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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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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.

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.

Data sets

MIPAS geo-located and calibrated atmospheric spectra ESA https://earth.esa.int/web/guest/-/mipas-localized-calibrated-emission-spectra-1541

Publications Copernicus
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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.
Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere....
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