The S-band dual-polarization (large) radar, owing to its longer wavelength and lower susceptibility to attenuation, serves as the primary tool for quantitative estimation of large-scale precipitation. However, factors such as scanning methods, the Earth's curvature, and terrain blockage cause the beam center to be too high or even blocked during long-range detection, leading to a significant underestimation of precipitation in these areas. The deployment of X-band dual-polarization (small) radars offers a potential solution to this issue. Leveraging the advantage of X-band radar in low-altitude detection, this paper proposes a method to improve the data quality of S-band dual-polarization radar, aiming to address the underestimation problem in long-range precipitation estimation by large radars. Experiments were conducted based on six precipitation events observed by 9 X-band dual-polarization phased array radars in Guangdong and the Heyuan S-band dual-polarization radar. The scientific validity of the method was verified. The results show that in long-range detection areas, the algorithm can significantly mitigate the underestimation by large radar and achieve better precipitation estimation performance than either single-band (S or X) radar, reducing errors by at least 14.27%. For light to moderate rain, the primary improvement generated by the algorithm is a reduction in the mean error. For heavy to torrential rain, the algorithm significantly alleviates the severe underestimation by single-band radars, reducing estimation errors of heavy rain (hourly rainfall 30—50 mm) and torrential rain (hourly rainfall >50 mm) by at least 44.47% and 42.95%, respectively. The algorithm can accurately capture the rapid intensification signals of precipitation during heavy rainfall events, yielding results closely matching minute-level precipitation data observed by ground automatic stations. Compared to single-band radar, the algorithm demonstrates significant improvements in precipitation estimation, particularly in areas with dense X-band radar coverage. This method provides a valuable algorithmic reference for operational precipitation estimation using large and small radar network fusion in specific areas.

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.