (DSDs) from polarimetric radar data can provide a good data support for the study of microphysical rain processes. Limitations in the DSD model lead to limitations of retrieving DSDs. For example, when calculating the μ-Λ relation in the constrained Gamma (C-G) model DSD retrieval method, some data are excluded, and thus the μ-Λ relation cannot represent the characteristics of all precipitation. To solve this problem, a new double-moment normalization method for retrieving DSDs from polarimetric radar data is proposed. On the basis of retaining all valid data, near-linear quantitative relationships are established. DSD moment six and seven are calculated by using Z_H and Z_DR, and then DSD is retrieved based on the double-moment normalization DSD model. Using data of two rainfall events occurred in Guangdong Province of China, the scientific feasibility of the method is verified by the simulation test, ground observations, and retrieval DSD results at different heights. The results reveal that the hypothetical DSD model is in good agreement with the actual observed data, and the method is reasonable and feasible in theory. The new method shows advantages over the traditional C-G DSD model method in both the simulation test and the ground verification, especially for heavy rain. At the same time, the particle size retrievals for the weak echo (＜35 dBz) in the high air derived from dual polarization radar (double-moment normalization method) and cloud radar are consistent. This method shows retrieval reliability based on both theory and ground and high-altitude DSD verification, and provides a good DSD retrieval way to study the DSD characteristics at different heights.

In this study, a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal–spatial variability of raindrop size distributions (DSDs) in the Zhengzhou extreme rainfall event on 20 July 2021. The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement, despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm. The Parsivel OTT observations show prominent temporal–spatial variations of DSDs, and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500–1600 LST (local standard time) was reported. This hourly rainfall is characterized by fairly high concentrations of large raindrops, and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h⁻¹. Besides, polarimetric radar observations show the highest differential phase shift (K_dp) and differential reflectivity (Z_dr) near surface over Zhengzhou Station from 1500 to 1600 LST. In light of the remarkable temporal–spatial variability of DSDs, a reflectivity-grouped fitting approach is proposed to optimize the reflectivity–rain rate (Z–R) parameterization for radar quantitative precipitation estimation (QPE), and the rain gauge measurements are used for validation. The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h⁻¹, as compared with the fixed Z–R parameterization. This study reveals the drastic temporal–spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z–R relationships for radar QPE of such events.