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@INPROCEEDINGS{Betancourt:890956,
      author       = {Betancourt, Clara and Stadtler, Scarlet and Stomberg, Timo
                      and Edrich, Ann-Kathrin and Patnala, Ankit and Roscher,
                      Ribana and Kowalski, Julia and Schultz, Martin G.},
      title        = {{G}lobal fine resolution mapping of ozone metrics through
                      explainable machine learning},
      reportid     = {FZJ-2021-01272},
      year         = {2021},
      abstract     = {Through the availability of multi-year ground based ozone
                      observations on a global scale, substantial geospatial meta
                      data, and high performance computing capacities, it is now
                      possible to use machine learning for a global data-driven
                      ozone assessment. In this presentation, we will show a
                      novel, completely data-driven approach to map tropospheric
                      ozone globally.</p><p>Our goal is to interpolate ozone
                      metrics and aggregated statistics from the database of the
                      Tropospheric Ozone Assessment Report (TOAR) onto a global
                      $0.1\&#176;$ x $0.1\&#176;$ resolution grid. $\&#160;It$ is
                      challenging to interpolate ozone, a toxic greenhouse gas
                      because its formation depends on many interconnected
                      environmental factors on small scales. We conduct the
                      interpolation with various machine learning methods trained
                      on aggregated hourly ozone data from five years at more than
                      5500 locations worldwide. We use several geospatial datasets
                      as training inputs to provide proxy input for environmental
                      factors controlling ozone formation, such as precursor
                      emissions and climate. The resulting maps contain different
                      ozone metrics, i.e. statistical aggregations which are
                      widely used to assess air pollution impacts on health,
                      vegetation, and climate.</p><p>The key aspects of this
                      contribution are twofold: First, we apply explainable
                      machine learning methods to the data-driven ozone
                      assessment. Second, we discuss dominant uncertainties
                      relevant to the ozone mapping and quantify their impact
                      whenever possible. Our methods include a thorough a-priori
                      uncertainty estimation of the various data and methods,
                      assessment of scientific consistency, finding critical model
                      parameters, using ensemble methods, and performing error
                      modeling.</p><p>Our work aims to increase the reliability
                      and integrity of the derived ozone maps through the
                      provision of scientific robustness to a data-centric machine
                      learning task. This study hence represents a blueprint for
                      how to formulate an environmental machine learning task
                      scientifically, gather the necessary data, and develop a
                      data-driven workflow that focuses on optimizing transparency
                      and applicability of its product to maximize its scientific
                      knowledge return.</p>},
      month         = {Apr},
      date          = {2021-04-19},
      organization  = {EGU General Assembly 2021, Online
                       (Online), 19 Apr 2021 - 30 Apr 2021},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Enabling Computational- $\&$ Data-Intensive Science
                      and Engineering (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)POF4-511 / G:(EU-Grant)787576 /
                      $G:(DE-Juel1)kiste_20200501$ / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.5194/egusphere-egu21-7596},
      url          = {https://juser.fz-juelich.de/record/890956},
}