The parameterization of raindrop size distribution (DSD) is critical for the satellite precipitation retrieval algorithms. Utilizing the multiple ground-based two-dimensional video (2DVD) and Particle Size and Velocity (Parsivel) disdrometers data from the Precipitation Validation Network (Guangdong) of the Fengyun satellites during April–September 2024, this study evaluates the uncertainty of DSD parameterization on a dual-frequency (DF) precipitation retrieval algorithm over South China. It is shown that the composite raindrop spectra generally conform to the gamma distribution, with the shape parameter μ on average of 4.5–4.8, which is higher than the fixed μ = 3 used in the Global Precipitation Measurement mission (GPM) Dual-Frequency Precipitation Radar (DPR) algorithms. By varying the μ value in the DSD gamma model, the effects on the retrieved mass-weighted mean diameter (D_m), normalized intercept parameter (N_w), and rain rate are examined. As μ increases from 1 to 6, the underestimation of D_m shifts to overestimation, while for lgN_w and rain rate, it is the opposite. The overestimation of rainfall, especially at the range of 8–32 mm h⁻¹, mainly comes from underestimated D_m and overestimated lgN_w. On the contrary, overestimation of D_m and underestimation of lgN_w mainly lead to underestimated rainfall, especially when rain rate is above 64 mm h⁻¹. Comprehensive analysis shows that the DSD gamma distribution with μ in the range of 4–5 may be more suitable for South China. These results provide valuable reference for optimizing the DSD module of the precipitation retrieval algorithm for the Fengyun-3G (FY-3G) satellite.

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.

Monitoring and predicting highly localized weather events over a very short-term period, typically ranging from minutes to a few hours, are very important for decision makers and public action. Nowcasting these events usually relies on radar observations through monitoring and extrapolation. With advanced high-resolution imaging and sounding observations from weather satellites, nowcasting can be enhanced by combining radar, satellite, and other data, while quantitative applications of those data for nowcasting are advanced through using machine learning techniques. Those applications include monitoring the location, impact area, intensity, water vapor, atmospheric instability, precipitation, physical properties, and optical properties of the severe storm at different stages (pre-convection, initiation, development, and decaying), identification of storm types (wind, snow, hail, etc.), and predicting the occurrence and evolution of the storm. Satellite observations can provide information on the environmental characteristics in the pre-convection stage and are very useful for situational awareness and storm warning. This paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges, and future perspectives are also addressed and discussed.