Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 23, issue 9
Articles | Volume 23, issue 9
https://doi.org/10.5194/acp-23-5317-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-5317-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 9
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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Latest update: 23 Nov 2024
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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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