Articles | Volume 19, issue 15
https://doi.org/10.5194/amt-19-5051-2026
https://doi.org/10.5194/amt-19-5051-2026
Research article
 | 
06 Aug 2026
Research article |  | 06 Aug 2026

Improving the confidence in retrievals of vertical distributions of cloud condensation nuclei number concentration from ARM supported by aircraft in situ observations

Jingjing Tian, Gourihar Kulkarni, Jennifer M. Comstock, John E. Shilling, Damao Zhang, Peng Wu, and Fan Mei

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Cited articles

Atmospheric Radiation Measurement (ARM) user facility: Retrieved number concentration of CCN profile from Kulkarni first algorithm (RNCCNPROF1KULKARNI), Southern Great Plains (C1), 25 January 2016–18 October 2023, ARM Data Center, https://doi.org/10.5439/1813858, 2023. 
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification and regression trees, Wadsworth International Group, Belmont, CA, USA, ISBN 978-0-534-98053-5, 1984. 
Bohlmann, S., Baars, H., Radenz, M., Engelmann, R., and Macke, A.: Ship-borne aerosol profiling with lidar over the Atlantic Ocean: from pure marine conditions to complex dust–smoke mixtures, Atmos. Chem. Phys., 18, 9661–9679, https://doi.org/10.5194/acp-18-9661-2018, 2018. 
Brendecke, J., Dong, X., Xi, B., and Zheng, X.: Maritime Aerosol and CCN Profiles Derived From Ship-Based Measurements Over Eastern North Pacific During MAGIC, Earth and Space Science, 9, https://doi.org/10.1029/2022ea002319, 2022. 
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Short summary
Cloud condensation nuclei are tiny particles that attract water vapor and help form clouds. A ground-based lidar method can estimate their number at different heights, but assumes the aerosol type does not change with height. Comparisons with aircraft data show good agreement of this method when aerosols are well mixed, but larger errors when stacked layers occur. We also developed an index to flag these complex conditions and indicate when this estimation method is more reliable.
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