Articles | Volume 25, issue 19
https://doi.org/10.5194/acp-25-12549-2025
https://doi.org/10.5194/acp-25-12549-2025
Research article
 | 
09 Oct 2025
Research article |  | 09 Oct 2025

Aerosol type classification with machine learning techniques applied to multiwavelength lidar data from EARLINET

Ana del Águila, Pablo Ortiz-Amezcua, Siham Tabik, Juan Antonio Bravo-Aranda, Sol Fernández-Carvelo, and Lucas Alados-Arboledas

Viewed

Total article views: 1,388 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,143 207 38 1,388 44 65
  • HTML: 1,143
  • PDF: 207
  • XML: 38
  • Total: 1,388
  • BibTeX: 44
  • EndNote: 65
Views and downloads (calculated since 29 Jan 2025)
Cumulative views and downloads (calculated since 29 Jan 2025)

Viewed (geographical distribution)

Total article views: 1,388 (including HTML, PDF, and XML) Thereof 1,387 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Oct 2025
Download
Short summary
This study applies machine learning (ML) techniques to classify aerosols using high-resolution multiwavelength lidar data from EARLINET network. We developed a reference dataset and evaluated six ML models, with LightGBM achieving over 90 % accuracy. Depolarization data proved critical for improving dust classification. Validated against independent datasets, our approach improves aerosol classification and may help refine lidar-based processing strategies.
Share
Altmetrics
Final-revised paper
Preprint