Articles | Volume 21, issue 18
https://doi.org/10.5194/acp-21-13811-2021
https://doi.org/10.5194/acp-21-13811-2021
Research article
 | 
17 Sep 2021
Research article |  | 17 Sep 2021

Ice and mixed-phase cloud statistics on the Antarctic Plateau

William Cossich, Tiziano Maestri, Davide Magurno, Michele Martinazzo, Gianluca Di Natale, Luca Palchetti, Giovanni Bianchini, and Massimo Del Guasta

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Cited articles

Adhikari, L., Wang, Z., and Deng, M.: Seasonal variations of Antarctic clouds observed by CloudSat and CALIPSO satellites, J. Geophys. Res.-Atmos., 117, D04202, https://doi.org/10.1029/2011jd016719, 2012. a, b, c
Bianchini, G., Castagnoli, F., Di Natale, G., and Palchetti, L.: A Fourier transform spectroradiometer for ground-based remote sensing of the atmospheric downwelling long-wave radiance, Atmos. Meas. Tech., 12, 619–635, https://doi.org/10.5194/amt-12-619-2019, 2019. a
Bromwich, D. H., Nicolas, J. P., Hines, K. M., Kay, J. E., Key, E. L., Lazzara, M. A., Lubin, D., McFarquhar, G. M., Gorodetskaya, I. V., Grosvenor, D. P., Lachlan-Cope, T., and van Lipzig, N. P. M.: Tropospheric clouds in Antarctica, Rev. Geophys., 50, RG1004, https://doi.org/10.1029/2011rg000363, 2012. a, b, c, d
Ceccaldi, M., Delanoë, J., Hogan, R. J., Pounder, N. L., Protat, A., and Pelon, J.: From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations, J. Geophys. Res.-Atmos., 118, 7962–7981, https://doi.org/10.1002/jgrd.50579, 2013. a
Chan, M. A. and Comiso, J. C.: Cloud features detected by MODIS but not by CloudSat and CALIOP, Geophys. Res. Lett., 38, L24813, https://doi.org/10.1029/2011gl050063, 2011. a, b
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Short summary
The presence of clouds over Concordia, in the Antarctic Plateau, is investigated. Results are obtained by applying a machine learning algorithm to measurements of the infrared radiation emitted by the atmosphere toward the surface. The clear-sky, ice cloud, and mixed-phase cloud occurrence at different timescales is studied. A comparison with satellite measurements highlights the ability of the algorithm to identify multiple cloud conditions and study their variability at different timescales.
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