Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 20, issue 13
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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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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