A 10 h needle-shaped snow process occurred in Weihai, east of Shandong Province, on 21 February 2024. The snowfall amount reached the blizzard level, which is a rare occurrence. In this paper, the synoptic background and microphysical characteristics of the needle-shaped snow process are analyzed based on comprehensive observations of dual polarization radars, precipitation weather instruments, ground automatic stations, soundings, ERA5 reanalysis data and Quasi-Vertical Profiles (QVP) method. The causes of the needle-shaped snow are discussed. The results are as follows: (1) The needle-shaped snow process occurred under the background of large-scale rain and snow in China. During the needle-shaped snow period, freezing rain turned into ice particles in southern Shandong Province, and ice particles transformed into sheet or branch snow in central and northern Shandong Province. The influencing system was a return-flow situation, with strong northeasterly winds below 925 hPa and strong southwesterly winds above 700 hPa. (2) The cloud top height of the needle-shaped snow event was about 500 hPa, and temperature below 600 hPa maintained at −6—−3℃ when the needle-shaped snow occurred. This is also the main characteristic that distinguishes needle-shaped snow from other types of snowfall such as ice pellets, freezing rain and plate crystals. (3) The diameter of needle-shaped crystal particles was 3—4 mm, the maximum was 8 mm, the final falling velocity was largely below 2 m/s, and the particle number concentration was two orders of magnitude higher than that of sleet. The snowfall intensity had a certain relationship with the size and particle number concentration of snowfall particles. The diameter of heavy snowfall particles with hourly snowfall greater than 1 mm was larger and the particle number concentration was higher. (4) Reflectance factor (Z_e) was generally within 20—30 dBz, differential reflectivity (Z_DR) reached up to 0.8—1.0 dB, and the high value area of differential propagation phase shift (K_DP) was concentrated below 1 km during heavy snowfall period. (5) Supercooled water was abundant during the needle-shaped snow process, and there existed secondary production of ice, which led to a high ice crystal particle number concentration.

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.