Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Jan 2021
Model description paper |
| 04 Jan 2021
| 04 Jan 2021
IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Institute of Geosciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Lukas H. Leufen
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Institute of Geosciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Martin G. Schultz
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
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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.
With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for...
