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@ARTICLE{Betancourt:908001,
      author       = {Betancourt, Clara and Stomberg, Timo T. and Edrich,
                      Ann-Kathrin and Patnala, Ankit and Schultz, Martin G. and
                      Roscher, Ribana and Kowalski, Julia and Stadtler, Scarlet},
      title        = {{G}lobal, high-resolution mapping of tropospheric ozone –
                      explainable machine learning and impact of uncertainties},
      journal      = {Geoscientific model development},
      volume       = {15},
      number       = {11},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2022-02315},
      pages        = {4331 - 4354},
      year         = {2022},
      abstract     = {Tropospheric ozone is a toxic greenhouse gas with a highly
                      variable spatial distribution which is challenging to map on
                      a global scale. Here, we present a data-driven ozone-mapping
                      workflow generating a transparent and reliable product. We
                      map the global distribution of tropospheric ozone from
                      sparse, irregularly placed measurement stations to a
                      high-resolution regular grid using machine learning methods.
                      The produced map contains the average tropospheric ozone
                      concentration of the years 2010–2014 with a resolution of
                      0.1∘ × 0.1∘. The machine learning model is trained
                      on AQ-Bench (“air quality benchmark dataset”), a
                      pre-compiled benchmark dataset consisting of multi-year
                      ground-based ozone measurements combined with an abundance
                      of high-resolution geospatial data.Going beyond standard
                      mapping methods, this work focuses on two key aspects to
                      increase the integrity of the produced map. Using
                      explainable machine learning methods, we ensure that the
                      trained machine learning model is consistent with commonly
                      accepted knowledge about tropospheric ozone. To assess the
                      impact of data and model uncertainties on our ozone map, we
                      show that the machine learning model is robust against
                      typical fluctuations in ozone values and geospatial data. By
                      inspecting the input features, we ensure that the model is
                      only applied in regions where it is reliable.We provide a
                      rationale for the tools we use to conduct a thorough global
                      analysis. The methods presented here can thus be easily
                      transferred to other mapping applications to ensure the
                      transparency and reliability of the maps produced.},
      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) / Earth
                      System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000805424800001},
      doi          = {10.5194/gmd-15-4331-2022},
      url          = {https://juser.fz-juelich.de/record/908001},
}