Cloud effective radius (CER) is a fundamental microphysical property of clouds, critical for understanding cloud formation and radiative effects. Satellite spectral imagers, widely utilized in passive remote sensing, facilitate the monitoring of cloud characteristics, including CER, over extended time periods and spatial scales. Various observational methods have been employed to evaluate satellite cloud property products; however, in situ measurement evaluations of CER remain limited, particularly for products over China. This study utilized aircraft observations provided by the China Meteorological Administration Weather Modification Centre to evaluate the CER retrieved by Fengyun-4A Advanced Geosynchronous Radiation Imager (AGRI) and Himawari-8 Advanced Himawari Imager (AHI). Three flights were selected from the aircraft dataset for evaluation, involving flights through non-precipitating stratiform clouds with stable, high-quality measurements. Rigorous data selection and collocation procedures were employed to ensure a comprehensive comparison. Satellite retrievals from heterogeneous cloud fields were excluded, and representative in-cloud aircraft measurements were identified through multi-parameter filtering. The flight trajectory was adjusted to account for horizontal cloud movement corresponding to time differences between observations from different platforms. Additionally, in situ measurements from different vertical layers were adjusted to a comparable position near the cloud top. Results indicate that CER retrieved from satellites is generally overestimated compared to in situ measurements. For AGRI, the average difference (AD) is 2.90 µm, with a root mean square difference (RMSD) of 3.53 µm. For AHI, the AD is 2.92 µm, and the RMSD is 3.59 µm. To enhance future validation and evaluation of remote sensing results, factors such as instrument calibration, flight patterns, and cloud conditions will be carefully considered. Increasing the number of cases should further reduce errors associated with individual instances, enabling more precise assessments.

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.