Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees

Ivatt, Peter D.; Evans, Mathew J.

doi:https://doi.org/10.5194/acp-20-8063-2020

Articles | Volume 20, issue 13

https://doi.org/10.5194/acp-20-8063-2020

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

https://doi.org/10.5194/acp-20-8063-2020

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

Articles | Volume 20, issue 13

Research article

|

13 Jul 2020

Research article |

| 13 Jul 2020

Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees

Peter D. Ivatt and Mathew J. Evans

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Final revised paper (published on 13 Jul 2020)
Preprint (discussion started on 02 Oct 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'review', Anonymous Referee #2, 30 Oct 2019
RC2: 'Review', Anonymous Referee #1, 03 Nov 2019
SC1: 'Comments on the manuscript', Hyun Soo Kim, 05 Nov 2019
AC1: 'Author's responses', Peter Ivatt, 28 Feb 2020

Peer-review completion

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

AR by Peter Ivatt on behalf of the Authors (03 Mar 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (09 Mar 2020) by Chul Han Song

RR by Anonymous Referee #1 (11 Mar 2020)

RR by Anonymous Referee #2 (18 Apr 2020)

ED: Publish subject to minor revisions (review by editor) (29 Apr 2020) by Chul Han Song

AR by Peter Ivatt on behalf of the Authors (24 May 2020) Author's response Manuscript

ED: Publish as is (09 Jun 2020) by Chul Han Song

AR by Peter Ivatt on behalf of the Authors (15 Jun 2020) Manuscript

Short summary

We investigate the potential of using a decision tree algorithm to identify and correct the tropospheric ozone bias in a chemical transport model. We train the algorithm on 2010–2015 ground and column observation data and test the algorithm on the 2016–2017 data using the ground data as well as independent flight data. We find the algorithm is successfully able to identify and correct the bias, improving the model performance.