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@ARTICLE{Leufen:891148,
      author       = {Leufen, Lukas Hubert and Kleinert, Felix and Schultz,
                      Martin G.},
      title        = {{MLA}ir (v1.0) – a tool to enable fast and flexible
                      machine learning on air data time series},
      journal      = {Geoscientific model development},
      volume       = {14},
      number       = {3},
      issn         = {1991-9603},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2021-01397},
      pages        = {1553 - 1574},
      year         = {2021},
      abstract     = {With MLAir (Machine Learning on Air data) we created a
                      software environment that simplifies and accelerates the
                      exploration of new machine learning (ML) models,
                      specifically shallow and deep neural networks, for the
                      analysis and forecasting of meteorological and air quality
                      time series. Thereby MLAir is not developed as an abstract
                      workflow, but hand in hand with actual scientific questions.
                      It thus addresses scientists with either a meteorological or
                      an ML background. Due to their relative ease of use and
                      spectacular results in other application areas, neural
                      networks and other ML methods are also gaining enormous
                      momentum in the weather and air quality research
                      communities. Even though there are already many books and
                      tutorials describing how to conduct an ML experiment, there
                      are many stumbling blocks for a newcomer. In contrast,
                      people familiar with ML concepts and technology often have
                      difficulties understanding the nature of atmospheric data.
                      With MLAir we have addressed a number of these pitfalls so
                      that it becomes easier for scientists of both domains to
                      rapidly start off their ML application. MLAir has been
                      developed in such a way that it is easy to use and is
                      designed from the very beginning as a stand-alone, fully
                      functional experiment. Due to its flexible, modular code
                      base, code modifications are easy and personal experiment
                      schedules can be quickly derived. The package also includes
                      a set of validation tools to facilitate the evaluation of ML
                      results using standard meteorological statistics. MLAir can
                      easily be ported onto different computing environments from
                      desktop workstations to high-end supercomputers with or
                      without graphics processing units (GPUs).},
      cin          = {JSC / NIC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / IntelliAQ -
                      Artificial Intelligence for Air Quality (787576) / Deep
                      Learning for Air Quality and Climate Forecasts
                      $(deepacf_20191101)$ / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      $G:(DE-Juel1)deepacf_20191101$ / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000631053800002},
      doi          = {10.5194/gmd-14-1553-2021},
      url          = {https://juser.fz-juelich.de/record/891148},
}