This study analyzes the spatial and temporal distribution characteristics of pollutants and the influence of meteorological factors using data from 13 cities in Jiangsu Province from 2014 to 2022. The results showed that, from 2014 to 2022, the average concentrations of PM_2.5, PM₁₀, SO₂, and CO in Jiangsu were high in the north and low in the south, while the NO₂ concentration was low in the north and high in the south, all of which decreased over time. O₃ concentration was higher on the eastern coast and increased with the year. PM_2.5 pollution days in northern Jiangsu were higher in autumn and winter. O₃ pollution days in southern Jiangsu were higher in spring, summer, and autumn. There was a significant positive correlation between O₃ and temperature in spring and autumn, and it was weaker in summer. Relative humidity (RH) in winter was positively correlated with PM_2.5 and RH showed a significant negative correlation with PM₁₀ in spring, summer, and autumn. The scavenging effect of precipitation on PM₁₀ concentration was the most pronounced, followed by PM_2.5. Precipitation has the weakest scavenging effect on CO, only reducing its concentration by an average of 13%. Precipitation also exhibits a significant scavenging effect on O₃. The decrease in O₃ was the smallest on heavy rain days (14.7%) and the largest on severe, torrential rain days (25.9%).

In this study, based on the retrieval data from the Fengyun geostationary meteorological satellite and the Tropical Rainfall Measuring Mission satellite, a large-scale precipitation case in eastern China is selected to address the systematic deviations of deterministic forecasts for clouds and precipitation. A multi-physical ensemble transform Kalman filter (ETKF) is constructed in this research based on the Weather Research and Forecast model version 3.6, and its forecasting ability in terms of cloud-top height and temperature, hydrometeors, and precipitation is evaluated by quantitatively comparing three microphysical parameterization schemes (Lin, Morrison, and CAM5.1 schemes) and their corresponding multi-physical ensemble mean. The results show that the Lin, Morrison, and CAM5.1 schemes all underestimate the range of cloud systems and have different advantages and disadvantages in forecasting different elements, while the forecasting improvement of the multi-physical ensemble mean is limited. However, the multi-physical ETKF can effectively improve the forecast accuracy of the cloud system range. In addition, the multi-physical ETKF has the advantages of different physical parameterization schemes, which can dramatically improve the forecast accuracy of cloud hydrometeors, reduce precipitation forecast errors, and improve threat scores.

Through the application of cloud top temperature data and the extraction of supercooled cloud information in cloud-type data from the next-generation Himawari-8 geostationary satellite with high spatial–temporal resolution, a quantitative evaluation of the forecasting performance of the weather modification model named the Cloud and Precipitation Explicit Forecasting System (CPEFS) was conducted. The evaluation, based on selected forecast cases from 8 days in September and October 2018 initialized at 00 and 12 UTC every day, focused especially on the forecasting performance in supercooled clouds (vertical integrated supercooled liquid water, VISL > 0), including the comprehensive spatial distribution of cloud top temperature (CTT) and 3 h precipitation over 0.1 mm (R3 > 0.1). The results indicated that the forecasting performance for VISL > 0 was relatively good, with the Threat Score (TS) ranging from 0.46 to 0.67. The forecasts initialized at 12 UTC slightly outperformed the forecasts initialized at 00 UTC. Additionally, the corresponding spatial Anomaly Correlation Coefficient (ACC) of CTT between forecasts and observations was 0.23, and the TS for R3 > 0.1 reached as high as 0.87. For a mix of cold and warm cloud systems, there was a correlation between the forecasting performance of VISL > 0 and CTT. The trends in the TS for VISL > 0 and the ACC of CTT aligned with the forecast lead-time.