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, Lars Hoffmann, Reinhold Spang, Gabriele Cavallaro, Sabine Griessbach, Michael Höpfner, Matthias Book, and Morris Riedel


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Svenja Lange on behalf of the Authors (27 Apr 2020)  Author's response
ED: Referee Nomination & Report Request started (27 Apr 2020) by Christian von Savigny
RR by Anonymous Referee #2 (05 May 2020)
RR by Anonymous Referee #1 (05 May 2020)
ED: Publish subject to minor revisions (review by editor) (12 May 2020) by Christian von Savigny
AR by Rocco Sedona on behalf of the Authors (20 May 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (15 Jun 2020) by Christian von Savigny
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.