The characterization of Taklamakan dust properties using a multi-wavelength Raman polarization lidar in Kashi, China

Abstract. The Taklamakan desert is an important dust source for the global atmospheric dust budget and a cause of the dust weather in Eastern Asia. The characterization of the properties and vertical distributions of Taklamakan dust in the source region is still very limited. To fill this gap, the DAO (Dust Aerosol Observation) was conducted in Kashi, China in 2019. Kashi site is about 150 km to the west rim of the Taklamakan desert and is strongly impacted by desert dust aerosols, especially in spring time, i.e. April and May. Apart from dust, fine particles coming from local anthropogenic emissions or/and transported aerosols are also a non-negligible aerosol component. In this study, we provide the first profiling of the 2α + 3β + 3δ lidar profiles of Taklamakan dust based on a multi-wavelength Raman polarization lidar. Four cases, including two Taklamakan dust events (Case 1 and 2) and two polluted dust events (Case 3 and 4) are presented. The lidar ratio in the Taklamakan dust outbreak is found to be 51 ± 8–56 ± 8 sr at 355 nm and 45 ± 7 sr at 532 nm. The particle linear depolarization ratios are about 0.28 ± 0.04–0.32 ± 0.05 at 355 nm, 0.35 ± 0.05 at 532 nm and 0.31 ± 0.05 at 1064 nm. The observed polluted dust is commonly featured with reduced particle linear depolarization ratio and enhanced extinction and backscatter Angstrom exponent. In Case 3, the lidar ratio of polluted dust is about 42 ± 6 sr at 355 nm and 40 ± 6 sr at 532 nm. The particles linear depolarization ratios decrease to about 0.25, with a weak spectral dependence. In Case 4, the variability of lidar ratio and particle linear depolarization ratio is higher than in Case 3, which reflects the complexity of the nature of mixed pollutant and the mixing state. The results provide the first reference for the characteristics of Taklamakan dust measured by Raman lidar. The data could contribute to complementing the dust model and improving the accuracy of climate modeling.