Articles | Volume 24, issue 5
https://doi.org/10.5194/acp-24-2861-2024
https://doi.org/10.5194/acp-24-2861-2024
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: 2,513 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,995 444 74 2,513 116 56 41
  • HTML: 1,995
  • PDF: 444
  • XML: 74
  • Total: 2,513
  • Supplement: 116
  • BibTeX: 56
  • EndNote: 41
Views and downloads (calculated since 03 Nov 2022)
Cumulative views and downloads (calculated since 03 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,513 (including HTML, PDF, and XML) Thereof 2,473 with geography defined and 40 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 Oct 2024
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.
Altmetrics
Final-revised paper
Preprint