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@ARTICLE{Kleinert:889193,
      author       = {Kleinert, Felix and Leufen, Lukas H. and Schultz, Martin
                      G.},
      title        = {{I}ntelli{O}3-ts v1.0: a neural network approach to predict
                      near-surface ozone concentrations in {G}ermany},
      journal      = {Geoscientific model development},
      volume       = {14},
      number       = {1},
      issn         = {1991-9603},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2021-00103},
      pages        = {1 - 25},
      year         = {2021},
      abstract     = {The prediction of near-surface ozone concentrations is
                      important for supporting 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 network (CNN) layers, grouped together
                      as inception blocks. The model is trained with measured
                      multi-year ozone and nitrogen oxide 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 the prediction of daily maximum
                      8 h average (dma8eu) ozone concentrations for a lead time
                      of up to 4 d, and we show that the model outperforms
                      standard reference models like persistence models. Moreover,
                      we demonstrate that IntelliO3-ts outperforms climatological
                      reference models for the first 2 d, 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.},
      cin          = {JSC / NIC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / IntelliAQ -
                      Artificial Intelligence for Air Quality (787576) / PhD no
                      Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405) / Deep Learning for Air Quality and
                      Climate Forecasts $(deepacf_20191101)$ / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405 /
                      $G:(DE-Juel1)deepacf_20191101$ / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000606577700001},
      doi          = {10.5194/gmd-14-1-2021},
      url          = {https://juser.fz-juelich.de/record/889193},
}