Articles | Volume 23, issue 9
https://doi.org/10.5194/acp-23-5317-2023
https://doi.org/10.5194/acp-23-5317-2023
Technical note
 | 
11 May 2023
Technical note |  | 11 May 2023

Technical note: Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques

Jean-Maxime Bertrand, Frédérik Meleux, Anthony Ung, Gaël Descombes, and Augustin Colette

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Cited articles

Badia, A. and Jorba, O.: Gas-phase evaluation of the online NMMB/BSC-CTM model over Europe for 2010 in the framework of the AQMEII-Phase2 project, Atmos. Environ., 115, 657–669, https://doi.org/10.1016/j.atmosenv.2014.05.055, 2015. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001. 
Breiman, L., Friedman, J. H., Ohlsen, R. A., and Stone C. J.: Classification and Regression Trees, Chapman and Hall/CRC, ISBN 13:978-0412048418, 1984. 
Christensen, J. H.: The Danish Eulerian hemispheric model – A three-dimensional air pollution model used for the Arctic, Atmos. Environ., 31, 4169–4191, 1997. 
Delle Monache, L. and Stull, R. B.: An ensemble air quality forecast over western Europe during an ozone episode, Atmos. Environ., 37, 3469–3474, 2003. 
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Short summary
Post-processing methods based on machine learning algorithms were applied to refine the forecasts of four key pollutants at monitoring sites across Europe. Performances show significant improvements compared to those of the deterministic model raw outputs. Taking advantage of the large modelling domain extension, an innovative global approach is proposed to drastically reduce the period necessary to train the models and thus facilitate the implementation in an operational context.
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