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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2019-481
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-481
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 30 Jan 2020

Submitted as: research article | 30 Jan 2020

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This preprint is currently under review for the journal AMT.

Exploration of machine learning methods for the classification of infrared limb spectra of polar stratospheric clouds

Rocco Sedona1,4, Lars Hoffmann1, Reinhold Spang2, Gabriele Cavallaro1, Sabine Griessbach1, Michael Höpfner3, Matthias Book4, and Morris Riedel4 Rocco Sedona et al.
  • 1Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, Germany
  • 2Institut für Energie- und Klimaforschung (IEK-7), Forschungszentrum Jülich, Jülich, Germany
  • 3Institut für Meteorlogie und Klimaforschung, Karlsruher Institut für Technologie, Karlsruhe, Germany
  • 4University of Iceland, Reykjavik, Iceland

Abstract. Polar stratospheric clouds (PSC) play a key role in polar ozone depletion in the stratosphere. Improved observations and continuous monitoring of PSCs can help to validate and enhance 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 have been 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 onboard ESA's 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 these datasets and to decide which features to extract from them. Here, we focused on an approach using brightness temperature differences (BTDs). From the both, the measured and the simulated infrared spectra, more than 10,000 BTD features have been 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) as well as the classification with the random forest (RF) and support vector machine (SVM) techniques. 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.

Rocco Sedona et al.

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Short summary
Polar stratospheric clouds (PSC) play a key role in polar ozone depletion in the stratosphere. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared spectra to classify PSC types. ML methods have proved to reach results in line with those obtained using well-established approaches. Among the considered ML methods, Random Forest (RF) seems to be the most promising one, being able to produce explainable classification results.
Polar stratospheric clouds (PSC) play a key role in polar ozone depletion in the stratosphere....
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