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@INPROCEEDINGS{Betancourt:892258,
      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},
      title        = {{G}lobal fine resolution mapping of ozone metrics through
                      explainable machine learning},
      reportid     = {FZJ-2021-01988},
      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.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° x 0.1°
                      resolution grid.  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.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.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.},
      month         = {Apr},
      date          = {2021-04-19},
      organization  = {EGU General Assembly 2021, Online
                       (Online), 19 Apr 2021 - 30 Apr 2021},
      subtyp        = {Other},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Enabling Computational- $\&$ Data-Intensive Science
                      and Engineering (POF4-511) / IntelliAQ - Artificial
                      Intelligence for Air Quality (787576) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-511 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.13140/RG.2.2.17134.13123},
      url          = {https://juser.fz-juelich.de/record/892258},
}