Articles | Volume 15, issue 1
Atmos. Meas. Tech., 15, 149–164, 2022
https://doi.org/10.5194/amt-15-149-2022
Atmos. Meas. Tech., 15, 149–164, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-152', Anonymous Referee #1, 27 Jun 2021
    • AC1: 'Reply on RC1', Alberto Sorrentino, 26 Jul 2021
  • RC2: 'Comment on amt-2021-152', Anonymous Referee #2, 28 Jun 2021
    • AC2: 'Reply on RC2', Alberto Sorrentino, 26 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Alberto Sorrentino on behalf of the Authors (30 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (31 Aug 2021) by Daniel Perez-Ramirez
RR by Anonymous Referee #2 (09 Sep 2021)
RR by Anonymous Referee #3 (09 Sep 2021)
ED: Reconsider after major revisions (09 Sep 2021) by Daniel Perez-Ramirez
AR by Alberto Sorrentino on behalf of the Authors (21 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (24 Oct 2021) by Daniel Perez-Ramirez
RR by Anonymous Referee #2 (09 Nov 2021)
ED: Publish as is (12 Nov 2021) by Daniel Perez-Ramirez
AR by Alberto Sorrentino on behalf of the Authors (15 Nov 2021)  Author's response    Manuscript

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Alberto Sorrentino on behalf of the Authors (03 Jan 2022)   Author's adjustment   Manuscript
EA: Adjustments approved (03 Jan 2022) by Daniel Perez-Ramirez
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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.