The aerosol–planetary boundary layer (PBL) interaction was proposed as an important mechanism to stabilize the atmosphere and exacerbate surface air pollution. Despite the tremendous progress made in understanding this process, its magnitude and significance still have large uncertainties and vary largely with aerosol distribution and meteorological conditions. In this study, we focus on the role of aerosol vertical distribution in thermodynamic stability and PBL development by jointly using micropulse lidar, sun photometer, and radiosonde measurements taken in Beijing. Despite the complexity of aerosol vertical distributions, cloud-free aerosol structures can be largely classified into three types: well-mixed, decreasing with height, and inverse structures. The aerosol–PBL relationship and diurnal cycles of the PBL height and PM_2.5 associated with these different aerosol vertical structures show distinct characteristics. The vertical distribution of aerosol radiative forcing differs drastically among the three types, with strong heating in the lower, middle, and upper PBL, respectively. Such a discrepancy in the heating rate affects the atmospheric buoyancy and stability differently in the three distinct aerosol structures. Absorbing aerosols have a weaker effect of stabilizing the lower atmosphere under the decreasing structure than under the inverse structure. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and can be potentially neutralized by the decreasing structure. Moreover, aerosols can both enhance and suppress PBL stability, leading to both positive and negative feedback loops. This study attempts to improve our understanding of the aerosol–PBL interaction, showing the importance of the observational constraint of aerosol vertical distribution for simulating this interaction and consequent feedbacks.

Light detection and ranging (lidar) measurements have been widely used to profile the ambient aerosol extinction coefficient (σ_ext). The particle extinction-to-backscatter ratio (lidar ratio, LR), which strongly depends on the aerosol dry particle number size distribution (PNSD) and aerosol hygroscopicity, is introduced to retrieve the σ_ext profile from elastic-backscatter lidar signals. Conventionally, a constant column-integrated LR that is estimated from aerosol optical depth is used by the retrieving algorithms. In this paper, the influences of aerosol PNSD, aerosol hygroscopic growth and relative humidity (RH) profiles on the variation in LR are investigated based on the datasets from field measurements in the North China Plain (NCP). Results show that LR has an enhancement factor of 2.2 when RH reaches 92 %. Simulation results indicate that both the magnitude and vertical structures of the σ_ext profiles by using the column-related LR method are significantly biased from the original σ_ext profile. The relative bias, which is mainly influenced by RH and PNSD, can reach up to 40 % when RH at the top of the mixed layer is above 90 %. A new algorithm for retrieving σ_ext profiles and a new scheme of LR enhancement factor by RH in the NCP are proposed in this study. The relative bias between the σ_ext profile retrieved with this new algorithm and the ideal true value is reduced to below 13 %.

Aeolus wind products became available to the public on 12 May 2020. In this study, Aeolus wind observations, L-band radiosonde (RS) data, and the European Centre for Medium-Range Weather Forecasts fifth-generation atmospheric reanalysis (ERA5) data were used to analyze the seasonality of Aeolus wind product performance over China. Based on the Rayleigh-clear and Mie-cloudy data, the data quality of the Aeolus effective detection data was verified, and the results showed that the Aeolus data were in good agreement with the L-band RS and ERA5 data. The Aeolus data relative errors in the four regions (Chifeng, Baoshan, Shapingba, and Qingyuan) in China were calculated based on different months (July to December 2019 and May to October 2020). The relative error in the Rayleigh-clear data in summer was significantly higher than that in winter, with the mean relative error parameter in July 174 % higher than that in December. The mean random error increased by 0.97 m s⁻¹ in July compared with December, which also supported this conclusion. In addition, the distribution of the wind direction and high-altitude clouds in different months (July and December) was analyzed. The results showed that the distribution of the angle between the horizontal wind direction of the atmosphere and the horizontal line of sight had a greater proportion in the high error interval (70–110^∘) in summer, and this proportion was 8.14 % higher in July than in December. The cloud top height in summer was approximately 3–5 km higher than that in winter, which might decrease the signal-to-noise ratio of Aeolus. Therefore, the wind product performance of Aeolus was affected by seasonal factors, which might be caused by seasonal changes in wind direction and cloud distribution.

Determination of cloud condensation nuclei (CCN) number concentrations at cloud base is important to constrain aerosol–cloud interactions. A new method to retrieve CCN number concentrations using backscatter and extinction profiles from multiwavelength Raman lidars is proposed. The method implements hygroscopic enhancements of backscatter and extinction with relative humidity to derive dry backscatter and extinction and humidogram parameters. Humidogram parameters, Ångström exponents, and lidar extinction-to-backscatter ratios are then linked to the ratio of CCN number concentration to dry backscatter and extinction coefficient (ARξ). This linkage is established based on the datasets simulated by Mie theory and κ-Köhler theory with in-situ-measured particle size distributions and chemical compositions. CCN number concentration can thus be calculated with ARξ and dry backscatter and extinction. An independent theoretical simulated dataset is used to validate this new method and results show that the retrieved CCN number concentrations at supersaturations of 0.07 %, 0.10 %, and 0.20 % are in good agreement with theoretical calculated values. Sensitivity tests indicate that retrieval error in CCN arises mostly from uncertainties in extinction coefficients and RH profiles. The proposed method improves CCN retrieval from lidar measurements and has great potential in deriving scarce long-term CCN data at cloud base, which benefits aerosol–cloud interaction studies.