Articles | Volume 15, issue 17
https://doi.org/10.5194/amt-15-5141-2022
© 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-15-5141-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Sep 2022
Research article |
| 13 Sep 2022
| 13 Sep 2022
Ice crystal images from optical array probes: classification with convolutional neural networks
Louis Jaffeux
CORRESPONDING AUTHOR
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
Alfons Schwarzenböck
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
Pierre Coutris
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
Christophe Duroure
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
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Yongjie Huang, Wei Wu, Greg M. McFarquhar, Xuguang Wang, Hugh Morrison, Alexander Ryzhkov, Yachao Hu, Mengistu Wolde, Cuong Nguyen, Alfons Schwarzenboeck, Jason Milbrandt, Alexei V. Korolev, and Ivan Heckman
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Cited articles
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., 66, 2888–2899,
https://doi.org/10.1175/2009JAS2883.1, 2009. a
Baumgardner, D., Avallone, L., Bansemer, A., Borrmann, S., Brown, P., Bundke,
U., Chuang, P., Cziczo, D., Field, P., Gallagher, M., and Gayet, J. F.: In situ, airborne instrumentation: Addressing and solving measurement problems in ice clouds, Bull. Am. Meteorol. Soc., 93, p. 9.1,
2012. a
Bony, S., Lothon, M., Delanoë, J., Coutris, P., Etienne, J.-C., Aemisegger, F., Albright, A. L., André, T., Bellec, H., Baron, A., Bourdinot, J.-F., Brilouet, P.-E., Bourdon, A., Canonici, J.-C., Caudoux, C., Chazette, P., Cluzeau, M., Cornet, C., Desbios, J.-P., Duchanoy, D., Flamant, C., Fildier, B., Gourbeyre, C., Guiraud, L., Jiang, T., Lainard, C., Le Gac, C., Lendroit, C., Lernould, J., Perrin, T., Pouvesle, F., Richard, P., Rochetin, N., Salaün, K., Schwarzenboeck, A., Seurat, G., Stevens, B., Totems, J., Touzé-Peiffer, L., Vergez, G., Vial, J., Villiger, L., and Vogel, R.: EUREC4A observations from the SAFIRE ATR42 aircraft, Earth Syst. Sci. Data, 14, 2021–2064, https://doi.org/10.5194/essd-14-2021-2022, 2022. a
Defer, E., Pinty, J.-P., Coquillat, S., Martin, J.-M., Prieur, S., Soula, S.,
Richard, E., Rison, W., Krehbiel, P., Thomas, R., Rodeheffer, D., Vergeiner,
C., Malaterre, F., Pedeboy, S., Schulz, W., Farges, T., Gallin, L.-J.,
Ortéga, P., Ribaud, J.-F., Anderson, G., Betz, H.-D., Meneux, B., Kotroni,
V., Lagouvardos, K., Roos, S., Ducrocq, V., Roussot, O., Labatut, L., and
Molinié, G.: An overview of the lightning and atmospheric electricity
observations collected in southern France during the HYdrological cycle in
Mediterranean EXperiment (HyMeX), Special Observation Period 1, Atmos.
Meas. Tech., 8, 649–669, https://doi.org/10.5194/amt-8-649-2015, 2015. a, b, c
Dezitter, F., Grandin, A., Brenguier, J.-L., Hervy, F., Schlager, H.,
Villedieu, P., and Zalamansky, G.: HAIC-High Altitude Ice Crystals, in: 5th
AIAA Atmospheric and Space Environments Conference, San Diego, United States, 25 June 2013, p. 2674, https://doi.org/10.2514/6.2013-2674,
2013. a, b
Duroure, C.: Une nouvelle méthode de traitement des images
d'hydrométéores données par les sondes bidimensionnelles,
Journal de recherches atmosphériques,
https://hal.uca.fr/hal-01950254 (last access: 7 September 2022), 1982. a
Gao, J., Jiang, Q., Zhou, B., and Chen, D.: Convolutional neural networks for
computer-aided detection or diagnosis in medical image analysis: an overview,
Math. Biosci. Eng., 16, 6536–6561, 2019. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, in: Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Tours,
France, 27–30 June 2016, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Hobbs, P. V., Chang, S., and Locatelli, J. D.: The dimensions and aggregation
of ice crystals in natural clouds, J. Geophys. Res., 79, 2199–2206, https://doi.org/10.1029/JC079i015p02199, 1974. a
Houze, R. A., McMurdie, L. A., Petersen, W. A., Schwaller, M. R., Baccus, W.,
Lundquist, J. D., Mass, C. F., Nijssen, B., Rutledge, S. A., Hudak, D. R.,
Tanelli, S., Mace, G. G., Poellot, M. R., Lettenmaier, D. P., Zagrodnik,
J. P., Rowe, A. K., DeHart, J. C., Madaus, L. E., Barnes, H. C., and
Chandrasekar, V.: The Olympic Mountains Experiment (OLYMPEX), Bull.
Am. Meteorol. Soc., 98, 2167–2188,
https://doi.org/10.1175/BAMS-D-16-0182.1, 2017. a, b
Jaffeux, L.: LJaffeux/AMT-2022-72: Ice crystals images from
Optical Array Probes: classification with Convolutional Neural Networks, Zenodo [code and data set], https://doi.org/10.5281/zenodo.6912294, 2022. a
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.,
132/133, 460–472, https://doi.org/10.1016/j.atmosres.2013.06.006,
2013. a, b, c
Knollenberg, R. G.: The Optical Array: An Alternative to Scattering or
Extinction for Airborne Particle Size Determination, J. Appl.
Meteorol., 9, 86–103,
https://doi.org/10.1175/1520-0450(1970)009<0086:TOAAAT>2.0.CO;2, 1970. a
Korolev, A. and Sussman, B.: A technique for habit classification of cloud
particles, J. Atmos. Ocean. Technol., 17, 1048–1057,
2000. a
Lawson, R. P., Baker, B. A., Zmarly, P., O'Connor, D., Mo, Q., Gayet, J.-F.,
and Shcherbakov, V.: Microphysical and optical properties of atmospheric ice
crystals at South Pole Station, J. Appl. Meteorol.
Clim., 45, 1505–1524, 2006. a
Leinonen, J., Grazioli, J., and Berne, A.: Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images, Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021, 2021. a
Leroy, D., Fontaine, E., Schwarzenboeck, A., Strapp, J. W., Korolev, A.,
McFarquhar, G. M., Dupuy, R., Gourbeyre, C., Lilie, L., Protat, A.,
Delanoë, J., Dezitter, F., and Grandin, A.: Ice crystal sizes in high
ice water content clouds. Part 2: Statistics of mass diameter percentiles in
tropical convection observed during the HAIC/HIWC project, J.
Atmos. Ocean. Technol., 34, 117–136,
https://doi.org/10.1175/jtech-d-15-0246.1, 2017. a
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.-Atmos., 117, D16, https://doi.org/10.1029/2012JD017573, 2012. a
Luo, J. Y., Irisson, J.-O., Graham, B., Guigand, C., Sarafraz, A., Mader, C.,
and Cowen, R. K.: Automated plankton image analysis using convolutional
neural networks, Limnol. Oceanogr.-Method., 16, 814–827, 2018. a
McFarquhar, G. M., Ghan, S., Verlinde, J., Korolev, A., Strapp, J. W., Schmid,
B., Tomlinson, J. M., Wolde, M., Brooks, S. D., Cziczo, D., Dubey, M. K.,
Fan, J., Flynn, C., Gultepe, I., Hubbe, J., Gilles, M. K., Laskin, A.,
Lawson, P., Leaitch, W. R., Liu, P., Liu, X., Lubin, D., Mazzoleni, C.,
Macdonald, A.-M., Moffet, R. C., Morrison, H., Ovchinnikov, M., Shupe, M. D.,
Turner, D. D., Xie, S., Zelenyuk, A., Bae, K., Freer, M., and Glen, A.:
Indirect and Semi-direct Aerosol Campaign: The Impact of Arctic Aerosols on
Clouds, Bull. Am. Meteorol. Soc., 92, 183–201,
https://doi.org/10.1175/2010BAMS2935.1, 2011. a
Nakaya, U.: Snow crystal, natural and artificial, Harvard University Press, https://doi.org/10.4159/harvard.9780674182769,
1954. a
Park, S. and Kwak, N.: Analysis on the dropout effect in convolutional neural
networks, in: Asian conference on computer vision, 189–204, S pringer International Publishing, https://doi.org/10.1007/978-3-319-54184-6_12,
2016. a
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. a
Praz, C., Ding, S., McFarquhar, G. M., and Berne, A.: A Versatile Method for
Ice Particle Habit Classification Using Airborne Imaging Probe Data, J. Geophys. Res.-Atmos., 123, 13472–13495,
https://doi.org/10.1029/2018JD029163, 2018. a, b, c, d
Pruppacher, H. and Klett, J.: Microphysics of Clouds and Precipitation, Springer Dordrecht,
18, p. 39, https://doi.org/10.1007/978-0-306-48100-0, 2010. a
Przybylo, V., Sulia, K. J., Lebo, Z. J., and Schmitt, C.: Automated
Classification of Cloud Particle Imagery through the Use of Convolutional
Neural Networks, in: 101st American Meteorological Society Annual Meeting,
AMS, 10–15 January 2021,
https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/377736, last access: 7 September 2021. a
Rahman, M. M., Quincy, E. A., Jacquot, R. G., and Magee, M. J.: Feature
Extraction and Selection for Pattern Recognition of Two-Dimensional
Hydrometeor Images, J. Appl. Meteorol., 20, 521–535,
https://doi.org/10.1175/1520-0450(1981)020<0521:FEASFP>2.0.CO;2, 1981. a, b
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z.,
Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L.:
ImageNet Large Scale Visual Recognition Challenge, Int. J.
Comput. Vis., 115, 211–252, https://doi.org/10.1007/s11263-015-0816-y, 2015. a
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting,
J. Mach. Learn. Res., 15, 1929–1958,
http://jmlr.org/papers/v15/srivastava14a.html (last access: 7 September 2022), 2014. a
Sukovich, E. M., Kingsmill, D. E., and Yuter, S. E.: Variability of graupel and
snow observed in tropical oceanic convection by aircraft during TRMM KWAJEX,
J. Appl. Meteorol. Clim., 48, 185–198, 2009. a
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B.,
Gotway, M. B., and Liang, J.: Convolutional neural networks for medical image
analysis: Full training or fine tuning?, IEEE T. Med.
Imaging, 35, 1299–1312, 2016. a
Vaillant de Guélis, T., Schwarzenböck, A., Shcherbakov, V., Gourbeyre, C., Laurent, B., Dupuy, R., Coutris, P., and Duroure, C.: Study of the diffraction pattern of cloud particles and the respective responses of optical array probes, Atmos. Meas. Tech., 12, 2513–2529, https://doi.org/10.5194/amt-12-2513-2019, 2019. a, b
Woods, S., Lawson, R. P., Jensen, E., Bui, T., Thornberry, T., Rollins, A.,
Pfister, L., and Avery, M.: Microphysical properties of tropical tropopause
layer cirrus, J. Geophys. Res.-Atmos., 123, 6053–6069,
2018. a
Wyser, K.: Ice crystal habits and solar radiation, Tellus A, 51, 937–950, 1999. a
Xiao, H., Zhang, F., He, Q., Liu, P., Yan, F., Miao, L., and Yang, Z.:
Classification of Ice Crystal Habits Observed From Airborne Cloud Particle
Imager by Deep Transfer Learning, Earth Space Sci., 6, 1877–1886,
https://doi.org/10.1029/2019EA000636, 2019.
a
Yi, B., Yang, P., Liu, Q., van Delst, P., Boukabara, S.-A., and Weng, F.:
Improvements on the ice cloud modeling capabilities of the Community
Radiative Transfer Model, J. Geophys. Res.-Atmos., 121,
13–577, 2016. a
Short summary
Optical array probes are instruments used aboard research aircraft to capture 2D images of ice or water particles in clouds. This study presents a new tool using innovative machine learning, called convolutional neural networks, designed to identify the shape of imaged ice particles for two of these imagers, namely 2DS and PIP. Such a tool will be a very strong asset for understanding cloud microphysics. Beyond traditional evaluation metrics, human inspections were performed of unknown data.
Optical array probes are instruments used aboard research aircraft to capture 2D images of ice...
