The new-generation atmospheric environment monitoring satellite DQ-1, launched successfully in April 2022, carries the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The ACDL/DQ-1 is a high-spectral-resolution lidar (HSRL) that separates molecular backscatter signals using an iodine filter and has 532 nm polarization detection and dual-wavelength detection at 532 and 1064 nm, which can be utilized to derive aerosol optical properties. The methods have been specifically developed for data processing and optical property retrieval according to the specific characteristics of the ACDL system and are introduced in detail in this paper. Considering the different signal characteristics and different background noise behaviors of each channel during daytime and nighttime, the procedures of data pre-processing, denoising process and quality control are applied to the original measurement signals. The aerosol and cloud optical property products of the ACDL/DQ-1, including the total depolarization ratio, backscatter coefficient, extinction coefficient, lidar ratio and color ratio, can be calculated by the retrieval algorithms presented in this paper. Two measurement cases with use of the ACDL/DQ-1 on 27 June 2022 and the global averaged aerosol optical depth (AOD) from 1 June to 4 August 2022 are provided and analyzed, demonstrating the measurement capability of the ACDL/DQ-1.

After the successful launch of Aeolus, which is the first spaceborne wind lidar developed by the European Space Agency (ESA), on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. To ensure the quality of the measurement data from CDLs and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced Level 2B (L2B) Mie-cloudy HLOS wind and Rayleigh-clear HLOS wind and CDL-produced HLOS wind are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled mean absolute deviation (MAD) and the bias are 0.83, 3.15 m s⁻¹, 2.64 m s⁻¹ and −0.25 m s⁻¹, respectively, while the y=ax slope, the $y=ax+b$ slope and the $y=ax+b$ intercept are 0.93, 0.92 and −0.33 m s⁻¹. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m s⁻¹, 5.77 m s⁻¹ and −1.15 m s⁻¹, respectively, while the y=ax slope, the $y=ax+b$ slope and the $y=ax+b$ intercept are 1.00, 0.96 and −1.2 m s⁻¹. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07 and 08, Baselines 09 and 10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

This paper briefly discusses the mobile ground-based incoherent Doppler wind lidar system, with iodine filters as receiving frequency discriminators, developed by the Ocean Remote Sensing Laboratory, Ocean University of Qingdao, China. The presented result of wind profiles in October and November 2000, retrieved from the combined Mie and Rayleigh backscattering, is the first report to our knowledge of wind measurements in the troposphere by such a system, where the required independent measurement of aerosol-scattering ratio can also be performed. A second iodine vapor filter was used to lock the laser to absolute frequency reference for both wind and aerosol-scattering ratio measurements. Intercomparison experiments of the lidar wind profile measurements were performed with pilot balloons. Results showed that the standard deviation of wind speed and wind direction, for the 2–4 km altitude range, were 0.985 m/s and 17.9°, respectively.

Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to obtain backward trajectories and then conduct clustering analysis is a common method to analyze potential sources and transmission paths of atmospheric particulate pollutants. Taking Qingdao (N36 E120) as an example, the global data assimilation system (GDAS 1°) of days from 2015 to 2018 provided by National Centers for Environmental Prediction (NCEP) is used to process the backward 72 h trajectory data of 3 arrival heights (10 m, 100 m, 500 m) through the HYSPLIT model with a data interval of 6 h (UTC 0:00, 6:00, 12:00, and 18:00 per day). Three common clustering methods of trajectory data, i.e., K-means, Hierarchical clustering (Hier), and Self-organizing maps (SOM), are used to conduct clustering analysis of trajectory data, and the results are compared with those of the HYSPLIT model released by National Oceanic and Atmospheric Administration (NOAA). Principal Component Analysis (PCA) is used to analyze the original trajectory data. The internal evaluation indexes of Davies–Bouldin Index (DBI), Silhouette Coefficient (SC), Calinski Harabasz Index (CH), and I index are used to quantitatively evaluate the three clustering algorithms. The results show that there is little information in the height data, and thus only two-dimensional plane data are used for clustering. From the results of clustering indexes, the clustering results of SOM and K-means are better than the Hier and HYSPLIT model. In addition, it is found that DBI and I index can help to select the number of clusters, of which DBI is preferred for cluster analysis.

Aiming at the detection of atmospheric water vapor mixing ratio, depolarization ratio, backscatter coefficient, extinction coefficient and cloud information, the Water vapor, Cloud and Aerosol Lidar (WACAL) is developed by the lidar group at Ocean University of China. The lidar consists of transmitter, receiver, data acquisition and auxiliary system. For the measurement of various atmospheric physical properties, three channels including Raman channel, polarization channel and infrared channel are integrated in WACAL. The integration and working principle of these channels are introduced in details. The optical setup, the housekeeping of the system and the data retrieval routines are also presented. After the completion of the construction of the lidar, the WACAL system was installed in Ocean University of China (36.165°N, 120.5°E), Qingdao for the measurement of atmosphere during 2013 and 2014. The measurement principles and some case studies corresponding to various atmospheric physical properties are provided. Finally, the result of one continuous measurement example operated on 13 June 2014 is presented. The WACAL can measure the aerosol and cloud optical properties as well as the water vapor mixing ratio. It is useful for studying the direct and indirect effects of the aerosol on the climate change.

A measuring campaign was performed over the neighboring towns of Nova Gorica in Slovenia and Gorizia in Italy on 24 and 25 May 2010, to investigate the concentration and distribution of urban aerosols. Tracking of two-dimensional spatial and temporal aerosol distributions was performed using scanning elastic LIDAR, operating at 1064 nm. In addition, PM₁₀ concentrations of particles, NO_x concentrations and meteorological data were continuously monitored within the LIDAR scanning region. Based on the data we collected, we investigated the flow dynamics and the aerosol concentrations within the lower troposphere and found an evidence for daily aerosol cycles. We observed a number of cases with spatially localized increased LIDAR returns, which are associated with the presence of point sources of particulate matter. Daily aerosol concentration cycles were also clearly visible with a peak in aerosol concentration during the morning rush hours and daily plateau at around 17:00 Central European Time. We also found that horizontal atmospheric extinction at the height of 200 m, averaged in limited region with a radius of 300 m directly above the ground-based measuring site, was linearly correlated to the PM₁₀ concentration with a correlation coefficient of 0.84. When considering the average of the horizontal atmospheric extinction over the entire scanning region, a strong dependence on traffic conditions (concentration of NO_x) in the vicinity of the ground-based measuring site was observed.