58 citations as recorded by crossref.

1. On the Limits of CALIOP for Constraining Modeled Free Tropospheric Aerosol D. Watson‐Parris et al. https://doi.org/10.1029/2018GL078195
2. Assessment of tropospheric CALIPSO Version 4.2 aerosol types over the ocean using independent CALIPSO–SODA lidar ratios Z. Li et al. https://doi.org/10.5194/amt-15-2745-2022
3. Three-Dimensional Distribution of Arctic Aerosols Based on CALIOP Data Y. Sun & L. Chang https://doi.org/10.3390/rs17050903
4. PISTON and CAMP2Ex observations of the fundamental modes of aerosol vertical variability in the Northwest Tropical Pacific and Maritime Continent's Monsoon J. Reid et al. https://doi.org/10.5194/acp-25-18639-2025
5. Retrieving particulate matter concentrations over the contiguous United States using CALIOP observations T. Toth et al. https://doi.org/10.1016/j.atmosenv.2022.118979
6. How much of the global aerosol optical depth is found in the boundary layer and free troposphere? Q. Bourgeois et al. https://doi.org/10.5194/acp-18-7709-2018
7. Spaceborne lidar retrieved composite and speciated aerosol extinction profiles and optical depths over India: A decade of observations P. Kulkarni & V. Sreekanth https://doi.org/10.1016/j.apr.2020.02.007
8. Global estimation of clear-sky shortwave aerosol direct radiative effects based on CALIPSO observations Z. Shi et al. https://doi.org/10.1080/01431161.2022.2045042
9. Assessing the benefits of Imaging Infrared Radiometer observations for the CALIOP version 4 cloud and aerosol discrimination algorithm T. Vaillant de Guélis et al. https://doi.org/10.5194/amt-15-1931-2022
10. Annual cycle of aerosol properties over the central Arctic during MOSAiC 2019–2020 – light-extinction, CCN, and INP levels from the boundary layer to the tropopause A. Ansmann et al. https://doi.org/10.5194/acp-23-12821-2023
11. Characterization and application of artificial light sources for nighttime aerosol optical depth retrievals using the Visible Infrared Imager Radiometer Suite Day/Night Band J. Zhang et al. https://doi.org/10.5194/amt-12-3209-2019
12. Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record A. Sayer et al. https://doi.org/10.1029/2018JD029465
13. Optimizing MERRA-2 aerosol extinction coefficient by fusing CALIPSO and SAGE observations with machine learning J. Yang et al. https://doi.org/10.1016/j.atmosenv.2025.121619
14. EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product E. Proestakis et al. https://doi.org/10.5194/acp-19-11743-2019
15. A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust E. Proestakis et al. https://doi.org/10.5194/amt-17-3625-2024
16. CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design J. Tackett et al. https://doi.org/10.5194/amt-11-4129-2018
17. Retrieving instantaneous extinction of aerosol undetected by the CALIPSO layer detection algorithm F. Mao et al. https://doi.org/10.5194/acp-22-10589-2022
18. Aerosols in the central Arctic cryosphere: satellite and model integrated insights during Arctic spring and summer B. Swain et al. https://doi.org/10.5194/acp-24-5671-2024
19. Large inter-model spread in simulated aerosol load over Arctic sea ice B. Swain et al. https://doi.org/10.1038/s44407-026-00080-7
20. Well-Posed Global 3-D Retrieval of Background Aerosol Optical Properties From the DQ-1/ACDL L. Dai et al. https://doi.org/10.1109/TGRS.2026.3670839
21. The sensitivity of smoke aerosol dispersion to smoke injection height and source-strength: a multi-model AeroCom study X. Pan et al. https://doi.org/10.5194/acp-26-171-2026
22. Accurately detecting nocturnal cloud over land using next-generation geostationary satellite imagery: A case study using advanced Himawari imager data for Australia Y. Qin et al. https://doi.org/10.1016/j.rse.2025.114861
23. Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain K. Vinjamuri et al. https://doi.org/10.1016/j.envpol.2019.113377
24. Insights of aerosol-precipitation nexus in the central Arctic through CMIP6 climate models B. Swain et al. https://doi.org/10.1038/s41612-025-00957-6
25. Model simulations capture seasonal Arctic Haze and clean-air cycle better than satellite and reanalysis B. Swain et al. https://doi.org/10.1038/s41598-025-29188-8
26. All-sky direct aerosol radiative effects estimated from integrated A-Train satellite measurements M. Kacenelenbogen et al. https://doi.org/10.5194/acp-25-15875-2025
27. Analysis of Atmospheric Aerosol Optical Properties in the Northeast Brazilian Atmosphere with Remote Sensing Data from MODIS and CALIOP/CALIPSO Satellites, AERONET Photometers and a Ground-Based Lidar A. de Oliveira et al. https://doi.org/10.3390/atmos10100594
28. Identifying Seasonal and Diurnal Variations and the Most Frequently Impacted Zone of Aerosols in the Aral Sea Region Y. Ge et al. https://doi.org/10.3390/ijerph192114144
29. The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm M. Kim et al. https://doi.org/10.5194/amt-11-6107-2018
30. Seasonality biases arise from the interplay of retrieval quality and solar zenith angle effects in passive sensor AOD products S. Smith et al. https://doi.org/10.5194/acp-25-14333-2025
31. Status and future of numerical atmospheric aerosol prediction with a focus on data requirements A. Benedetti et al. https://doi.org/10.5194/acp-18-10615-2018
32. Quantifying dust deposition over the Atlantic Ocean E. Proestakis et al. https://doi.org/10.5194/essd-17-4351-2025
33. Global distribution of maritime low clouds with an emphasis on different aerosol types and meteorological parameters inferred from multi-satellite and reanalysis data during 2007–2016 B. Pan et al. https://doi.org/10.1016/j.atmosenv.2020.118082
34. A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection P. Selmer et al. https://doi.org/10.3390/rs16152735
35. Aerosol Direct Radiative Effects under Cloud-Free Conditions over Highly-Polluted Areas in Europe and Mediterranean: A Ten-Years Analysis (2007–2016) T. Landi et al. https://doi.org/10.3390/rs13152933
36. Widespread biomass burning smoke throughout the remote troposphere G. Schill et al. https://doi.org/10.1038/s41561-020-0586-1
37. Total column optical depths retrieved from CALIPSO lidar ocean surface backscatter R. Ryan et al. https://doi.org/10.5194/amt-17-6517-2024
38. Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations B. Swain et al. https://doi.org/10.5194/amt-17-359-2024
39. A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations T. Toth et al. https://doi.org/10.5194/amt-12-1739-2019
40. Mapping 532 nm lidar ratios for CALIPSO-classified marine aerosols using MODIS AOD constrained retrievals and GOCART model simulations T. Toth et al. https://doi.org/10.5194/amt-18-6765-2025
41. High-Resolution Detection of Stratospheric Aerosols in CALIPSO Atmospheric Lidar Data Facilitated by the CALIOP-Density-Dimension Algorithm U. Herzfeld et al. https://doi.org/10.1109/TGRS.2025.3569546
42. Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveals improved agreement with observations A. Wells et al. https://doi.org/10.5194/acp-23-3985-2023
43. Detecting layer height of smoke aerosols over vegetated land and water surfaces via oxygen absorption bands: hourly results from EPIC/DSCOVR in deep space X. Xu et al. https://doi.org/10.5194/amt-12-3269-2019
44. Vertical distribution of aerosols and clouds over north-eastern South Asia: Aerosol-cloud interactions P. Dahutia et al. https://doi.org/10.1016/j.atmosenv.2019.116882
45. A Deep Learning Approach to Lidar Signal Denoising and Atmospheric Feature Detection J. Gomes et al. https://doi.org/10.3390/rs17244060
46. A 13-year space-based climatological evolution of desert dust aerosols in the Middle East and North Africa regions observed by the CALIPSO S. Berhane et al. https://doi.org/10.1016/j.scitotenv.2024.174793
47. A global analysis of diurnal variability in dust and dust mixture using CATS observations Y. Yu et al. https://doi.org/10.5194/acp-21-1427-2021
48. The day-to-day co-variability between mineral dust and cloud glaciation: a proxy for heterogeneous freezing D. Villanueva et al. https://doi.org/10.5194/acp-20-2177-2020
49. Novel aerosol extinction coefficients and lidar ratios over the ocean from CALIPSO–CloudSat: evaluation and global statistics D. Painemal et al. https://doi.org/10.5194/amt-12-2201-2019
50. Arctic spring and summertime aerosol optical depth baseline from long-term observations and model reanalyses – Part 1: Climatology and trend P. Xian et al. https://doi.org/10.5194/acp-22-9915-2022
51. An observational estimate of Arctic UV-absorbing aerosol direct radiative forcing on instantaneous and climatic scales B. Sorenson et al. https://doi.org/10.5194/acp-25-11867-2025
52. Upper troposphere dust belt formation processes vary seasonally and spatially in the Northern Hemisphere K. Yang et al. https://doi.org/10.1038/s43247-022-00353-5
53. Layer detection algorithm for CALIPSO observation based on automatic segmentation with a minimum cost function F. Mao et al. https://doi.org/10.1016/j.jqsrt.2020.107498
54. Investigation of CATS aerosol products and application toward global diurnal variation of aerosols L. Lee et al. https://doi.org/10.5194/acp-19-12687-2019
55. Ozone Monitoring Instrument (OMI) UV aerosol index data analysis over the Arctic region for future data assimilation and climate forcing applications B. Sorenson et al. https://doi.org/10.5194/acp-23-7161-2023
56. Spatiotemporal distribution of dust aerosol optical properties from CALIPSO and CATS observations in Xinjiang, China G. Ren et al. https://doi.org/10.1016/j.jastp.2023.106006
57. Sensitivity studies of nighttime top-of-atmosphere radiances from artificial light sources using a 3-D radiative transfer model for nighttime aerosol retrievals J. Zhang et al. https://doi.org/10.5194/amt-16-2531-2023
58. Estimations of global shortwave direct aerosol radiative effects above opaque water clouds using a combination of A-Train satellite sensors M. Kacenelenbogen et al. https://doi.org/10.5194/acp-19-4933-2019