In this study, a mesoscale Weather Research and Forecast (WRF) model coupled with an AgI (silver iodide) cold cloud catalytic module were used to explore the potential impact of the catalytic position and rate in the catalytic module based on a ground rain enhancement operation in the Qilian Mountains, on 16 August 2020. Results show that the simulated precipitation, liquid water content (LWC), and water vapor content (PWV) are in good agreement with the observations, demonstrating that the WRF model using the coupled AgI cloud-seeding scheme is well-applicable to the precipitation simulation of the Qilian Mountains. It is also observed that there are some differences in the catalytic effect of catalysis at different cloud temperatures. The precipitation enhancement effect is the most favorable in the fifth layer of 15 km, followed by that in the fourth layer of 12 km and the sixth layer of 18 km. Considering the flight cost and catalytic efficiency, the fourth layer is highly recommended for seeding. Furthermore, the AgI seeding rate also plays a crucial impact on ground precipitation. In the case of a seeding rate of about 1.2 g·s⁻¹, the precipitation enhancement effect tends to be stable, and the percentage of the precipitation increase reaches up to 10.4%. While in the case of a seeding rate of about 1.5 g·s⁻¹, the percentage of ground precipitation increase is 10%, which is 0.4% lower than that of 1.2 g·s⁻¹. In summary, the introduction of a AgI catalyst with a seeding rate of 1.2 g·s⁻¹ can significantly increase the ground precipitation at a height of 12 km and a temperature of −3 °C in the Qilian Mountains.

The Gradient Richardson Number (R_i) is an important parameter for appraising the stability and turbulence exchange at the atmospheric boundary layer (ABL). However, high-resolution measurements of R_i profiles are rarely reported, especially in megacities. In this study, a Doppler wind lidar and a microwave radiometer were simultaneously utilized to measure the 2 km R_i vertical profile in downtown Beijing. These measurements were verified to have high accuracy compared with observations from a 325 m meteorological tower, with root-mean-square errors (RMSEs) of less than 1.66 K, 7.9%, and 1.45 m/s for the temperature, relative humidity, and wind speed (WS) for all altitudes and corresponding Pearson correlation coefficients (R) of 0.97, 0.93, and 0.81. The inter-comparisons of different spatial (25 m, 50 m, 100 m) and temporal resolutions (1 min, 30 min, 1 h) form a 3 × 3 resolution matrix of R_i, in which the 1 h temporal resolution of R_i overestimates the intensity and active area of turbulence. The R_i value retrieved from the 100 m spatial resolution data overestimates these by half as it misidentifies the height of the stable area at the near surface. There are significant differences between the data with a 1 min temporal resolution and a 25 m spatial resolution (defined as the standard resolution of R_i), and the rest of the data in the resolution matrix (defined as data at other resolutions), with an RMSE > 1 and an R < 0.8. The difference between data at the standard resolution and data at other resolutions increases with elevations, which results from frequent weather processes or from water-vapor blocking at higher altitudes. The R_i profiles reveal that the atmospheric layer at altitudes from 100 m to 500 m in daytime is unstable, with R_i < 0, while it is neutral, with 0 < R_i < 0.25, at night-time from 200 m to 400 m. The atmosphere above the ABL in a megacity is rather stable, with R_i > 0.25, whereas below the ABL, it is neutral or unstable, which is due to drastic changes in the WS and temperature that are affected by the topography and surface friction.

At present, atmospheric aerosol pollution is a major issue in China. Himawari-8 (H8) aerosol products with high temporal resolutions are helpful for achieving real-time monitoring of subtle aerosol variation. However, the H8 aerosol optical depth (AOD) product has been updated three times since its launch, and evaluations of this dataset are scarce at present. To validate its accuracy, this study compared the H8 Level-2 (L2) minute AOD products of all versions with measurements obtained from eight typical sites in eastern China, a high aerosol region, in 2016. The AOD of H8 version 010 underestimated observed AOD by 37%-46% in this region. The overall underestimation of AOD in version 021 still exists (7%-15%). The absolute error of H8 AOD in version 010 symmetrically increased by 0.05 while increasing the observed AOD by 0.1, which was a span error for H8 AOD in version 010. Based on the absolute error linear regression equation, a span error correction was performed in the region. Compared with the AOD of version 021, the span error corrected AOD of version 010 has lower uncertainty (Root Mean Squared Error (RMSE): 0.13-0.39) and higher consistency (s: 0.80-1.2). The underestimation was also further improved (2%-4%).

Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the Beijing area from 2012 to 2013, we found that the annual average concentration was 41.32 μg·m⁻³, with the largest percentage of organics accounting for 49.3% of NR-PM1, followed by nitrates, sulfates, and ammonium. We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. By employing the CM model to the spatially and temporally matched AOD (aerosol optical depth) data and meteorological data of the Beijing–Tianjin–Hebei region, the spatial distribution of the annual average concentrations of the four components was obtained. The areas with higher concentrations are mainly situated in the southwest of Beijing, and the annual average concentrations of the four components in Beijing’s southwest are 28 μg·m⁻³, 7 μg·m⁻³, 8 μg·m⁻³, and 15 μg·m⁻³ for organics, sulfates, ammonium, and nitrates, respectively. This study not only provides new methodological ideas for obtaining aerosol chemical composition concentrations based on satellite remote sensing data but also provides a data foundation and theoretical support for the formulation of atmospheric pollution prevention and control policies.

Aerosol radiative forcing is known to significantly disturb the thermodynamic and dynamic structure of the Planetary Boundary Layer (PBL), particularly in heavily polluted urban regions. In this study, the effects of aerosol–PBL interactions were examined over a megacity in China’s Central Plains by comparing ERA5 reanalysis data with multi-source ground-based observations. Key meteorological variables—including wind speed, wind direction, temperature, and relative humidity—were analyzed across pressure levels from 1000 to 800 hPa. Good agreement in wind direction was observed between ERA5 and observations (R² > 0.84), while wind speed showed a moderate correlation (R² = 0.54–0.73) with an RMSE of 1.85 m/s near 975 hPa. Temperature discrepancies were found to decrease with altitude, with RMSE values reducing from 3.02 K to 1.84 K, indicating a modulation of thermal stratification by aerosol radiative effects. A stable structure was revealed by humidity analysis near the surface but increased variability aloft, with absolute differences reaching ±30% at 850–800 hPa. Diurnal variations were characterized by night-time warming of up to +5 °C in the lower PBL and concurrent cooling above 800 hPa. The Heating and surface Dimming (HD) Index was found to correlate positively with PM_2.5 concentration (R = 0.60), reflecting increased thermal stability and vertical inhomogeneity under aerosol loading. These findings underscore the need for an improved understanding and mitigation of aerosol–PBL interactions to support sustainable urban air quality management strategies.