Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Chang, Kai-Lan; Cooper, Owen R.; Gaudel, Audrey; Petropavlovskikh, Irina; Thouret, Valérie

doi:https://doi.org/10.5194/acp-20-9915-2020

Articles | Volume 20, issue 16

https://doi.org/10.5194/acp-20-9915-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-20-9915-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 16

Research article

|

26 Aug 2020

Research article |

| 26 Aug 2020

Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Kai-Lan Chang, Owen R. Cooper, Audrey Gaudel, Irina Petropavlovskikh, and Valérie Thouret

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Final revised paper (published on 26 Aug 2020)
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Preprint (discussion started on 19 Mar 2020)
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Interactive discussion

Status: closed

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

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RC1: 'referee comment for acp-2019-959', Anonymous Referee #2, 01 Jun 2020
- AC1: 'response to both reviewers', Kai-Lan Chang, 08 Jul 2020
RC2: 'Review of “Statistical regularization for trend detection: An integrated approach for detecting long-term trends from sparse tropospheric ozone profiles” submitted by Kai-Lan Chang et al. to Atmos Chem Phys', Anonymous Referee #1, 04 Jun 2020
- AC2: 'response to both reviewers', Kai-Lan Chang, 08 Jul 2020

Peer-review completion

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

AR by Kai-Lan Chang on behalf of the Authors (08 Jul 2020) Author's response Manuscript

ED: Publish as is (21 Jul 2020) by Stefano Galmarini

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Short summary

We provide a statistical framework for detecting trends of multiple autocorrelated time series from sparsely sampled profile data. The result is a better and more consistent quantification of trend estimates of vertical profile data. The focus was placed on the long-term ozone time series from commercial aircraft and balloon-borne ozonesonde measurements. This framework can be applied to other trace gases in the atmosphere.