Automatic recognition of convective clouds from geostationary satellite imagers is essential for enhancing situational awareness and severe storms nowcasting. A dynamic classification framework is developed for rapid extraction of convective clouds from high temporal and spatial resolution Advanced Geostationary Radiation Imager (AGRI) infrared (IR) band measurements. While existing cloud property retrievals (including cloud phase and cloud type discrimination) mostly rely on radiative transfer modeling with empirical thresholds, such static approaches demonstrate limited operational reliability across diverse meteorological regimes and synoptic conditions, and do not output convection areas directly. To address this gap, built on the cloud type products and AGRI IR radiance measurements, a regionally adaptive K-means dynamic clustering algorithm is implemented for seven regions (Northeast, North, East, Central, South, Northwest, and Southwest China) over the Chinese mainland, counting for spatial heterogeneity in cloud regime characteristics. While this methodology overcomes the limitation of using only thresholds in cloud type production through adaptation of K-means technique, it also avoids the lack of prior information in K-means technique by using cloud type product. Validation against ground-based radar data during mid-June and mid-July of 2023 reveals good agreement between convective clouds and high dBZ values, and further comparisons with rain-gauge measurements show high correlation between convective clouds and heavy rainfall. Implementation of this dynamic classification approach enables near real-time convective monitoring with 15-min refresh cycles for Fengyun-4, significantly enhancing nowcasting capabilities for severe weather events.

Cloud water plays an important role in the global atmospheric water cycle and weather modification, but cloud is one of the most uncertain parameters in the study of weather and climate. The cloud water products from different data sources may have considerable discrepancies. In this study, the total cloud liquid water (termed as cloud liquid water path, LWP) obtained from satellite observations [Advanced Himawari Imager (AHI) and Advanced Microwave Scanning Radiometer (AMSR)] and three sets of modern reanalysis data (ERA5, JRA-55, and MERRA-2) are compared and analyzed. Moreover, characteristics of vertical distributions of cloud liquid water content (LWC) in different regions over East Asia are analyzed by using the profile data from the reanalyses. The main findings are as follows: (1) in extensive warm marine clouds, AHI and AMSR have a good agreement (with the correlation coefficient larger than 0.7) but with an overestimation from AHI; (2) under warm cloud conditions, the LWP in ERA5 shows a significant positive bias (about 0.065 kg m⁻²) over land, while MERRA-2 is closer to the satellite product compared with ERA5 and JRA-55; and (3) Southwest China (SW) is the area with most abundant LWC. The LWC is mainly concentrated in the middle and lower troposphere in the study area, and the LWC in ERA5 is higher than that in MERRA-2 and JRA-55. Overall, satellite observations and reanalyses exhibit significant inconsistency for cloud LWP, which needs further investigation and understanding.

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.