To address the insufficient applicability of the original hail size discrimination algorithm (HSDA) in China, a modified HSDA applicable to S-band and C-band dual-polarization radars in China is proposed in this study. Utilizing 760 ground hail reports and dual-polarization radar observations in China from 2021 to April 2026, this study statistically analyzes the polarimetric characteristics of different hail sizes at various height layers, based on which the membership functions are optimized. The findings are as follows. (1) Differences in hail polarimetric characteristics observed by different radar wavelengths are identified, which are attributed to the combined effects of hail melting, resonance scattering, and attenuation correction residual errors. In lower height layers, the differential reflectivity (Z_DR) of S-band radars increases as the horizontal reflectivity factor (Z_H) decreases, while essentially negligible correlation between the two is observed for C-band radars. (2) The modified HSDA at S-band expands the identified areas of small and large hails while reducing the area of giant hail. The classification accuracy of the modified HSDA for S-band radars at 1.5° elevation exceeds 80%, representing an 8% increase compared to the original HSDA. Although the HSDA was originally developed only for S-band, adapting the membership functions to C-band characteristics enables the classification accuracy to exceed 81% at 1.5° elevation, demonstrating the algorithm’s robust adaptability to different radar wavelengths. (3) Analysis of the severe hail event in Beijing on 13 May 2025 reveals that the modified HSDA effectively reveals the evolution of hail sizes and the growth process of large hail within the storm. Furthermore, deepening of the Z_DR and K_DP columns can provide a lead time of 18–30 min prior to the descent of giant hail to the ground.

For numerical weather prediction (NWP), data assimilation (DA) combines short-term forecasts and various atmospheric observations to achieve optimal initial conditions, based on which subsequent forecasts are launched. With the rapid advancements in numerical models and observing systems, DA has been significantly evolved. Modern methods now can account for uncertainties of state variables across various spatiotemporal scales, incorporate multiscale observation error statistics, and enforce dynamical constrains and model balances. Meanwhile, observations from various platforms, such as ground-based, aircraft, and satellite, have been assimilated. These include data from polar-orbiting and geostationary satellites, radar-derived radial winds and reflectivity, Global Navigation Satellite System (GNSS) radio occultations, etc. To further utilize the advanced observing systems and DA techniques for high-impact weather predictions, target observation strategies have been developed to identify areas where additional observations can yield the greatest predict improvements. Based on the advancements of DA theories and methods, China’s operational systems have made significant progress, establishing advanced operational DA systems. Over the past decade, the forecast skill of 5-day global weather prediction has improved by approximately 15%. The article reviews a century of development in DA, and discusses future directions, including the advanced DA methods, operational frameworks, integration of novel observations, and the synergy between DA and artificial intelligence.