The horizontal and vertical distributions of aerosol optical properties over China in 2013–2015 were investigated using RAMS (Regional Atmospheric Modeling System)-CMAQ (Models-3 Community Multiscale Air Quality) simulations and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations. To better understand the performance of the RAMS-CMAQ model over China, comparisons with the ground-based Sun photometers AERONET (Aerosol Robotic Network), MODIS (Moderate Resolution Imaging Spectroradiometers) data and the on-board Lidar CALIOP were used for comprehensive evaluations, which could characterize the abilities of the model to simulate the spatial and vertical distributions of the AOD (Aerosol Optical Depth) as well as the optical properties for four seasons. Several high value areas (e.g., the Sichuan Basin, Taklamakan Desert, North China Plain, and Yangtze River Delta) were found over China during the study period, with the maximum mean AOD (CALIOP: ~0.7; RAMS-CMAQ: >1) in the Sichuan district. Compared with AODs of AERONET, both the CALIOP and RAMS-CMAQ AODs were underestimated, but the RAMS-CMAQ data show a better correlation with AERONET (AERONET vs. RAMS-CMAQ R: 0.69, AERONET vs. CALIOP R: 0.5). The correlation coefficients between RAMS-CMAQ and CALIOP are approximately 0.6 for all four seasons. The AEC (Aerosol Extinction Coefficient) vertical profiles over major cities and their cross sections exhibit two typical features: (1) most of the AEC peaks occurred in the lowest ~0.5 km, decreasing with increasing altitude; and (2) the RAMS-CMAQ AEC underestimated the region with high AODs in the northwest of China and overestimated the region with high AODs in the east–central plain and the central basin regions. The major difference in the AEC values of RAMS-CMAQ and CALIOP is mainly caused by the level of relative humidity and the hygroscopic growth effects of water-soluble aerosols, especially, in the Sichuan district. In general, both the column and vertical RAMS-CMAQ aerosol optical properties could be supplemented efficiently when satellite observations are not available or invalid over China in the applications of climate change and air pollution.

The aerosol liquid water content (ALWC) dictates the hygroscopicity of aerosol particles. To date, measurements of ALWC have been confined primarily to ground-based observations although vertical profiles of ALWC are crucial for understanding its interactions with meteorology. This study proposes a novel method for deriving profiles of ALWC using data acquired by a Light Detection and Ranging (LiDAR), a microwave radiometer, and a suite of aerosol instruments measuring aerosol physical and chemical properties, deployed during a five-month field experiment in Guangzhou, China. The retrieval approach is based on a machine-learning model named the gradient-boosted decision tree model. The inversion accuracy and stability are assessed through comparisons with ALWC data acquired on the ground and at the top of the Guangzhou tower of 532 m above ground. The agreements are encouraging: with the coefficient of determination $({R} ^{2}) = 0.870$ and root-mean-square error (RMSE) = $3.28 ~\mu \text{g} ~\cdot $ m ⁻³ for all data; ${R} ^{2} = 0.776$ and RMSE = $2.18 ~\mu \text{g}~ \cdot $ m ⁻³ for tower data; and ${R} ^{2} = 0.872$ and RMSE = $4.1~ \mu \text{g} ~\cdot $ m ⁻³ for ground data. From the vertical distribution of the retrieved ALWC in Guangzhou, ALWC is higher in the lower boundary layer, especially when air pollution is severe. The proportion of liquid water in aerosol particles is closely related to the relative humidity in the environment, which will affect the morphology of aerosol particles (with about every 10% increase in liquid water, the depolarization ratio decreases by 0.02). The model may be of general use for studying air pollution and secondary aerosol generation.