Changes in aerosol loadings and properties are important for the understanding of atmospheric environment and climate change. This study investigates the characteristics and long-term trends of aerosols of different sizes and types in Asia from 2000 to 2020 by considering multi-source aerosol data and novel analysis methods and perspectives, all of which provides the groundwork for promoting the acquisition of new discoveries that are different from the past. The geometric mean aggregation method is applied, and serial autocorrelation is considered to avoid the overestimation of trend significance. Among regions in Asia, high values of aerosol optical depth (AOD) are mainly concentrated in East Asia (EA) and South Asia (SA) and are closely related to the population density. The AOD in EA showed the most significant negative trend, with a value of $-\mathrm{5.28}\times {\mathrm{10}}^{-\mathrm{4}}$ per year, mainly owing to decreases in organic carbon (OC), black carbon (BC), and dust aerosols. It is also worth noting that this observed large-scale decrease in OC and BC is a unique and significant phenomenon to region of EA and mainly around China. In contrast, the aerosol concentrations in SA generally show a positive trend, with an increased value of AOD of 1.25 × 10⁻³ per year. This increase is mainly due to large emissions of fine-mode aerosols, such as OC and sulfate aerosol. Additionally, the high aerosol loading in northern SA has a lower AOD variability when compared with that of East China Plain, revealing a relatively more persistent air pollution situation. Over the whole region of Asia, the characteristics of the percentage changes in different types of AOD are seen in the increases in BC (6.23 %) and OC (17.09 %) AOD, together with a decrease in dust (−5.51 %), sulfate (−3.07 %), and sea salt (−9.80 %) AOD. Except for anthropogenic emissions, the large increase in the percentage of OC is also due to wildfires found in northern Asia in the summer, whereas the differently sized AOD only shows slight changes in Asia in that small-sized AOD decreases (−3.34 %) and that the total AOD did not show a significant change. This suggests that, from a trend perspective, decreases in aerosol in recent years have mostly been offsetting earlier increases in the anthropogenic emissions over Asia. To summarize, the above findings analyse the comprehensive characteristics of aerosol distributions and reappraise the long-term trends of different aerosol parameters, which will greatly enhance the understanding of regional and global aerosol environment and climatology, in addition to filling the gaps and breaking through the limitations of past knowledge.

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) provides three-dimensional information on aerosol optical properties across the globe. However, the performance of CALIPSO aerosol optical depth (AOD) products under different air quality conditions remains unclear. In this research, three years of CALIPSO level 2 AOD data (November 2013 to December 2017) were employed to compare with the Moderate Resolution Imaging Spectroradiometer (MODIS) level 2 columnar AOD products and ground-based sun photometer measurements for the same time period. To investigate the effect of air quality on AODs retrieved from CALIPSO, the AODs obtained from CALIPSO, MODIS, and sun photometer were inter-compared under different air quality conditions over Wuhan and Dengfeng. The average absolute bias of AOD between CALIPSO and sun photometer was 0.22 ± 0.21, 0.11 ± 0.07, and 0.14 ± 0.13 under clean, moderate, and polluted weather, respectively. The result indicates that the CALIPSO AOD were more reliable under moderate and polluted days. Moreover, the deviation of AOD between CALIPSO and sun photometer was largest (0.23 ± 0.21) in the autumn season, and lowest (0.13 ± 0.12) in the winter season. The results show that CALIPSO AOD products were more applicable to regions and seasons with high aerosol concentrations.

Aerosol spatial distribution obtained from satellite sensors is critical for understanding regional aerosol environments, anthropogenic aerosol emissions, and global climate change. The Directional Polarimetric Camera (DPC) is the first generation of multi-angle polarized sensor developed by China. It is on-board the GaoFen-5 satellite, running in 705 km sun-synchronous orbit with a 13:30 LT (local time) ascending node. The sensor has three polarized channels at 490, 670, and 865 nm and ∼ 9 viewing angles, mainly used for observing aerosols. The spatial resolution is ∼ 3.3 km at nadir, and global coverage is ∼ 2 d. In this study, the performance of aerosol optical depth (AOD) retrievals from the DPC/GaoFen-5 using the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm were evaluated on a global basis for the first time. The results showed that the DPC GRASP/Model scheme, which used several forms of aerosol-type mixing, achieved good performance. By comparing with Aerosol Robotic Network (AERONET) observations, the correlation coefficient (R), root-mean-square error (RMSE), and expected error (EE%, ± ($\mathrm{0.05}+\mathrm{0.15}\times \text{AOD}$ )) were 0.9007 %, 0.0662 %, and 82.54 %, respectively. The scattering angle, number of averaged pixels, length of time steps, and radiative and polarized fitting residuals showed impacts on the results of AOD retrieval in the DPC GRASP/Model scheme. From the most AERONET sites, the R and EE% were larger than ∼ 0.9 % and ∼ 80%. Compared with Moderate resolution Imaging Spectroradiometer (MODIS) products, the spatial and temporal variations of aerosol could be caught by the DPC with the GRASP/Model scheme, showing a good performance. However, values of AOD were also underestimated by DPC, probably due to an overly strict cloud mask. The above findings validated the ability of the DPC sensor to monitor aerosols. It should contribute to the development of aerosol parameter retrieval from multi-angle polarized sensors in the future.

The atmospheric boundary layer is an important atmospheric feature that affects environmental health and weather forecasting. In this study, we proposed a graphics algorithm for the derivation of atmospheric boundary layer height (BLH) from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Owing to the differences in scattering intensity between molecular and aerosol particles, the total attenuated backscatter coefficient 532 and attenuated backscatter coefficient 1064 were used simultaneously for BLH detection. The proposed algorithm transformed the gradient solution into graphics distribution solution to overcome the effects of large noise and improve the horizontal resolution. This method was then tested with real signals under different horizontal smoothing numbers (1, 3, 15 and 30). Finally, the results of BLH obtained by CALIPSO data were compared with the results retrieved by the ground-based lidar measurements. Under the horizontal smoothing number of 15, 12 and 9, the correlation coefficients between the BLH derived by the proposed algorithm and ground-based lidar were both 0.72. Under the horizontal smoothing number of 6, 3 and 1, the correlation coefficients between the BLH derived by graphics distribution method (GDM) algorithm and ground-based lidar were 0.47, 0.14 and 0.12, respectively. When the horizontal smoothing number was large (15, 12 and 9), the CALIPSO BLH derived by the proposed method demonstrated a good correlation with ground-based lidar. The algorithm provided a reliable result when the horizontal smoothing number was greater than 9. This finding indicated that the proposed algorithm can be applied to the CALIPSO satellite data with 3 and 5 km horizontal resolution.

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol.

Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.