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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-169
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-169
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 17 Aug 2020

Submitted as: model description paper | 17 Aug 2020

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

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

Felix Kleinert1,2, Lukas H. Leufen1,2, and Martin G. Schultz1 Felix Kleinert et al.
  • 1Forschungszentrum Jülich, Jülich Supercomputing Center, Wilhelm-Johnen-Straße 52428 Jülich
  • 2Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Geosciences, Germany

Abstract. The prediction of near-surface ozone concentrations is important to support regulatory procedures for the protection of humans from high exposure to air pollution. In this study, we introduce a data-driven forecasting model named IntelliO3-ts, which consists of multiple convolutional neural layers (CNN), grouped together as inception blocks. The model is trained with measured multi-year ozone and nitrogen oxides concentrations of more than 300 German measurement stations in rural environments, and six meteorological variables from the meteorological COSMO reanalysis. This is by far the most extensive dataset used for time series predictions based on neural networks so far. IntelliO3-ts allows predicting daily maximum 8-hour average (dma8eu) ozone concentrations for a lead time of up to four days, and we show that the model outperforms standard reference models like persistence. Moreover, we demonstrate that IntelliO3-ts outperforms climatological reference models for the first two days, while it does not add any genuine value for longer lead times. We attribute this to the limited deterministic information that is contained in the single station time series training data. We applied a bootstrapping technique to analyse the influence of different input variables and found, that the previous day ozone concentrations are of major importance, followed by 2 m temperature. As we did not use any geographic information to train IntelliO3-ts in its current version and included no relation between stations, the influence of the horizontal wind components on the model performance is minimal. We expect that the inclusion of advection-diffusion terms in the model could improve results in future versions of our model.

Felix Kleinert et al.

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Felix Kleinert et al.

Felix Kleinert et al.

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Latest update: 01 Dec 2020
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Short summary
With IntelliO3-ts v1.0 we present a new forecasting model for daily aggregated near-surface ozone concentrations. The model is based on Convolutional Neural Networks (CNN). 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 and demonstrate that IntelliO3-ts outperforms climatological reference models for the first two days.
With IntelliO3-ts v1.0 we present a new forecasting model for daily aggregated near-surface...
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