Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8913-2022
https://doi.org/10.5194/gmd-15-8913-2022
Model experiment description paper
 | 
13 Dec 2022
Model experiment description paper |  | 13 Dec 2022

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz

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Latest update: 19 May 2024
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Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.