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@ARTICLE{Sedona:878382,
      author       = {Sedona, Rocco and Hoffmann, Lars and Spang, Reinhold and
                      Cavallaro, Gabriele and Griessbach, Sabine and Höpfner,
                      Michael and Book, Matthias and Riedel, Morris},
      title        = {{E}xploration of machine learning methods for the
                      classification of infrared limb spectra of polar
                      stratospheric clouds},
      journal      = {Atmospheric measurement techniques},
      volume       = {13},
      number       = {7},
      issn         = {1867-8548},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2020-02819},
      pages        = {3661 - 3682},
      year         = {2020},
      abstract     = {Polar stratospheric clouds (PSCs) play a key role in polar
                      ozone depletion in the stratosphere. Improved observations
                      and continuous monitoring of PSCs can help to validate and
                      improve chemistry–climate models that are used to predict
                      the evolution of the polar ozone hole. In this paper, we
                      explore the potential of applying machine learning (ML)
                      methods to classify PSC observations of infrared limb
                      sounders. Two datasets were considered in this study. The
                      first dataset is a collection of infrared spectra captured
                      in Northern Hemisphere winter 2006/2007 and Southern
                      Hemisphere winter 2009 by the Michelson Interferometer for
                      Passive Atmospheric Sounding (MIPAS) instrument on board the
                      European Space Agency's (ESA) Envisat satellite. The second
                      dataset is the cloud scenario database (CSDB) of simulated
                      MIPAS spectra. We first performed an initial analysis to
                      assess the basic characteristics of the CSDB and to decide
                      which features to extract from it. Here, we focused on an
                      approach using brightness temperature differences (BTDs).
                      From both the measured and the simulated infrared spectra,
                      more than 10 000 BTD features were generated. Next, we
                      assessed the use of ML methods for the reduction of the
                      dimensionality of this large feature space using principal
                      component analysis (PCA) and kernel principal component
                      analysis (KPCA) followed by a classification with the
                      support vector machine (SVM). The random forest (RF)
                      technique, which embeds the feature selection step, has also
                      been used as a classifier. All methods were found to be
                      suitable to retrieve information on the composition of PSCs.
                      Of these, RF seems to be the most promising method, being
                      less prone to overfitting and producing results that agree
                      well with established results based on conventional
                      classification methods.},
      cin          = {JSC / IEK-7},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IEK-7-20101013},
      pnm          = {244 - Composition and dynamics of the upper troposphere and
                      middle atmosphere (POF3-244) / 511 - Computational Science
                      and Mathematical Methods (POF3-511) / 512 - Data-Intensive
                      Science and Federated Computing (POF3-512)},
      pid          = {G:(DE-HGF)POF3-244 / G:(DE-HGF)POF3-511 /
                      G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000548519300003},
      doi          = {10.5194/amt-13-3661-2020},
      url          = {https://juser.fz-juelich.de/record/878382},
}