59 citations as recorded by crossref.

1. Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm S. Lolli et al. https://doi.org/10.3390/rs12010071
2. Observations of Saharan Dust Intrusions over Potenza, Southern Italy, During 13 Years of Lidar Measurements: Seasonal Variability of Optical Properties and Radiative Impact B. De Rosa et al. https://doi.org/10.3390/rs17030453
3. Three-wavelength polarization Scheimpflug lidar system developed for remote sensing of atmospheric aerosols Z. Kong et al. https://doi.org/10.1364/AO.58.008612
4. Optical properties of Central Asian aerosol relevant for spaceborne lidar applications and aerosol typing at 355 and 532 nm J. Hofer et al. https://doi.org/10.5194/acp-20-9265-2020
5. Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products Y. Wang et al. https://doi.org/10.3390/rs14102342
6. Combining Mie–Raman and fluorescence observations: a step forward in aerosol classification with lidar technology I. Veselovskii et al. https://doi.org/10.5194/amt-15-4881-2022
7. Does the Intra-Arctic Modification of Long-Range Transported Aerosol Affect the Local Radiative Budget? (A Case Study) K. Nakoudi et al. https://doi.org/10.3390/rs12132112
8. Combined Raman Lidar and Ka-Band Radar Aerosol Observations P. Gumà-Claramunt et al. https://doi.org/10.3390/rs17152662
9. Combined use of Mie–Raman and fluorescence lidar observations for improving aerosol characterization: feasibility experiment I. Veselovskii et al. https://doi.org/10.5194/amt-13-6691-2020
10. Global classification of aerosols based on ground observations: A comparison between an empirical method and machine learning algorithms M. Awais & L. Wang https://doi.org/10.1016/j.pce.2025.104017
11. Smoke of extreme Australian bushfires observed in the stratosphere over Punta Arenas, Chile, in January 2020: optical thickness, lidar ratios, and depolarization ratios at 355 and 532 nm K. Ohneiser et al. https://doi.org/10.5194/acp-20-8003-2020
12. Unveiling Atmospheric Layers: Vertical Pollution Patterns and Prospects for High-Resolution Aerosol Retrievals Using the Eastern Mediterranean as a Case Study I. Rogozovsky et al. https://doi.org/10.1021/acs.est.4c14556
13. PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy S. Pignatti et al. https://doi.org/10.3390/rs14091985
14. Exceptional wildfire smoke over Greece in summer 2023: a synergistic study of aerosol optical-microphysical and UVB radiative impacts M. Gidarakou et al. https://doi.org/10.5194/acp-26-4313-2026
15. Aerosol type classification with machine learning techniques applied to multiwavelength lidar data from EARLINET A. del Águila et al. https://doi.org/10.5194/acp-25-12549-2025
16. Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations M. Mylonaki et al. https://doi.org/10.5194/acp-21-2211-2021
17. Classification and source analysis of low-altitude aerosols in Beijing using fluorescence–Mie polarization lidar Y. Zhang et al. https://doi.org/10.1016/j.optcom.2020.126417
18. Multiwavelength fluorescence lidar observations of smoke plumes I. Veselovskii et al. https://doi.org/10.5194/amt-16-2055-2023
19. Radiative impacts of the Australian bushfires 2019–2020 – Part 2: Large-scale and in-vortex radiative heating P. Sellitto et al. https://doi.org/10.5194/acp-23-15523-2023
20. Multiyear Typology of Long-Range Transported Aerosols over Europe V. Nicolae et al. https://doi.org/10.3390/atmos10090482
21. Comparison of dust optical depth from multi-sensor products and MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) dust reanalysis over North Africa, the Middle East, and Europe M. Mytilinaios et al. https://doi.org/10.5194/acp-23-5487-2023
22. Optical Properties and Radiative Forcing Estimations of High-Altitude Aerosol Transport During Saharan Dust Events Based on Laser Remote Sensing Techniques (CLIMPACT Campaign 2021, Greece) A. Papayannis et al. https://doi.org/10.3390/rs17213607
23. HETEAC – the Hybrid End-To-End Aerosol Classification model for EarthCARE U. Wandinger et al. https://doi.org/10.5194/amt-16-2485-2023
24. A method of aerosol types classification combining MODIS data and Mie-scattering lidar detection data Z. Yang et al. https://doi.org/10.1016/j.optcom.2025.132694
25. Aerosol types classification in coastal areas of China using sun photometer and Mie-scattering lidar simultaneous detection data Z. Yang et al. https://doi.org/10.1016/j.jqsrt.2026.109976
26. Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering N. Siomos et al. https://doi.org/10.3390/rs12060965
27. Classification of atmospheric aerosols and clouds by use of dual-polarization lidar measurements S. Qi et al. https://doi.org/10.1364/OE.430456
28. Observation and Classification of Low-Altitude Haze Aerosols Using Fluorescence–Raman–Mie Polarization Lidar in Beijing during Spring 2024 Y. Jiang et al. https://doi.org/10.3390/rs16173225
29. Potential of Polarization Lidar to Profile the Urban Aerosol Phase State during Haze Episodes W. Tan et al. https://doi.org/10.1021/acs.estlett.9b00695
30. Aerosol Typing Based on Multiwavelength Lidar Observations and Meteorological Model Data M. Mylonaki et al. https://doi.org/10.1051/epjconf/202023708003
31. An EARLINET early warning system for atmospheric aerosol aviation hazards N. Papagiannopoulos et al. https://doi.org/10.5194/acp-20-10775-2020
32. Evaluation of Aerosol Typing with Combination of Remote Sensing Techniques with In Situ Data during the PANACEA Campaigns in Thessaloniki Station, Greece K. Voudouri et al. https://doi.org/10.3390/rs14205076
33. Global aerosol models considering their spatial heterogeneities based on AERONET measurements S. Wang et al. https://doi.org/10.1016/j.atmosres.2024.107521
34. Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign A. Tsekeri et al. https://doi.org/10.5194/amt-16-6025-2023
35. Deriving Aerosol Absorption Properties from Solar Ultraviolet Radiation Spectral Measurements at Thessaloniki, Greece I. Fountoulakis et al. https://doi.org/10.3390/rs11182179
36. Numerical investigation on the accuracy of size information of fractal soot aerosols retrieved by lidar: Optical property, morphology effect, and parameterization scheme J. Liu et al. https://doi.org/10.1016/j.jqsrt.2022.108435
37. Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station K. Voudouri et al. https://doi.org/10.3390/rs12233969
38. 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. https://doi.org/10.5194/amt-16-2353-2023
39. Examining the characteristics of aerosols: a statistical analysis based on a decade of lidar and photometer observations at the Eastern border of ACTRIS D. Nicolae et al. https://doi.org/10.5194/amt-19-1179-2026
40. FLARE-GMM: an automatic aerosol typing model based on Mie–Raman–fluorescence lidar measurements with LILAS R. Miri et al. https://doi.org/10.5194/amt-18-5729-2025
41. Atmospheric Boundary Layer Height Estimation from Lidar Observations: Assessment and Validation of MIPA Algorithm G. D’Amico et al. https://doi.org/10.3390/rs17162748
42. Extreme wildfires over northern Greece during summer 2023 – Part A: Effects on aerosol optical properties and solar UV radiation K. Michailidis et al. https://doi.org/10.1016/j.atmosres.2024.107700
43. Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean E. Marinou et al. https://doi.org/10.3390/rs13245001
44. Long-term variation in aerosol lidar ratio in Shanghai based on Raman lidar measurements T. Liu et al. https://doi.org/10.5194/acp-21-5377-2021
45. Understanding Aerosol–Cloud Interactions through Lidar Techniques: A Review F. Cairo et al. https://doi.org/10.3390/rs16152788
46. Aerosol particle depolarization ratio at 1565 nm measured with a Halo Doppler lidar V. Vakkari et al. https://doi.org/10.5194/acp-21-5807-2021
47. Advancing global aerosol classification: Source-compositional typology and variability from 30 years (1993–2024) of AERONET observations A. Krishnaveni et al. https://doi.org/10.1016/j.scitotenv.2025.180918
48. Characterization of forest fire and Saharan desert dust aerosols over south-western Europe using a multi-wavelength Raman lidar and Sun-photometer V. Salgueiro et al. https://doi.org/10.1016/j.atmosenv.2021.118346
49. CIAO main upgrade: building up an ACTRIS-compliant aerosol in situ laboratory T. Laurita et al. https://doi.org/10.5194/amt-18-2373-2025
50. Optical and Microphysical Properties of the Aerosols during a Rare Event of Biomass-Burning Mixed with Polluted Dust M. Gidarakou et al. https://doi.org/10.3390/atmos15020190
51. Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations I. Veselovskii et al. https://doi.org/10.5194/amt-17-4137-2024
52. Characterization of Extremely Fresh Biomass Burning Aerosol by Means of Lidar Observations B. De Rosa et al. https://doi.org/10.3390/rs14194984
53. TiARA (Version 2.1): Simulations of Particle Microphysical Parameters Retrievals Based on MERRA-2 Synthetic Organic Carbon–Dust Mixtures in the Context of Multiwavelength Lidar Data A. Kolgotin et al. https://doi.org/10.3390/rs18040658
54. Development of an Automatic Polarization Raman LiDAR for Aerosol Monitoring over Complex Terrain L. Wang et al. https://doi.org/10.3390/s19143186
55. Optical and geometrical aerosol particle properties over the United Arab Emirates M. Filioglou et al. https://doi.org/10.5194/acp-20-8909-2020
56. Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. https://doi.org/10.1029/2022RG000796
57. Interrelations between surface, boundary layer, and columnar aerosol properties derived in summer and early autumn over a continental urban site in Warsaw, Poland D. Wang et al. https://doi.org/10.5194/acp-19-13097-2019
58. Comparison of two automated aerosol typing methods and their application to an EARLINET station K. Voudouri et al. https://doi.org/10.5194/acp-19-10961-2019
59. HETEAC-Flex: an optimal estimation method for aerosol typing based on lidar-derived intensive optical properties A. Floutsi et al. https://doi.org/10.5194/amt-17-693-2024