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@INPROCEEDINGS{Kleinert:872741,
      author       = {Kleinert, Felix and Gong, Bing and Götz, Markus and
                      Schultz, Martin},
      title        = {{N}ear {S}urface {O}zone {P}redictions {B}ased on
                      {M}ultiple {A}rtificial {N}eural {N}etwork {A}rchitectures},
      reportid     = {FZJ-2020-00219},
      year         = {2019},
      abstract     = {Artificial neural networks (ANNs) are well suited to solve
                      complex and highly non-linear problems. Various ma-chine
                      learning architectures like convolutional neural networks
                      (CNNs) and Long Short-Term Memory networks (LSTMs), both
                      subclasses of ANNs, are applied to the prediction of near
                      surface ozone concentrations (dma8eu) for a lead time of up
                      to four days at 51 measurement sites in southern Germany.
                      Only stations with at least 3500 days of valid data between
                      1997 and 2015 were used, while the first $80\%$ of the data
                      were used for training and the remaining $20\%$ for testing
                      and validation. Forecasts were evaluated with respect to
                      other continuous predictions from climatological,
                      persistence and ordinary least square (ols) models.
                      Furthermore, the quality of threshold exceedance predictions
                      for varying thresholds was analysed based on the joint
                      distribution of forecasts and ob-ervations. Finally, it was
                      examined which input variables are most important to
                      generate skilful predictions. The results of all three
                      analyses will be presented for all network architectures.
                      For example, experiments with a CNN architecture show that
                      those networks outperform ols and persistence pre-dictions
                      on all four days. The climatological predictions, however,
                      are outperformed at the first two days, only. On day three
                      and four the CNN’s skill score with respect to multivalued
                      climatological forecasts decreased with time,indicating no
                      added value on those days as the scores no longer differ
                      substantially. The CNNs performed best for continuous ozone
                      predictions between the $20\%$ and the $80\%$ percentile,
                      but cannot generate skilful predictions for higher or lower
                      concentrations due to imbalanced training data. The quality
                      of forecasts was determined primarily by the previous
                      day’s ozone concentration. Of all other input variables,
                      which were selected based on their importance for ozone
                      formation and air mass transport, only temperature and the
                      wind’s v-component had a small impact on forecast
                      quality.},
      month         = {Apr},
      date          = {2019-04-07},
      organization  = {EGU General Assembly 2019, Wien
                       (Austria), 7 Apr 2019 - 12 Apr 2019},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF3-512 / G:(DE-Juel1)PHD-NO-GRANT-20170405 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/872741},
}