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@INPROCEEDINGS{Kleinert:890961,
      author       = {Kleinert, Felix and Leufen, Lukas H. and Lupascu, Aurelia
                      and Butler, Tim and Schultz, Martin G.},
      title        = {{R}epresenting chemical history for ozone time-series
                      predictions - a method development study for deep learning
                      models},
      reportid     = {FZJ-2021-01277},
      year         = {2021},
      abstract     = {<p>Machine learning techniques like deep learning gained
                      enormous momentum in recent years. This was mainly caused by
                      the success story of the main drivers like image and speech
                      recognition, video prediction and autonomous driving, to
                      name just a few.<br>Air pollutant forecasting models are an
                      example, where earth system scientists start picking up deep
                      learning models to enhance the forecast quality of time
                      series. Almost all previous air pollution forecasts with
                      machine learning rely solely on analysing temporal features
                      in the observed time series of the target compound(s) and
                      additional variables describing precursor concentrations and
                      meteorological conditions. These studies, therefore, neglect
                      the 'chemical history' of air masses, i.e. the fact that air
                      pollutant concentrations at a given observation site are a
                      result of emission and sink processes, mixing and chemical
                      transformations along the transport pathways of air.<br>This
                      study develops a concept of how such factors can be
                      represented in the recently published deep learning model
                      IntelliO3. The concept is demonstrated with numerical model
                      data from the WRF-Chem model because the gridded model data
                      provides an internally consistent dataset with complete
                      spatial coverage and no missing values.<br>Furthermore,
                      using model data allows for attributing changes of the
                      forecasting performance to specific conceptual aspects. For
                      example, we use the 8 wind sectors (N, NE, E, SE, etc.) and
                      circles with predefined radii around our target locations to
                      aggregate meteorological and chemical data from the
                      intersections. Afterwards, we feed this aggregated data into
                      a deep neural network while using the ozone concentration of
                      the central point's next timesteps as targets. By analysing
                      the change of forecast quality when moving from
                      4-dimensional (x, y, z, t) to 3-dimensional (x, y, t or r,
                      $\&#966;,$ t) sectors and thinning out the underlying model
                      data, we can deliver first estimates of expected performance
                      gains or losses when applying our concept to station based
                      surface observations in future studies.</p>},
      month         = {Apr},
      date          = {2021-04-19},
      organization  = {EGU General Assembly 2021, Vienna
                       (online) (Austria), 19 Apr 2021 - 30
                       Apr 2021},
      cin          = {JSC},
      ddc          = {610},
      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) / Earth
                      System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.5194/egusphere-egu21-12146},
      url          = {https://juser.fz-juelich.de/record/890961},
}