Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 10
Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 10
Research article
 | Highlight paper
 | 
16 Oct 2020
Research article | Highlight paper |  | 16 Oct 2020

A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements

Yun Dong, Elena Spinei, and Anuj Karpatne

Viewed

Total article views: 6,444 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
5,279 1,061 104 6,444 204 155 163
  • HTML: 5,279
  • PDF: 1,061
  • XML: 104
  • Total: 6,444
  • Supplement: 204
  • BibTeX: 155
  • EndNote: 163
Views and downloads (calculated since 21 Oct 2019)
Cumulative views and downloads (calculated since 21 Oct 2019)

Viewed (geographical distribution)

Total article views: 6,444 (including HTML, PDF, and XML) Thereof 5,996 with geography defined and 448 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Feb 2026
Download
Short summary
This paper is about a feasibility study of applying a machine learning technique to derive aerosol properties from a single MAX-DOAS sky scan, which detects sky-scattered UV–visible photons at multiple elevation angles. Evaluation of retrieved aerosol properties shows good performance of the ML algorithm, suggesting several advantages of a ML-based inversion algorithm such as fast data inversion, simple implementation and the ability to extract information not available using other algorithms.
Read more
Share