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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Polar stratospheric clouds (PSCs) 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.
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AMT | Articles | Volume 13, issue 7
AMT | Articles | Volume 13, issue 7
Atmos. Meas. Tech., 13, 3661–3682, 2020
https://doi.org/10.5194/amt-13-3661-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 3661–3682, 2020
https://doi.org/10.5194/amt-13-3661-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
AMT | Articles | Volume 13, issue 7

Research article 08 Jul 2020

Research article | 08 Jul 2020

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

Rocco Sedona et al.

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Short summary
Polar stratospheric clouds (PSCs) 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.
Read more
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Atmospheric Measurement Techniques

An interactive open-access journal of the European Geosciences Union

All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.
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