Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-149-2022
https://doi.org/10.5194/amt-15-149-2022
Research article
 | 
06 Jan 2022
Research article |  | 06 Jan 2022

A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

Alberto Sorrentino, Alessia Sannino, Nicola Spinelli, Michele Piana, Antonella Boselli, Valentino Tontodonato, Pasquale Castellano, and Xuan Wang

Viewed

Total article views: 1,756 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,147 553 56 1,756 47 67
  • HTML: 1,147
  • PDF: 553
  • XML: 56
  • Total: 1,756
  • BibTeX: 47
  • EndNote: 67
Views and downloads (calculated since 04 Jun 2021)
Cumulative views and downloads (calculated since 04 Jun 2021)

Viewed (geographical distribution)

Total article views: 1,756 (including HTML, PDF, and XML) Thereof 1,676 with geography defined and 80 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Apr 2024
Download
Short summary
We present a novel approach that can be used to obtain microphysical properties of atmospheric aerosol, up to several kilometers in the atmosphere, from lidar measurements taken from the ground. Our approach provides accurate reconstructions under many different experimental conditions. Our results can contribute to the expansion of the use of remote sensing techniques for air quality monitoring and atmospheric science in general.