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

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Cited articles

Achtert, P. and Tesche, M.: Assessing lidar-based classification schemes for polar stratospheric clouds based on 16 years of measurements at Esrange, Sweden, J. Geophys. Res.-Atmos., 119, 1386–1405,, 2014. a
Adriani, A.: Climatology of polar stratospheric clouds based on lidar observations from 1993 to 2001 over McMurdo Station, Antarctica, J. Geophys. Res., 109, D24,, 2004. a
Arnone, E., Castelli, E., Papandrea, E., Carlotti, M., and Dinelli, B. M.: Extreme ozone depletion in the 2010–2011 Arctic winter stratosphere as observed by MIPAS/ENVISAT using a 2-D tomographic approach, Atmos. Chem. Phys., 12, 9149–9165,, 2012. a
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281–305, 2012. a
Biele, J., Tsias, A., Luo, B. P., Carslaw, K. S., Neuber, R., Beyerle, G., and Peter, T.: Nonequilibrium coexistence of solid and liquid particles in Arctic stratospheric clouds, J. Geophys. Res.-Atmos., 106, 22991–23007,, 2001. a
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.