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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Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
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Cited articles

Anderson, G. J. and Lucas, D. D.: Machine Learning Predictions of a Multiresolution Climate Model Ensemble, Geophys. Res. Lett., 45, 4273–4280,, 2018. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55,, 2015. a
Bergstra, J. and Bengio, Y.: Random Search for Hyper-Parameter Optimization, J. Mach. Learn. Res., 13, 281–305, 2012. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q. B., Liu, H. G. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095,, 2001. a
Blockeel, H. and De Raedt, L.: Top-down induction of first-order logical decision trees, Artificial Intelligence, 101, 285–297,, 1998. a
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.
Final-revised paper