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@ARTICLE{Leufen:908456,
      author       = {Leufen, Lukas Hubert and Kleinert, Felix and Schultz,
                      Martin G.},
      title        = {{E}xploring decomposition of temporal patterns to
                      facilitate learning of neural networks for ground-level
                      daily maximum 8-hour average ozone prediction},
      journal      = {Environmental data science},
      volume       = {1},
      issn         = {2634-4602},
      address      = {Cambridge},
      publisher    = {Cambridge University Press},
      reportid     = {FZJ-2022-02615},
      pages        = {e10},
      year         = {2022},
      abstract     = {Exposure to ground-level ozone is a concern for both humans
                      and vegetation, so accurate prediction of ozone time series
                      is of great importance. However, conventional as well as
                      emerging methods have deficiencies in predicting time series
                      when a superposition of differently pronounced oscillations
                      on various time scales is present. In this paper, we propose
                      a meteorologically motivated filtering method of time series
                      data, which can separate oscillation patterns, in
                      combination with different multibranch neural networks. To
                      avoid phase shifts introduced by using a causal filter, we
                      combine past observation data with a climatological estimate
                      about the future to be able to apply a noncausal filter in a
                      forecast setting. In addition, the forecast in the form of
                      the expected climatology provides some a priori information
                      that can support the neural network to focus not merely on
                      learning a climatological statistic. We apply this method to
                      hourly data obtained from over 50 different monitoring
                      stations in northern Germany situated in rural or suburban
                      surroundings to generate a prediction for the daily maximum
                      8-hr average values of ground-level ozone 4 days into the
                      future. The data preprocessing with time filters enables
                      simpler neural networks such as fully connected networks as
                      well as more sophisticated approaches such as convolutional
                      and recurrent neural networks to better recognize long-term
                      and short-term oscillation patterns like the seasonal cycle
                      and thus leads to an improvement in the forecast skill,
                      especially for a lead time of more than 48 hr, compared to
                      persistence, climatological reference, and other reference
                      models.},
      cin          = {JSC},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)JSC-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) / Earth System Data Exploration
                      (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001223640200010},
      doi          = {10.1017/eds.2022.9},
      url          = {https://juser.fz-juelich.de/record/908456},
}