Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 7
Articles | Volume 19, issue 7
https://doi.org/10.5194/amt-19-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-19-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 7
Research article
 | 
16 Apr 2026
Research article |  | 16 Apr 2026

An ensemble machine learning method to retrieve aerosol parameters from ground-based Sun-sky photometer measurements

Qiurui Li, Zhongxia Sun, Meijing Liu, Huizheng Che, Yu Zheng, and Jing Li

Viewed

Total article views: 2,501 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,351 1,039 111 2,501 129 114
  • HTML: 1,351
  • PDF: 1,039
  • XML: 111
  • Total: 2,501
  • BibTeX: 129
  • EndNote: 114
Views and downloads (calculated since 15 Oct 2025)
Cumulative views and downloads (calculated since 15 Oct 2025)

Viewed (geographical distribution)

Total article views: 2,501 (including HTML, PDF, and XML) Thereof 2,433 with geography defined and 68 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jul 2026
Download
Short summary
We present a fast, interpretable machine learning method to retrieve key aerosol parameters from ground-based Sun-sky photometer measurements. Trained on simulated data covering diverse aerosol and atmospheric conditions, ensuring robustness and physical consistency. Applied to real observations, it agrees well with AERONET products and reduces computation time by orders of magnitude, offering a practical tool for monitoring aerosols and their effects on air quality and climate.
Read more
Share
Special issue
Sun-photometric measurements of aerosols: harmonization, comparisons,...