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@ARTICLE{Betancourt:905607,
      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 discussions},
      issn         = {1991-9611},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2022-00839},
      year         = {2022},
      abstract     = {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, a precompiled 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 feature space, 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          = {910},
      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) / AI
                      Strategy for Earth system data $(kiste_20200501)$ / Earth
                      System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      $G:(DE-Juel1)kiste_20200501$ / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.5194/gmd-2022-2},
      url          = {https://juser.fz-juelich.de/record/905607},
}