Articles | Volume 14, issue 1
Geosci. Model Dev., 14, 1–25, 2021
https://doi.org/10.5194/gmd-14-1-2021
Geosci. Model Dev., 14, 1–25, 2021
https://doi.org/10.5194/gmd-14-1-2021

Model description paper 04 Jan 2021

Model description paper | 04 Jan 2021

IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany

Felix Kleinert et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Felix Kleinert on behalf of the Authors (06 Nov 2020)  Author's response    Manuscript
ED: Publish as is (07 Nov 2020) by Juan Antonio Añel
AR by Felix Kleinert on behalf of the Authors (13 Nov 2020)  Author's response    Manuscript
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Short summary
With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for daily aggregated near-surface ozone concentrations with a lead time of up to 4 d. We used measurement and reanalysis data from more than 300 German monitoring stations to train, fine tune, and test the model. We show that the model outperforms standard reference models like persistence models and demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d.