Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 1
Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 1
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: 2,767 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,916 760 91 2,767 104 130
  • HTML: 1,916
  • PDF: 760
  • XML: 91
  • Total: 2,767
  • BibTeX: 104
  • EndNote: 130
Views and downloads (calculated since 04 Jun 2021)
Cumulative views and downloads (calculated since 04 Jun 2021)

Viewed (geographical distribution)

Total article views: 2,767 (including HTML, PDF, and XML) Thereof 2,651 with geography defined and 116 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
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.
Read more
Share