Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 5
Articles | Volume 24, issue 5
https://doi.org/10.5194/acp-24-2861-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-2861-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 5
Research article
 | 
05 Mar 2024
Research article |  | 05 Mar 2024

A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations

Piyushkumar N. Patel, Jonathan H. Jiang, Ritesh Gautam, Harish Gadhavi, Olga Kalashnikova, Michael J. Garay, Lan Gao, Feng Xu, and Ali Omar

Viewed

Total article views: 5,233 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,867 1,226 140 5,233 313 156 166
  • HTML: 3,867
  • PDF: 1,226
  • XML: 140
  • Total: 5,233
  • Supplement: 313
  • BibTeX: 156
  • EndNote: 166
Views and downloads (calculated since 03 Nov 2022)
Cumulative views and downloads (calculated since 03 Nov 2022)

Viewed (geographical distribution)

Total article views: 5,233 (including HTML, PDF, and XML) Thereof 5,196 with geography defined and 37 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 09 Jun 2026
Download
Short summary
Global measurements of cloud condensation nuclei (CCN) are essential for understanding aerosol–cloud interactions and predicting climate change. To address this gap, we introduced a remote sensing algorithm that retrieves vertically resolved CCN number concentrations from airborne and spaceborne lidar systems. This innovation offers a global distribution of CCN concentrations from space, facilitating model evaluation and precise quantification of aerosol climate forcing.
Read more
Share
Altmetrics
Final-revised paper
Preprint