Articles | Volume 20, issue 13
https://doi.org/10.5194/acp-20-8063-2020
https://doi.org/10.5194/acp-20-8063-2020
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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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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
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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.
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