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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2020
Research article |
| 13 Jul 2020
| 13 Jul 2020
Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees
Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, YO10 5DD, UK
National Centre for Atmospheric Science, Department of Chemistry, University of York, York, YO10 5DD, UK
Mathew J. Evans
Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, YO10 5DD, UK
National Centre for Atmospheric Science, Department of Chemistry, University of York, York, YO10 5DD, UK
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Latest update: 13 Dec 2024
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.
We investigate the potential of using a decision tree algorithm to identify and correct the...
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