Optimal estimation method retrievals of stratospheric ozone profiles from a DIAL

Farhani, Ghazal; Sica, Robert J.; Godin-Beekmann, Sophie; Haefele, Alexander

doi:https://doi.org/10.5194/amt-12-2097-2019

Articles | Volume 12, issue 4

https://doi.org/10.5194/amt-12-2097-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-12-2097-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 4

Research article

|

04 Apr 2019

Research article |

| 04 Apr 2019

Optimal estimation method retrievals of stratospheric ozone profiles from a DIAL

Ghazal Farhani, Robert J. Sica, Sophie Godin-Beekmann, and Alexander Haefele

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Final revised paper (published on 04 Apr 2019)
Preprint (discussion started on 22 Oct 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Referee Comments #1', Anonymous Referee #1, 02 Nov 2018
- AC1: 'Reply to Referee 1', Robert Sica, 14 Mar 2019
RC2: 'Review of “Optimal Estimation Method Retrievals of Stratospheric Ozone Profiles from a DIAL Lidar”', Anonymous Referee #2, 16 Jan 2019
- AC2: 'Response to Referee 2', Robert Sica, 14 Mar 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Robert Sica on behalf of the Authors (14 Mar 2019) Manuscript

ED: Publish subject to technical corrections (22 Mar 2019) by Frank Hase

AR by Robert Sica on behalf of the Authors (25 Mar 2019) Manuscript

Short summary

This paper presents a new application of the optimal estimation method (OEM) for the retrieval of ozone density profiles from DIAL measurements. The OEM results show excellent agreement with coincident ozonesonde measurements, with improved resolution over the traditional technique. The method also provides averaging kernels (facilitating comparison with other instruments), the vertical resolution of the retrieval, and a complete random and systematic uncertainty budget.