Articles | Volume 26, issue 5
https://doi.org/10.5194/acp-26-3697-2026
https://doi.org/10.5194/acp-26-3697-2026
Research article
 | 
16 Mar 2026
Research article |  | 16 Mar 2026

Cloud condensation nuclei phenomenology: predictions based on aerosol chemical and optical properties

Inés Zabala, Juan Andrés Casquero-Vera, Elisabeth Andrews, Andrea Casans, Gerardo Carrillo-Cardenas, Anna Gannet Hallar, and Gloria Titos

Viewed

Total article views: 776 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
474 266 36 776 82 32 27
  • HTML: 474
  • PDF: 266
  • XML: 36
  • Total: 776
  • Supplement: 82
  • BibTeX: 32
  • EndNote: 27
Views and downloads (calculated since 07 Nov 2025)
Cumulative views and downloads (calculated since 07 Nov 2025)

Viewed (geographical distribution)

Total article views: 776 (including HTML, PDF, and XML) Thereof 756 with geography defined and 20 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Mar 2026
Download
Short summary
This study presents a comprehensive analysis of cloud condensation nuclei (CCN) phenomenology across nine observatories in diverse environments. We evaluate CCN prediction methods based on aerosol chemical composition and optical properties, including empirical and machine learning approaches. While simplified chemical schemes provide first-order estimates, incorporating optical data substantially improves CCN prediction accuracy in regions without direct measurements.
Share
Altmetrics
Final-revised paper
Preprint