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@ARTICLE{Kleinert:916411,
      author       = {Kleinert, Felix and Leufen, Lukas H. and Lupascu, Aurelia
                      and Butler, Tim and Schultz, Martin G.},
      title        = {{R}epresenting chemical history in ozone time-series
                      predictions – a model experiment study building on the
                      {MLA}ir (v1.5) deep learning framework},
      journal      = {Geoscientific model development},
      volume       = {15},
      number       = {23},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2022-06211},
      pages        = {8913 - 8930},
      year         = {2022},
      abstract     = {Tropospheric ozone is a secondary air pollutant that is
                      harmful to living beings and crops. Predicting ozone
                      concentrations at specific locations is thus important to
                      initiate protection measures, i.e. emission reductions or
                      warnings to the population. Ozone levels at specific
                      locations result from emission and sink processes, mixing
                      and chemical transformation along an air parcel's
                      trajectory. Current ozone forecasting systems generally rely
                      on computationally expensive chemistry transport models
                      (CTMs). However, recently several studies have demonstrated
                      the potential of deep learning for this task. While a few of
                      these studies were trained on gridded model data, most
                      efforts focus on forecasting time series from individual
                      measurement locations. In this study, we present a hybrid
                      approach which is based on time-series forecasting (up to
                      4 d) but uses spatially aggregated meteorological and
                      chemical data from upstream wind sectors to represent some
                      aspects of the chemical history of air parcels arriving at
                      the measurement location. To demonstrate the value of this
                      additional information, we extracted pseudo-observation data
                      for Germany from a CTM to avoid extra complications with
                      irregularly spaced and missing data. However, our method can
                      be extended so that it can be applied to observational time
                      series. Using one upstream sector alone improves the
                      forecasts by $10 \%$ during all 4 d, while the use of
                      three sectors improves the mean squared error (MSE) skill
                      score by $14 \%$ during the first 2 d of the prediction
                      but depends on the upstream wind direction. Our method shows
                      its best performance in the northern half of Germany for the
                      first 2 prediction days. Based on the data's seasonality and
                      simulation period, we shed some light on our models' open
                      challenges with (i) spatial structures in terms of
                      decreasing skill scores from the northern German plain to
                      the mountainous south and (ii) concept drifts related to an
                      unusually cold winter season. Here we expect that the
                      inclusion of explainable artificial intelligence methods
                      could reveal additional insights in future versions of our
                      model.},
      cin          = {JSC},
      ddc          = {550},
      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) /
                      Open-Access-Publikationskosten Forschungszentrum Jülich
                      (OAPKFZJ) (491111487) / Earth System Data Exploration
                      (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(GEPRIS)491111487 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000898538800001},
      doi          = {10.5194/gmd-15-8913-2022},
      url          = {https://juser.fz-juelich.de/record/916411},
}