81 citations as recorded by crossref.

1. POLIPHON conversion factors for retrieving dust-related cloud condensation nuclei and ice-nucleating particle concentration profiles at oceanic sites Y. He et al. 10.5194/amt-16-1951-2023
2. Retrieved XCO2 Accuracy Improvement by Reducing Aerosol-Induced Bias for China’s Future High-Precision Greenhouse Gases Monitoring Satellite Mission J. Ke et al. 10.3390/atmos13091384
3. Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations H. Andersen et al. 10.5194/acp-23-10775-2023
4. A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations F. Alexandri et al. 10.5194/amt-17-1739-2024
5. Assessment of simulated aerosol effective radiative forcings in the terrestrial spectrum I. Heyn et al. 10.1002/2016GL071975
6. Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning J. Fuchs et al. 10.5194/acp-18-16537-2018
7. Opportunistic experiments to constrain aerosol effective radiative forcing M. Christensen et al. 10.5194/acp-22-641-2022
8. SPEX airborne spectropolarimeter calibration and performance J. Smit et al. 10.1364/AO.58.005695
9. Constraining the instantaneous aerosol influence on cloud albedo E. Gryspeerdt et al. 10.1073/pnas.1617765114
10. Under What Conditions Can We Trust Retrieved Cloud Drop Concentrations in Broken Marine Stratocumulus? Y. Zhu et al. 10.1029/2017JD028083
11. Distinct impacts on precipitation by aerosol radiative effect over three different megacity regions of eastern China Y. Sun & C. Zhao 10.5194/acp-21-16555-2021
12. Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds D. Rosenfeld et al. 10.1126/science.aav0566
13. Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity S. Burrows et al. 10.1007/s00376-018-7300-x
14. Bibliometric Analysis of Aerosol-Radiation Research from 1999 to 2023 S. Wang & B. Yi 10.3390/atmos15101189
15. Satellite Retrieval of Cloud Condensation Nuclei Concentrations in Marine Stratocumulus by Using Clouds as CCN Chambers A. Efraim et al. 10.1029/2020JD032409
16. The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness M. Salzmann et al. 10.1029/2021MS002699
17. Variability in the correlation between satellite-derived liquid cloud droplet effective radius and aerosol index over the northern Pacific Ocean S. Tan et al. 10.1080/16000889.2017.1391656
18. The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2 U. Lohmann & D. Neubauer 10.5194/acp-18-8807-2018
19. Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface T. Su et al. 10.1126/sciadv.adl5044
20. Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods H. Jia et al. 10.1038/s41467-021-23888-1
21. A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing J. Redemann & L. Gao 10.1038/s41467-024-52747-y
22. Bounding Global Aerosol Radiative Forcing of Climate Change N. Bellouin et al. 10.1029/2019RG000660
23. Investigating the sign of stratocumulus adjustments to aerosols in the ICON global storm-resolving model E. Fons et al. 10.5194/acp-24-8653-2024
24. Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations D. Painemal et al. 10.5194/acp-20-7167-2020
25. Automated Mapping of Convective Clouds (AMCC) Thermodynamical, Microphysical, and CCN Properties from SNPP/VIIRS Satellite Data Z. Yue et al. 10.1175/JAMC-D-18-0144.1
26. Quantifying cloud adjustments and the radiative forcing due to aerosol–cloud interactions in satellite observations of warm marine clouds A. Douglas & T. L'Ecuyer 10.5194/acp-20-6225-2020
27. How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth A. Sayer & K. Knobelspiesse 10.5194/acp-19-15023-2019
28. Is positive correlation between cloud droplet effective radius and aerosol optical depth over land due to retrieval artifacts or real physical processes? H. Jia et al. 10.5194/acp-19-8879-2019
29. Passive remote sensing of aerosol layer height using near‐UV multiangle polarization measurements L. Wu et al. 10.1002/2016GL069848
30. Sensitivity of cloud microphysics to aerosol is highly associated with cloud water content: Implications for indirect radiative forcing Y. Wang et al. 10.1016/j.atmosres.2024.107552
31. Cloud water adjustments to aerosol perturbations are buffered by solar heating in non-precipitating marine stratocumuli J. Zhang et al. 10.5194/acp-24-10425-2024
32. Unveiling aerosol–cloud interactions – Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data D. Neubauer et al. 10.5194/acp-17-13165-2017
33. Representation Uncertainty in the Earth Sciences C. Bulgin et al. 10.1029/2021EA002129
34. Observational Quantification of Aerosol Invigoration for Deep Convective Cloud Lifecycle Properties Based on Geostationary Satellite Z. Pan et al. 10.1029/2020JD034275
35. Strong Precipitation Suppression by Aerosols in Marine Low Clouds C. Fan et al. 10.1029/2019GL086207
36. Aerosol measurements by SPEXone on the NASA PACE mission: expected retrieval capabilities O. Hasekamp et al. 10.1016/j.jqsrt.2019.02.006
37. Impact of isolated atmospheric aging processes on the cloud condensation nuclei activation of soot particles F. Friebel et al. 10.5194/acp-19-15545-2019
38. Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation X. Zhao et al. 10.3390/rs16132487
39. Constraining Uncertainty in Aerosol Direct Forcing D. Watson‐Parris et al. 10.1029/2020GL087141
40. Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation M. Bonazzola et al. 10.5194/gmd-16-1359-2023
41. Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks H. Andersen et al. 10.5194/acp-17-9535-2017
42. Analyzing Sensitive Aerosol Regimes and Active Geolocations of Aerosol Effects on Deep Convective Clouds over the Global Oceans by Using Long-Term Operational Satellite Observations X. Zhao & M. Foster 10.3390/cli10110167
43. Use of lidar aerosol extinction and backscatter coefficients to estimate cloud condensation nuclei (CCN) concentrations in the southeast Atlantic E. Lenhardt et al. 10.5194/amt-16-2037-2023
44. Climatology Perspective of Sensitive Regimes and Active Regions of Aerosol Indirect Effect for Cirrus Clouds over the Global Oceans X. Zhao et al. 10.3390/rs12050823
45. Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades Y. Cao et al. 10.1029/2022JD037417
46. Quantifying variations in shortwave aerosol–cloud–radiation interactions using local meteorology and cloud state constraints A. Douglas & T. L'Ecuyer 10.5194/acp-19-6251-2019
47. A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations P. Patel et al. 10.5194/acp-24-2861-2024
48. Opinion: A critical evaluation of the evidence for aerosol invigoration of deep convection A. Varble et al. 10.5194/acp-23-13791-2023
49. Aerosol-induced modification of organised convection and top-of-atmosphere radiation N. Nishant et al. 10.1038/s41612-019-0089-1
50. Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model U. Proske et al. 10.5194/acp-24-5907-2024
51. Constraining the Twomey effect from satellite observations: issues and perspectives J. Quaas et al. 10.5194/acp-20-15079-2020
52. Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 2: Controls on the ice crystal number concentration E. Gryspeerdt et al. 10.5194/acp-18-14351-2018
53. Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations H. Jia et al. 10.5194/acp-22-7353-2022
54. A First Case Study of CCN Concentrations from Spaceborne Lidar Observations A. Georgoulias et al. 10.3390/rs12101557
55. Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements G. Choudhury & M. Tesche 10.3390/rs14143342
56. Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals A. Varble et al. 10.5194/acp-23-13523-2023
57. Cloud condensation nuclei concentrations derived from the CAMS reanalysis K. Block et al. 10.5194/essd-16-443-2024
58. Surprising similarities in model and observational aerosol radiative forcing estimates E. Gryspeerdt et al. 10.5194/acp-20-613-2020
59. Decomposing the effective radiative forcing of anthropogenic aerosols based on CMIP6 Earth system models A. Kalisoras et al. 10.5194/acp-24-7837-2024
60. Spatial Heterogeneity of Aerosol Effect on Liquid Cloud Microphysical Properties in the Warm Season Over Tibetan Plateau P. Zhao et al. 10.1029/2022JD037738
61. The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing, and climate sensitivity D. Neubauer et al. 10.5194/gmd-12-3609-2019
62. Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations P. Ma et al. 10.1038/s41467-018-05028-4
63. Retrieval and validation of cloud condensation nuclei from satellite and airborne measurements over the Indian Monsoon region A. Aravindhavel et al. 10.1016/j.atmosres.2023.106802
64. Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions H. Jia & J. Quaas 10.1038/s41558-023-01775-5
65. Relationship between cloud condensation nuclei (CCN) concentration and aerosol optical depth in the Arctic region S. Ahn et al. 10.1016/j.atmosenv.2021.118748
66. Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. 10.1029/2022RG000796
67. Development of ZJU high-spectral-resolution lidar for aerosol and cloud: Feature detection and classification N. Wang et al. 10.1016/j.jqsrt.2021.107513
68. Observing short-timescale cloud development to constrain aerosol–cloud interactions E. Gryspeerdt et al. 10.5194/acp-22-11727-2022
69. A Case Study in Low Aerosol Number Concentrations Over the Eastern North Atlantic: Implications for Pristine Conditions in the Remote Marine Boundary Layer S. Pennypacker & R. Wood 10.1002/2017JD027493
70. Opposite Aerosol Index‐Cloud Droplet Effective Radius Correlations Over Major Industrial Regions and Their Adjacent Oceans X. Ma et al. 10.1029/2018GL077562
71. Validation of satellite-retrieved CCN based on a cruise campaign over the polluted Northwestern Pacific ocean Y. Wang et al. 10.1016/j.atmosres.2021.105722
72. Spaceborne Lidar in the Study of Marine Systems C. Hostetler et al. 10.1146/annurev-marine-121916-063335
73. Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. 10.1029/2022RG000799
74. The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing J. Johnson et al. 10.5194/acp-18-13031-2018
75. Effects of auxiliary atmospheric state parameters on the aerosol optical properties retrieval errors of high-spectral-resolution lidar Y. Zhang et al. 10.1364/AO.57.002627
76. Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements G. Choudhury & M. Tesche 10.5194/amt-15-639-2022
77. Impact of Saharan dust on North Atlantic marine stratocumulus clouds: importance of the semidirect effect A. Amiri-Farahani et al. 10.5194/acp-17-6305-2017
78. Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors H. Dadashazar et al. 10.5194/acp-21-10499-2021
79. Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. 10.16993/tellusb.1868
80. Potential Modulation of Aerosol on Precipitation Efficiency in Southwest China P. Zhao et al. 10.3390/rs16081445
81. Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability S. Ghan et al. 10.1073/pnas.1514036113