16 citations as recorded by crossref.

1. Pristine oceans are a significant source of uncertainty in quantifying global cloud condensation nuclei G. Choudhury et al. https://doi.org/10.5194/acp-25-3841-2025
2. Modelled surface climate response to effusive Icelandic volcanic eruptions: sensitivity to season and size T. Zoëga et al. https://doi.org/10.5194/acp-25-2989-2025
3. Estimation of Cloud Condensation Nuclei (CCN) using a neural network retrieval algorithm—A synthetic study for SPEXone on the NASA PACE mission N. Hannadige et al. https://doi.org/10.1016/j.jqsrt.2026.109853
4. On the sensitivity of aerosol–cloud interactions to changes in sea surface temperature in radiative–convective equilibrium S. Lorian & G. Dagan https://doi.org/10.5194/acp-24-9323-2024
5. The sensitivity of Southern Ocean atmospheric dimethyl sulfide (DMS) to modeled oceanic DMS concentrations and emissions Y. Bhatti et al. https://doi.org/10.5194/acp-23-15181-2023
6. Composite sharpening by vortex symmetrization and normalization of tropical cyclones A. Caratsch et al. https://doi.org/10.5194/gmd-19-5041-2026
7. Image-based observation of aerosol hygroscopic growth and extinction coefficient reversal at three wavelengths, supported by Köhler theory simulations J. Sim et al. https://doi.org/10.1039/D5EM00826C
8. A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations F. Alexandri et al. https://doi.org/10.5194/amt-17-1739-2024
9. CCN Retrievals from Spaceborne Lidar Observations During ACEMED: Sensitivity to Smoke Parameterization A. Georgoulias et al. https://doi.org/10.3390/rs18040586
10. Simulations of the impact of cloud condensation nuclei and ice-nucleating particles perturbations on the microphysics and radar reflectivity factor of stratiform mixed-phase clouds J. Lee et al. https://doi.org/10.5194/acp-24-5737-2024
11. Contrasting aerosol–cloud feedbacks across a warming Arctic S. Chua et al. https://doi.org/10.1038/s41561-026-02114-x
12. Understanding Aerosol–Cloud Interactions through Lidar Techniques: A Review F. Cairo et al. https://doi.org/10.3390/rs16152788
13. Machine learning-based retrieval of aerosol size and hygroscopicity using horizontal scanning LiDAR and PM data J. Shin et al. https://doi.org/10.1038/s41612-025-01276-6
14. Radiative forcing from aerosol–cloud interactions enhanced by large-scale circulation adjustments G. Dagan et al. https://doi.org/10.1038/s41561-023-01319-8
15. Stochastic homogeneous freezing of supercooled droplets in mixed-phase clouds in particle-based idealised microphysics framework (exemplified with PySDM v3.0) T. Lüttmer et al. https://doi.org/10.5194/gmd-19-10185-2026
16. Extended POLIPHON dust conversion factor dataset for lidar-derived cloud condensation nuclei and ice-nucleating particle concentration profiles Y. He et al. https://doi.org/10.5194/amt-18-5669-2025