Articles | Volume 15, issue 22
https://doi.org/10.5194/amt-15-6521-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-6521-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ICE-CAMERA: a flatbed scanner to study inland Antarctic polar precipitation
Massimo Del Guasta
CORRESPONDING AUTHOR
Istituto Nazionale Ottica CNR, Sesto Fiorentino 50019, Italy
Related authors
Adrian Hamel, Massimo del Guasta, Carl Schmitt, Christophe Genthon, Emma Järvinen, and Martin Schnaiter
EGUsphere, https://doi.org/10.5194/egusphere-2025-3598, https://doi.org/10.5194/egusphere-2025-3598, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We measured the size and shape of small ice particles in the dry and cold atmosphere of inland Antarctica. We observed that particles originating near the surface are smaller than those falling from higher altitudes. Inland Antarctic particles of frozen fog occur at lower concentrations and are less complex than those observed in an urban, polluted environment. These findings help to improve Antarctic climate models and to accurately interpret satellite observations of the polar atmosphere.
Federico Donat, Tiziano Maestri, Elisa Fabbri, Michele Martinazzo, Giovanni Bianchini, Massimo Del Guasta, Gianluca Di Natale, Luca Palchetti, Guido Masiello, Carmine Serio, and Giuliano Liuzzi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2793, https://doi.org/10.5194/egusphere-2025-2793, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The cloud occurrence over the Antarctic Plateau is characterized using ground-based interferometric data from 2012 to 2020. The results show a yearly pattern, and a six-month cycle linked to atmospheric oscillations. The cloud radiative forcing at far infrared doubles during cloud occurrence oscillation peaks. Infrared Atmospheric Sounding Interferometer (IASI) Level 2 products are compared to ground data, showing an improved agreement in cloud identification from year 2020.
Giuliano Dreossi, Mauro Masiol, Barbara Stenni, Daniele Zannoni, Claudio Scarchilli, Virginia Ciardini, Mathieu Casado, Amaëlle Landais, Martin Werner, Alexandre Cauquoin, Giampietro Casasanta, Massimo Del Guasta, Vittoria Posocco, and Carlo Barbante
The Cryosphere, 18, 3911–3931, https://doi.org/10.5194/tc-18-3911-2024, https://doi.org/10.5194/tc-18-3911-2024, 2024
Short summary
Short summary
Oxygen and hydrogen stable isotopes have been extensively used to reconstruct past temperatures, with precipitation representing the input signal of the isotopic records in ice cores. We present a 10-year record of stable isotopes in daily precipitation at Concordia Station: this is the longest record for inland Antarctica and represents a benchmark for quantifying post-depositional processes and improving the paleoclimate interpretation of ice cores.
Philippe Ricaud, Pierre Durand, Paolo Grigioni, Massimo Del Guasta, Giuseppe Camporeale, Axel Roy, Jean-Luc Attié, and John Bognar
Atmos. Meas. Tech., 17, 5071–5089, https://doi.org/10.5194/amt-17-5071-2024, https://doi.org/10.5194/amt-17-5071-2024, 2024
Short summary
Short summary
Clouds in Antarctica are key elements affecting climate evolution. Some clouds are composed of supercooled liquid water (SLW; water held in liquid form below 0 °C) and are difficult to forecast by models. We performed in situ observations of SLW clouds at Concordia Station using SLW sondes attached to meteorological balloons in summer 2021–2022. The SLW clouds were observed in a saturated layer at the top of the planetary boundary layer in agreement with ground-based lidar observations.
Philippe Ricaud, Massimo Del Guasta, Angelo Lupi, Romain Roehrig, Eric Bazile, Pierre Durand, Jean-Luc Attié, Alessia Nicosia, and Paolo Grigioni
Atmos. Chem. Phys., 24, 613–630, https://doi.org/10.5194/acp-24-613-2024, https://doi.org/10.5194/acp-24-613-2024, 2024
Short summary
Short summary
Clouds affect the Earth's climate in ways that depend on the type of cloud (solid/liquid water). From observations at Concordia (Antarctica), we show that in supercooled liquid water (liquid water for temperatures below 0°C) clouds (SLWCs), temperature and SLWC radiative forcing increase with liquid water (up to 70 W m−2). We extrapolated that the maximum SLWC radiative forcing can reach 40 W m−2 over the Antarctic Peninsula, highlighting the importance of SLWCs for global climate prediction.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
Short summary
Short summary
In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
Étienne Vignon, Lea Raillard, Christophe Genthon, Massimo Del Guasta, Andrew J. Heymsfield, Jean-Baptiste Madeleine, and Alexis Berne
Atmos. Chem. Phys., 22, 12857–12872, https://doi.org/10.5194/acp-22-12857-2022, https://doi.org/10.5194/acp-22-12857-2022, 2022
Short summary
Short summary
The near-surface atmosphere over the Antarctic Plateau is cold and pristine and resembles to a certain extent the high troposphere where cirrus clouds form. In this study, we use innovative humidity measurements at Concordia Station to study the formation of ice fogs at temperatures <−40°C. We provide observational evidence that ice fogs can form through the homogeneous freezing of solution aerosols, a common nucleation pathway for cirrus clouds.
William Cossich, Tiziano Maestri, Davide Magurno, Michele Martinazzo, Gianluca Di Natale, Luca Palchetti, Giovanni Bianchini, and Massimo Del Guasta
Atmos. Chem. Phys., 21, 13811–13833, https://doi.org/10.5194/acp-21-13811-2021, https://doi.org/10.5194/acp-21-13811-2021, 2021
Short summary
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.
Adrian Hamel, Massimo del Guasta, Carl Schmitt, Christophe Genthon, Emma Järvinen, and Martin Schnaiter
EGUsphere, https://doi.org/10.5194/egusphere-2025-3598, https://doi.org/10.5194/egusphere-2025-3598, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We measured the size and shape of small ice particles in the dry and cold atmosphere of inland Antarctica. We observed that particles originating near the surface are smaller than those falling from higher altitudes. Inland Antarctic particles of frozen fog occur at lower concentrations and are less complex than those observed in an urban, polluted environment. These findings help to improve Antarctic climate models and to accurately interpret satellite observations of the polar atmosphere.
Federico Donat, Tiziano Maestri, Elisa Fabbri, Michele Martinazzo, Giovanni Bianchini, Massimo Del Guasta, Gianluca Di Natale, Luca Palchetti, Guido Masiello, Carmine Serio, and Giuliano Liuzzi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2793, https://doi.org/10.5194/egusphere-2025-2793, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The cloud occurrence over the Antarctic Plateau is characterized using ground-based interferometric data from 2012 to 2020. The results show a yearly pattern, and a six-month cycle linked to atmospheric oscillations. The cloud radiative forcing at far infrared doubles during cloud occurrence oscillation peaks. Infrared Atmospheric Sounding Interferometer (IASI) Level 2 products are compared to ground data, showing an improved agreement in cloud identification from year 2020.
Giuliano Dreossi, Mauro Masiol, Barbara Stenni, Daniele Zannoni, Claudio Scarchilli, Virginia Ciardini, Mathieu Casado, Amaëlle Landais, Martin Werner, Alexandre Cauquoin, Giampietro Casasanta, Massimo Del Guasta, Vittoria Posocco, and Carlo Barbante
The Cryosphere, 18, 3911–3931, https://doi.org/10.5194/tc-18-3911-2024, https://doi.org/10.5194/tc-18-3911-2024, 2024
Short summary
Short summary
Oxygen and hydrogen stable isotopes have been extensively used to reconstruct past temperatures, with precipitation representing the input signal of the isotopic records in ice cores. We present a 10-year record of stable isotopes in daily precipitation at Concordia Station: this is the longest record for inland Antarctica and represents a benchmark for quantifying post-depositional processes and improving the paleoclimate interpretation of ice cores.
Philippe Ricaud, Pierre Durand, Paolo Grigioni, Massimo Del Guasta, Giuseppe Camporeale, Axel Roy, Jean-Luc Attié, and John Bognar
Atmos. Meas. Tech., 17, 5071–5089, https://doi.org/10.5194/amt-17-5071-2024, https://doi.org/10.5194/amt-17-5071-2024, 2024
Short summary
Short summary
Clouds in Antarctica are key elements affecting climate evolution. Some clouds are composed of supercooled liquid water (SLW; water held in liquid form below 0 °C) and are difficult to forecast by models. We performed in situ observations of SLW clouds at Concordia Station using SLW sondes attached to meteorological balloons in summer 2021–2022. The SLW clouds were observed in a saturated layer at the top of the planetary boundary layer in agreement with ground-based lidar observations.
Philippe Ricaud, Massimo Del Guasta, Angelo Lupi, Romain Roehrig, Eric Bazile, Pierre Durand, Jean-Luc Attié, Alessia Nicosia, and Paolo Grigioni
Atmos. Chem. Phys., 24, 613–630, https://doi.org/10.5194/acp-24-613-2024, https://doi.org/10.5194/acp-24-613-2024, 2024
Short summary
Short summary
Clouds affect the Earth's climate in ways that depend on the type of cloud (solid/liquid water). From observations at Concordia (Antarctica), we show that in supercooled liquid water (liquid water for temperatures below 0°C) clouds (SLWCs), temperature and SLWC radiative forcing increase with liquid water (up to 70 W m−2). We extrapolated that the maximum SLWC radiative forcing can reach 40 W m−2 over the Antarctic Peninsula, highlighting the importance of SLWCs for global climate prediction.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
Short summary
Short summary
In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
Étienne Vignon, Lea Raillard, Christophe Genthon, Massimo Del Guasta, Andrew J. Heymsfield, Jean-Baptiste Madeleine, and Alexis Berne
Atmos. Chem. Phys., 22, 12857–12872, https://doi.org/10.5194/acp-22-12857-2022, https://doi.org/10.5194/acp-22-12857-2022, 2022
Short summary
Short summary
The near-surface atmosphere over the Antarctic Plateau is cold and pristine and resembles to a certain extent the high troposphere where cirrus clouds form. In this study, we use innovative humidity measurements at Concordia Station to study the formation of ice fogs at temperatures <−40°C. We provide observational evidence that ice fogs can form through the homogeneous freezing of solution aerosols, a common nucleation pathway for cirrus clouds.
William Cossich, Tiziano Maestri, Davide Magurno, Michele Martinazzo, Gianluca Di Natale, Luca Palchetti, Giovanni Bianchini, and Massimo Del Guasta
Atmos. Chem. Phys., 21, 13811–13833, https://doi.org/10.5194/acp-21-13811-2021, https://doi.org/10.5194/acp-21-13811-2021, 2021
Short summary
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.
Cited articles
Argentini, S., Pietroni, I., Mastrantonio, G., Viola, A. P., Dargaud, G., and
Petenko, I.: Observations of near surface wind speed, temperature and
radiative budget at Dome C, Antarctic Plateau during 2005, Antarct. Sci.,
26, 104–112, https://doi.org/10.1017/S0954102013000382, 2014.
Aristidi, E.: An analysis of temperatures and wind speeds above Dome C,
Antarctica, Astron. Astrophys., 430, 739–746, https://doi.org/10.1051/0004-6361:20041876, 2005.
Bailey, M. P. and Hallett, J.: A Comprehensive Habit Diagram for Atmospheric
Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field
Studies, J. Atmos. Sci., 61, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009.
Böhm, H. P.: A General Equation for the Terminal Fall Speed of Solid
Hydrometeors, J. Atmos. Sci., 46, 2419–2427, https://doi.org/10.1175/1520-0469(1989)046<2419:AGEFTT>2.0.CO;2, 1989.
Bracci, A., Baldini, L., Roberto, N., Adirosi, E., Montopoli, M.,
Scarchilli, C., Grigioni, P., Ciardini, V., Levizzani, V., and Porcù, F.:
Quantitative Precipitation Estimation over Antarctica Using Different Ze–SR Relationships Based on Snowfall Classification Combining Ground Observations, Remote Sens., 14, 82, https://doi.org/10.3390/rs14010082, 2022.
Bromwich, D. H.: Snowfall in high southern latitudes, Rev. Geophys., 26, 149–168, https://doi.org/10.1029/RG026i001p00149, 1988.
Chokshi, A., Tielens, A. G. G. M., and Hollenbach, D.: Dust Coagulation, Astrophys. J., 407, p. 806, https://doi.org/10.1086/172562, 1993.
Del Guasta, M.: CNN for the classification of ICE-CAMERA images of
Antarctic ice particles, Zenodo [data set], https://doi.org/10.5281/zenodo.6822140, 2022.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F.-F.: ImageNet: A
large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009, IEEE, 248–255, https://doi.org/10.1109/CVPR.2009.5206848, 2009.
Eidevåg, T., Abrahamsson, P., Eng, M., and Rasmuson, A.: Modeling of dry
snow adhesion during normal impact with surfaces, Powder Technol., 361,
1081–1092, https://doi.org/10.1016/j.powtec.2019.10.085, 2020
Fujita, K. and Abe, O.: Stable isotopes in daily precipitation at Dome Fuji,
East Antarctica, Geophys. Res. Lett., 33, L18503, https://doi.org/10.1029/2006GL026936, 2006.
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012.
Genthon, C., Veron, D. E., Vignon, E., Madeleine, J.-B., and Piard, L.: Water vapor in cold and clean atmosphere: a 3-year data set in the boundary layer of Dome C, East Antarctic Plateau, Earth Syst. Sci. Data, 14, 1571–1580, https://doi.org/10.5194/essd-14-1571-2022, 2022.
Grazioli, J., Tuia, D., Monhart, S., Schneebeli, M., Raupach, T., and Berne, A.: Hydrometeor classification from two-dimensional video disdrometer data, Atmos. Meas. Tech., 7, 2869–2882, https://doi.org/10.5194/amt-7-2869-2014, 2014.
Grazioli, J., Genthon, C., Boudevillain, B., Duran-Alarcon, C., Del Guasta, M., Madeleine, J.-B., and Berne, A.: Measurements of precipitation in Dumont d'Urville, Adélie Land, East Antarctica, The Cryosphere, 11, 1797–1811, https://doi.org/10.5194/tc-11-1797-2017, 2017a.
Grazioli, J., Madeleine, J.-B., Gallée, H., Forbes, R. M., Genthon, C.,
Krinner, G., and Berne, A.: Katabatic Winds Diminish Precipitation Contribution to the Antarctic Ice Mass Balance, P. Natl. Acad. SCI. USA, 114, 10858–10863, https://doi.org/10.1073/pnas.1707633114, 2017b.
Grazioli, J., Ghiggi, G., Billault-Roux, A. C., and Berne, A.: MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall, Scientific Data, 9, 186, https://doi.org/10.1038/s41597-022-01269-7, 2022.
Ham, F. S.: Shape-preserving solutions of the time-dependent diffusion equation, Q. Appl. Math., 17, 137–145, https://doi.org/10.1090/qam/108196, 1959.
Heymsfield, A. J., Protat, A., Bouniol, D., Austin, R. T., Hogan, R. J.,
Delanoë, J., Okamoto, H., Sato, K., van Zadelhoff, G., Donovan, D. P., and Wang, Z.: Testing IWC Retrieval Methods Using Radar and Ancillary
Measurements with In Situ Data, J. Appl. Meteorol. Clim., 47, 135–163, https://doi.org/10.1175/2007JAMC1606.1, 2008.
Hiley, M. J., Kulie, M. S., and Bennartz, R.: Uncertainty Analysis for CloudSat Snowfall Retrievals, J. Appl. Meteorol. Clim., 50, 399–418, 2011.
Hogan, A. W.: Summer Ice Crystal Precipitation at the South Pole, J. Appl. Meteorol., 14, 246–248, https://doi.org/10.1175/1520-0450(1975)014<0246:SICPAT>2.0.CO;2, 1975.
Hu, M. K.: Visual pattern recognition by moment invariants, IRE T. Inform. Theor., 8, 179–187, https://doi.org/10.1109/TIT.1962.1057692, 1962.
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S.,
Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree
Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001.
Jambon-Puillet, E., Shahidzadeh, N., and Bonn, D.: Singular sublimation of
ice and snow crystals, Nat. Commun., 9, 4191, https://doi.org/10.1038/s41467-018-06689-x, 2018.
Kikuchi, K. and Hogan, A. W.: Properties of Diamond Dust Type Ice Crystals
Observed in Summer Season at Amundsen-Scott South Pole Station, Antarctica, J. Meteorol. Soc. Jpn. Ser. II, 57, 180–190, https://doi.org/10.2151/jmsj1965.57.2_180, 1979.
Kikuchi, K., Kameda, T., Higuchi, K., and Yamashita, A.: A global classification of snow crystals, ice crystals, and solid precipitation based on observations from middle latitudes to polar regions, Atmos. Res., 132, 460–472, https://doi.org/10.1016/j.atmosres.2013.06.006, 2013.
Konishi, H., Muramoto, K., Shiina, T., Endoh, T., and Kitano, K.: Z–R relation for graupels and aggregates observed at Syowa station, Antarctica, Proc. NIPR Symp. Polar Meteorol. Glaciol., 5, 97–103, 1992.
Lachlan-Cope, T., Ladkin, R., Turner, J., and Davison, P.: Observations of
cloud and precipitation particles on the Avery Plateau, Antarctic Peninsula, Antarct. Sci., 13, 339–348, https://doi.org/10.1017/S0954102001000475, 2001.
Lamb, D. and Hobbs, P. V.: Growth Rates and Habits of Ice Crystals Grown from
the Vapor Phase, J. Atmos. Sci., 28, 1506–1509,
https://doi.org/10.1175/1520-0469(1971)028<1507:GRAHOI>2.0.CO;2, 1971.
Lawson, R. P., Baker, B. A., Zmarzly, P., O'Connor, D., Mo, Q., Gayet, J., and Shcherbakov, V.: Microphysical and Optical Properties of Atmospheric Ice
Crystals at South Pole Station, J. Appl. Meteorol. Clim., 45, 1505–1524, https://doi.org/10.1175/JAM2421.1, 2006.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
Libbrecht, K. G.: The physics of snow crystals, Rep. Prog. Phys., 68, 855–895, https://doi.org/10.1088/0034-4885/68/4/R03, 2005.
Libbrecht, K. G.: Physical Dynamics of Ice Crystal Growth, Annu. Rev. Mater. Res., 47, 271–295, https://doi.org/10.1146/annurev-matsci-070616-124135, 2017.
Libois, Q., Picard, G., Arnaud, L., Morin, S., and Brun, E.: Modeling the
impact of snow drift on the decameter-scale variability of snow properties
on the Antarctic Plateau, J. Geophys. Res.-Atmos., 119, 662–681,
https://doi.org/10.1002/2014JD022361, 2014.
Lindqvist, H., Muinonen, K., Nousiainen, T., Um, J., McFarquhar, G. M., Haapanala, P., Makkonen, R., and Hakkarainen, H.: Ice-cloud particle
habit classification using principal components, J. Geophys. Res., 117,
D16206, https://doi.org/10.1029/2012JD017573, 2012.
Liu, G.: Deriving snow cloud characteristics from cloudsat observations, J.
Geophys. Res.-Atmos., 113, D00A09, https://doi.org/10.1029/2007JD009766, 2008.
Magono, C. and Lee, C. W.: Meteorological classification of natural snow
crystals, Journal of the Faculty of Science, Hokkaido University, Series 7, Geophysics, 2, 321–335, 1966.
Nelson, J.: Sublimation of Ice Crystals, J. Atmos. Sci., 55, 910–919,
https://doi.org/10.1175/1520-0469(1998)055<0910:SOIC>2.0.CO;2, 1988.
Newman, A. J., Kucera, P. A., and Bliven, L. F.: Presenting the Snowflake Video Imager (SVI), J. Atmos. Ocean. Tech., 26, 167–179, https://doi.org/10.1175/2008JTECHA1148.1, 2009.
Ohtake, T. and Yogi, T.: Winter ice crystals at the South Pole, Antarct. J.
US, 14, 201–203, 1979.
Palerme, C., Kay, J. E., Genthon, C., L'Ecuyer, T., Wood, N. B., and Claud, C.: How much snow falls on the Antarctic ice sheet?, The Cryosphere, 8, 1577–1587, https://doi.org/10.5194/tc-8-1577-2014, 2014.
Palerme, C., Claud, C., Dufour, A., Genthon, C., Wood, N. B., and L'Ecuyer, T.: Evaluation of Antarctic snowfall in global meteorological reanalyses,
Atmos. Res., 190, 104–112, https://doi.org/10.1016/j.atmosres.2017.02.015, 2017.
Palm, S. P., Yang, Y., and Vinay, K.: New Perspectives on Blowing Snow in Antarctica and Implications for Ice Sheet Mass Balance, in: Antarctica – A Key To Global Change, edited by: Kanao, M., Toyokuni, G., and Yamamoto, M., IntechOpen, https://doi.org/10.5772/intechopen.81319, 2018
Pratt, W. K.: Digital Image Processing: PIKS Scientific Inside, 4th edn., John Wiley & Sons, Inc., Los Altos, California, https://doi.org/10.1002/0470097434, 2007.
Praz, C., Roulet, Y.-A., and Berne, A.: Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera, Atmos. Meas. Tech., 10, 1335–1357, https://doi.org/10.5194/amt-10-1335-2017, 2017.
Russ, J. C. and Brent Neal, F.: The Image Processing Handbook, 7th edn.,
CRC Press, 1053 pp., https://doi.org/10.1201/b18983, 2017.
Ryzhkin, I. A. and Petrenko, V. F.: Physical Mechanisms Responsible for Ice
Adhesion, J. Phys. Chem. B, 101, 6267–6270, https://doi.org/10.1021/jp9632145, 1977.
Santachiara, G., Belosi, F., and Prodi, F.: Ice crystal precipitation at Dome C site (East Antarctica), Atmos. Res., 167, 108–117,
https://doi.org/10.1016/j.atmosres.2015.08.006, 2016.
Satow, K.: Observations on the Shapes of Snow Crystals in the Summer Season
in Mizuho Plateau, Antarctica, Memoirs of National Institute of Polar
Research, Special issue, 29, 103–109, 1983.
Schlosser, E., Dittmann, A., Stenni, B., Powers, J. G., Manning, K. W., Masson-Delmotte, V., Valt, M., Cagnati, A., Grigioni, P., and Scarchilli, C.: The influence of the synoptic regime on stable water isotopes in precipitation at Dome C, East Antarctica, The Cryosphere, 11, 2345–2361, https://doi.org/10.5194/tc-11-2345-2017, 2017.
Schmidhuber, J.: Deep Learning in Neural Networks: An Overview, Neural Networks, 61, 85–117, https://doi.org/10.1016/j.neunet.2014.09.003, 2014.
Schneider, U., Finger, P., Meyer-Christoffer, A., Rustemeier, E., Ziese, M.,
and Becker, A.: Evaluating the Hydrological Cycle over Land Using the
Newly-Corrected Precipitation Climatology from the Global Precipitation
Climatology Centre (GPCC), Atmosphere, 8, 52, https://doi.org/10.3390/atmos8030052, 2017.
Shimizu, H.: Long prism “Crystals observed in the precipitation in Antarctica”, J. Meteorol. Soc. Jpn., 41, 305–307, https://doi.org/10.2151/jmsj1923.41.5_305, 1963.
Shorten, C. and Khoshgoftaar, T. M.: A survey on Image Data Augmentation for
Deep Learning, Journal of Big Data, 6, 60, https://doi.org/10.1186/s40537-019-0197-0, 2019.
Smiley, V. N., Whitcomb, B. M., Morley, B. M., and Warburton, J. A.: Lidar
Determinations of Atmospheric Ice Crystal Layers at South Pole during
Clear-Sky Precipitation, J. Appl. Meteorol., 19, 1074–1090, 1980.
Souverijns, N., Gossart, A., Lhermitte, S., Gorodetskaya, I. V., Kneifel, S.,
Maahn, M., Bliven, F. L., and van Lipzig, P. M.: Estimating radar reflectivity - Snowfall rate relationships and their uncertainties over Antarctica by combining disdrometer and radar observations, Atmos. Res., 196,
211–223, https://doi.org/10.1016/j.atmosres.2017.06.001, 2017.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan,
D., Vanhoucke, V., and Rabinovich, A.: Going deeper with convolutions, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 7–12 June 2015, IEEE, 1–9, https://doi.org/10.1109/CVPR.2015.7298594, 2015.
Tremblin, P., Minier, V., Schneider, N., Durand, G. Al., Ashley, M. C. B.,
Lawrence, J. S., Luong-Van, D. M., Storey, J. W. V., Durand, G. An., Reinert, Y., Veyssiere, C., Walter, C., Ade, P., Calisse, P. G., Challita, Z., Fossat, E., Sabbatini, L., Pellegrini, A., Ricaud, P., and Urban, J.: Site testing for submillimetre astronomy at Dome C, Antarctica, Astron. Astrophys., 535, A112, https://doi.org/10.1051/0004-6361/201117345, 2011.
Vignon, E., Genthon, C., Barral, H., Amory, C., Picard, G., Gallée, H.,
Casasanta, G., and Argentini, S.: Momentum- and Heat-Flux Parametrization at
Dome C, Antarctica: A Sensitivity Study, Bound.-Lay. Meteorol., 162, 341–367, https://doi.org/10.1007/s10546-016-0192-3, 2017.
Walden, V. P., Warren, S. G., and Tuttle, E.: Atmospheric Ice Crystals over the Antarctic Plateau in Winter, J. Appl. Meteorol., 42, 1391–1405, https://doi.org/10.1175/1520-0450(2003)042<1391:AICOTA>2.0.CO;2, 2003.
Walton, W. H.: Feret's Statistical Diameter as a Measure of Particle Size, Nature, 162, 329–330, https://doi.org/10.1038/162329b0, 1948.
Wood, N., L'Ecuyer, T., Heymsfield, A., and Stephens, G.: Microphysical
Constraints on Millimeter-Wavelength Scattering Properties of Snow
Particles, J. Appl. Meteorol. Clim., 54, 909–931,
https://doi.org/10.1175/JAMC-D-14-0137.1, 2015.
Xiao, H., Zhang, F., He, Q., Liu, P., Yan, F., Miao, L., and Yang, Z.:
Classification of ice crystal habits observed from airborne Cloud ParticleImager by deep transfer learning, Earth and Space Science, 6,
1877–1886, https://doi.org/10.1029/2019EA000636, 2019.
Zheleznyak, A. G. and Sidorov, V. G.: Flatbed scanner as an instrument for
physical studies, St. Petersburg Polytechnical University Journal: Physics
and Mathematics, 1, 134–141, https://doi.org/10.1016/j.spjpm.2015.04.001, 2015.
Short summary
Any instrument on the Antarctic plateau must cope with a harsh environment. Concordia station is a special place for testing new instruments. With low temperatures and weak winds, precipitation can be studied by simply collecting it on horizontal surfaces. This is typically done manually. ICE-CAMERA is intended as an automatic alternative. The combined construction of rugged equipment for taking photographs of particles and the adoption of machine learning techniques have served this purpose.
Any instrument on the Antarctic plateau must cope with a harsh environment. Concordia station is...