The vertical structure of dust properties in desert sources is crucial for evaluating their long-range transportation and radiative forcing. To investigate vertical profiles of dust optical properties in the Taklimakan Desert, we conducted ground-based polarization Raman lidar measurements in Tazhong (83.39°E, 38.58°N, 1103 m above sea level), located at the center of the Taklimakan Desert in the summer of 2019. The lidar system developed by Lanzhou University for continuous network observation is capable of measuring polarization at 532 and 355 nm and detecting Raman signals at 387, 407, and 607 nm. The results indicate that dust aerosols in the central Taklimakan Desert were regularly lifted over 6 km during the summer with a mass concentration of 400–1000 µg m−3, while the majority of the dust remained restricted within 2 km. Moreover, the height of the boundary layer can reach 5–6 km in the afternoon under the strong convention. Above 3 km, dust is composed of finer particles with an effective radius (Reff.) less than 3 μm and a Ångström exponent (AE) related to the extinction coefficient (AEE)532,355 greater than 4; below 3 km, however, dust is dominated by coarser particles. In addition, the particle depolarization ratios (PDR) of Taklimakan dust are 0.32 ± 0.06 at 532 nm and 0.27 ± 0.04 at 355 nm, while the lidar ratios (LRs) are 49 ± 19 sr at 532 nm and 43 ± 12 sr at 355 nm. This study firstly provides information on dust vertical structure and its optical properties in the center of the desert, which may aid in further evaluating their associated impacts on the climate and ecosystem.

Polarization measurements have been widely used to detect aerosol properties by remote sensing in recent decades. To better understand the polarization characteristics of aerosols by lidar, the numerically exact T-matrix method was used to simulate the depolarization ratio (DR) of dust and smoke aerosols at typical laser wavelengths in this study. The results show that the DRs of dust and smoke aerosols have obviously different spectral dependences. Moreover, the ratio of DRs at two wavelengths has an obvious linear relationship with the microphysical properties of aerosols, including aspect ratio, effective radius and complex refractive index. At short wavelengths, we can use it to invert the absorption characteristics of particles, further improving the detection ability of lidar. Comparing the simulation results of different channels, DR, (color ratio) CR and (lidar ratio) LR have a good logarithmic fitting relationship at 532 nm and 1064 nm, which helps to classify the aerosol types. On this basis, a new inversion algorithm, “1β+1α+2δ”, was presented. By this algorithm, the backscattering coefficient (β), extinction coefficient (α), DR (δ) at 532 nm and 1064 nm can be used to expand the range of inversion and compare lidar data with different configurations to obtain more extensive optical characteristics of aerosols. Our study enhances the application of laser remote sensing in aerosol observations more accurately.

Polarization measurements have been widely used to detect aerosol properties by remote sensing in recent decades. To better understand the polarization characteristics of aerosols by lidar, the numerically exact T-matrix method was used to simulate the depolarization ratio (DR) of dust and smoke aerosols at typical laser wavelengths in this study. The results show that the DRs of dust and smoke aerosols have obviously different spectral dependences. Moreover, the ratio of DRs at two wavelengths has an obvious linear relationship with the microphysical properties of aerosols, including aspect ratio, effective radius and complex refractive index. At short wavelengths, we can use it to invert the absorption characteristics of particles, further improving the detection ability of lidar. Comparing the simulation results of different channels, DR, (color ratio) CR and (lidar ratio) LR have a good logarithmic fitting relationship at 532 nm and 1064 nm, which helps to classify the aerosol types. On this basis, a new inversion algorithm, “1β+1α+2δ”, was presented. By this algorithm, the backscattering coefficient (β), extinction coefficient (α), DR (δ) at 532 nm and 1064 nm can be used to expand the range of inversion and compare lidar data with different configurations to obtain more extensive optical characteristics of aerosols. Our study enhances the application of laser remote sensing in aerosol observations more accurately.

Polarization measurements have been widely used to detect aerosol properties by remote sensing in recent decades. To better understand the polarization characteristics of aerosols by lidar, the numerically exact T-matrix method was used to simulate the depolarization ratio (DR) of dust and smoke aerosols at typical laser wavelengths in this study. The results show that the DRs of dust and smoke aerosols have obviously different spectral dependences. Moreover, the ratio of DRs at two wavelengths has an obvious linear relationship with the microphysical properties of aerosols, including aspect ratio, effective radius and complex refractive index. At short wavelengths, we can use it to invert the absorption characteristics of particles, further improving the detection ability of lidar. Comparing the simulation results of different channels, DR, (color ratio) CR and (lidar ratio) LR have a good logarithmic fitting relationship at 532 nm and 1064 nm, which helps to classify the aerosol types. On this basis, a new inversion algorithm, “1β+1α+2δ”, was presented. By this algorithm, the backscattering coefficient (β), extinction coefficient (α), DR (δ) at 532 nm and 1064 nm can be used to expand the range of inversion and compare lidar data with different configurations to obtain more extensive optical characteristics of aerosols. Our study enhances the application of laser remote sensing in aerosol observations more accurately.

Understanding the vertical structures of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) number concentrations in desert source regions is crucial for examining dust-cloud interactions and other related impacts. To explore the vertical profiles of the CCN and INP number concentrations and their possible atmospheric–dynamic influence factors at the center of the Taklimakan Desert, intensive observations were conducted by employing a ground-based polarization Raman lidar, sounding balloons, and a sun photometer in Tazhong (83.39°E, 38.58°N, 1103 m above sea level) during the summer of 2019. Based on the GRASP algorithm, the extinction-to-volume conversion factor of dust aerosols was 0.85 × 10−12 Mmm3m−3, and the extinction-to-number conversion factor was predicted to be 0.20 Mmcm−3 on the basis of the sun photometer observations. Thus, the vertical CCN and INP number concentration profiles obtained with different parameterization schemes in the presence of various pollution levels were calculated by combining dust extinction coefficients retrieved by lidar and meteorological data observed by sounding balloon observations. The achieved results indicated that the CCN number concentration varied from 10−2 to 102 cm−3 and decreased from ground level to 12 km with an average value of 36.57 cm−3 at the 10–12 km height range, while the INP number concentration based on parameterization schemes D10 and D15 mainly varied from 10−1 to 102 L−1 and from 1 L−1 to 103 L−1, with average values of 3.50 L−1 and 7.80 L−1, respectively. Moreover, we observed a strong relationship between the INP number concentration of scheme D10 and the wind speed, with an R2 value of 0.72, but a weak relationship between the CCN number concentration and the relative humidity in the boundary layer, with a Spearman’s rank correlation coefficient R2 value of 0.38. The present study provides original and valuable information regarding the CCN and INP number concentrations and their related influencing factors at the center of the Taklimakan Desert and can improve our understanding of the vertical distributions of dust–cloud–atmosphere dynamic interactions, as well as of the roles of dust aerosols in the desert hydrological cycle.