11 citations as recorded by crossref.

1. Stratospheric Smoke Properties Based on Lidar Observations in Autumn 2017 Over Warsaw D. Wang et al. 10.1051/epjconf/202023702033
2. Assessing Lidar Ratio Impact on CALIPSO Retrievals Utilized for the Estimation of Aerosol SW Radiative Effects across North Africa, the Middle East, and Europe A. Moustaka et al. 10.3390/rs16101689
3. Preliminary results of water cloud and aerosol properties in the Yinchuan area using a Multi-wavelength lidar based on dual field of view J. Mao et al. 10.1016/j.optlastec.2021.107784
4. Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model M. Costa-Surós et al. 10.5194/acp-20-5657-2020
5. DeLiAn – a growing collection of depolarization ratio, lidar ratio and Ångström exponent for different aerosol types and mixtures from ground-based lidar observations A. Floutsi et al. 10.5194/amt-16-2353-2023
6. Discussion of the spectral slope of the lidar ratio between 355 and 1064 nm from multiwavelength Raman lidar observations M. Haarig et al. 10.5194/acp-25-7741-2025
7. The unprecedented 2017–2018 stratospheric smoke event: decay phase and aerosol properties observed with the EARLINET H. Baars et al. 10.5194/acp-19-15183-2019
8. Experimental assessment of a micro-pulse lidar system in comparison with reference lidar measurements for aerosol optical properties retrieval C. Córdoba-Jabonero et al. 10.5194/amt-14-5225-2021
9. Development and evaluation of an online monitoring single-particle optical particle counter with polarization detection W. Yao et al. 10.1016/j.jes.2023.04.010
10. Invisible aerosol layers: improved lidar detection capabilities by means of laser-induced aerosol fluorescence B. Gast et al. 10.5194/acp-25-3995-2025
11. Leveraging Deep Learning as a New Approach to Layer Detection and Cloud–Aerosol Classification Using ICESat-2 Atmospheric Data B. Oladipo et al. 10.3390/rs16132344