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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Numerous small ice crystals in the tropical convective storms are difficult to detect and could be potentially hazardous for commercial aircraft. This study evaluated the numerical models against the airborne observations and investigated the potential cloud processes that could lead to the production of these large numbers of small ice crystals. It is found that key microphysical processes are still lacking or misrepresented in current numerical models to realistically simulate the phenomenon.
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This study investigates properties of ice hydrometeors (shape, concentration, density, and size) in deep convective systems. The analysis focuses on similarities and differences over four locations in the tropical troposphere. It shows that measurements as a function of temperature and radar reflectivity factors tend to be similar in the four types of deep convective systems when concentrations of ice are larger than 0.1 g m-3.
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Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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Many types of halos appear in the sky. Each type of halo reflects the state of the atmosphere; therefore observing them from the ground greatly helps in understanding the state of the atmosphere. However, halos are easily obscured by the contrast of the cloud itself, making it difficult to observe them. This study describes the construction of a sky-color model for halos and a new effective algorithm to reveal halos in images.
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Atmos. Meas. Tech., 17, 1475–1495, https://doi.org/10.5194/amt-17-1475-2024, https://doi.org/10.5194/amt-17-1475-2024, 2024
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In some clouds, liquid water droplets can freeze onto ice crystals (riming). Riming leads to the formation of snowflakes. We show two ways to quantify riming using aircraft data collected in the Arctic. One aircraft had a cloud radar, while the other aircraft was measuring directly in cloud. The first method compares radar and direct observations. The second looks at snowflake shape. Both methods agree, except when there were gaps in the cloud. This improves our ability to understand riming.
Huiying Zhang, Xia Li, Fabiola Ramelli, Robert O. David, Julie Pasquier, and Jan Henneberger
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Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals, taking into account both their basic shape and physical processes. Trained on ice crystal images from the Arctic taken by a holographic camera, it correctly classifies over 92 % of the ice crystals. The more detailed insights into the components of aggregated ice crystals have the potential to improve our estimates of microphysical properties such as riming rate and ice water content.
Bu-Yo Kim, Joo Wan Cha, and Yong Hee Lee
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A camera-based imager and convolutional neural network (CNN) were used to estimate ground cloud cover. Image data from 2019 were used for training and validation, and those from 2020 were used for testing. The CNN model exhibited high performance, with an accuracy of 0.92, RMSE of 1.40 tenths, and 93% agreement with observed cloud cover within ±2 tenths' difference. It also outperformed satellites and ceilometers and proved to be the most suitable for ground-based cloud cover estimation.
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This study investigates a method for 24 h cloud cover calculation using a camera-based imager and supervised machine learning methods. The cloud cover is calculated by learning the statistical characteristics of the ratio, difference, and luminance using RGB channels of the image with a machine learning model. The proposed approach is suitable for nowcasting because it has higher learning and prediction speed than the method in which the many pixels of a 2D image are learned.
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021, https://doi.org/10.5194/amt-14-4971-2021, 2021
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A cloud particle sensor (CPS) sonde is an observing system to obtain the signals of the phase, size, and the number of cloud particles. Based on the field experiments in the Arctic regions and numerical experiments, we proposed a method to correct the CPS sonde data and found that the CPS sonde system can appropriately observe the liquid cloud if our correction method is applied.
Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
Atmos. Meas. Tech., 13, 5595–5619, https://doi.org/10.5194/amt-13-5595-2020, https://doi.org/10.5194/amt-13-5595-2020, 2020
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In this study, we developed a new algorithm for cloud classification using solar radiation and cloud base height measurements. Our objective was to develop a simple and inexpensive but effective algorithm for the needs of studies related to ecosystem and atmosphere interactions. In the present study, we used the algorithm for obtaining cloud statistics at a measurement station in southern Finland, and we discuss the advantages and shortcomings of the algorithm.
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020, https://doi.org/10.5194/amt-13-2219-2020, 2020
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Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Carola Barrientos Velasco, Hartwig Deneke, Hannes Griesche, Patric Seifert, Ronny Engelmann, and Andreas Macke
Atmos. Meas. Tech., 13, 1757–1775, https://doi.org/10.5194/amt-13-1757-2020, https://doi.org/10.5194/amt-13-1757-2020, 2020
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In the changing Arctic, quantifying the resulting variability of incoming solar radiation is important to better elucidate the net radiative effect of clouds. As part of a multidisciplinary expedition in the central Arctic held in early summer 2017, a novel network of pyranometers was deployed over an ice floe to investigate the spatiotemporal variability of solar radiation under different sky conditions. This study presents the collected data and an analysis of the spatiotemporal variability.
Sylke Boyd, Stephen Sorenson, Shelby Richard, Michelle King, and Morton Greenslit
Atmos. Meas. Tech., 12, 4241–4259, https://doi.org/10.5194/amt-12-4241-2019, https://doi.org/10.5194/amt-12-4241-2019, 2019
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How cirroform clouds affect the radiation balance of the atmosphere depends on their properties, including ice particle types such as crystals, pellets, and fragments. Ice halos form if ice particles in these clouds are in a smooth hexagonal crystalline form. This paper introduces a method to search long-term records of sky images for ice halos, as gathered by total sky imagers (TSIs). Such an analysis will allow one to explore geographical and seasonal variations in cirrus cloud particle types.
Thibault Vaillant de Guélis, Alfons Schwarzenböck, Valery Shcherbakov, Christophe Gourbeyre, Bastien Laurent, Régis Dupuy, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 12, 2513–2529, https://doi.org/10.5194/amt-12-2513-2019, https://doi.org/10.5194/amt-12-2513-2019, 2019
Michael L. Larsen and Raymond A. Shaw
Atmos. Meas. Tech., 11, 4261–4272, https://doi.org/10.5194/amt-11-4261-2018, https://doi.org/10.5194/amt-11-4261-2018, 2018
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A statistical tool frequently utilized to measure scale-dependent departures from perfect randomness is the radial distribution function. This tool has many strengths, but it is not easy to calculate for particle detections within a three-dimensional sample volume. In this manuscript, we introduce and test a new method to estimate the three-dimensional radial distribution function in realistic measurement volumes.
Jiangchuan Tao, Chunsheng Zhao, Ye Kuang, Gang Zhao, Chuanyang Shen, Yingli Yu, Yuxuan Bian, and Wanyun Xu
Atmos. Meas. Tech., 11, 895–906, https://doi.org/10.5194/amt-11-895-2018, https://doi.org/10.5194/amt-11-895-2018, 2018
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Existing chamber technologies for direct measurements of number concentration of cloud condensation nuclei (NCCN) are sophisticated and expensive. In this paper, a new method is proposed to calculate NCCN based only on measurements of a humidified nephelometer system which have accounted for influences of both aerosol size and aerosol hygroscopicity on NCCN calculation. This new method makes NCCN measurements more convenient and is capable of obtaining NCCN at lower supersaturations.
Christine Aebi, Julian Gröbner, Niklaus Kämpfer, and Laurent Vuilleumier
Atmos. Meas. Tech., 10, 4587–4600, https://doi.org/10.5194/amt-10-4587-2017, https://doi.org/10.5194/amt-10-4587-2017, 2017
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The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3–5 years. Information about fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud cover, cloud type and other atmospheric parameters have an influence on the magnitude of the longwave cloud effect as well as on the shortwave.
Emmanuel Fontaine, Delphine Leroy, Alfons Schwarzenboeck, Julien Delanoë, Alain Protat, Fabien Dezitter, Alice Grandin, John Walter Strapp, and Lyle Edward Lilie
Atmos. Meas. Tech., 10, 2239–2252, https://doi.org/10.5194/amt-10-2239-2017, https://doi.org/10.5194/amt-10-2239-2017, 2017
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In this study we evaluate a method to estimate cloud water content (CWC) knowing cloud reflectivity. Ice hydrometeors are replace by ice oblate spheroids to simulate their reflectivity. There is no assumption on the relation between mass and their size. Then, a broad range of CWCs are compared with direct measurements of CWC. The accuracy of the method is ~ ±32 %. This study is performed in areas of convective clouds where reflectivity and CWC are especially high, what makes it unique.
Hsu-Yung Cheng and Chih-Lung Lin
Atmos. Meas. Tech., 10, 199–208, https://doi.org/10.5194/amt-10-199-2017, https://doi.org/10.5194/amt-10-199-2017, 2017
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A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.
Qingyong Li, Zhen Zhang, Weitao Lu, Jun Yang, Ying Ma, and Wen Yao
Atmos. Meas. Tech., 9, 753–764, https://doi.org/10.5194/amt-9-753-2016, https://doi.org/10.5194/amt-9-753-2016, 2016
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This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.
Som Sharma, Rajesh Vaishnav, Munn V. Shukla, Prashant Kumar, Prateek Kumar, Pradeep K. Thapliyal, Shyam Lal, and Yashwant B. Acharya
Atmos. Meas. Tech., 9, 711–719, https://doi.org/10.5194/amt-9-711-2016, https://doi.org/10.5194/amt-9-711-2016, 2016
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Cloud base height observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03°N, 72.54°E), India. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and that the MODIS satellite provides accurate retrieval of high-level clouds over this region.
S. Gagné, L. P. MacDonald, W. R. Leaitch, and J. R. Pierce
Atmos. Meas. Tech., 9, 619–630, https://doi.org/10.5194/amt-9-619-2016, https://doi.org/10.5194/amt-9-619-2016, 2016
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Measurements of clouds with an aircraft are essential to understand how clouds form and how they affect the Earth's climate. These measurements are used in climate models to help predict how our climate might develop in the next century. Aircraft measurements are, however, difficult for modellers to interpret because the way they were acquired and analyzed varies from one team of scientists to the next. We present a software platform for scientists to share and compare their analysis tools.
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
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This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
A. Korolev and P. R. Field
Atmos. Meas. Tech., 8, 761–777, https://doi.org/10.5194/amt-8-761-2015, https://doi.org/10.5194/amt-8-761-2015, 2015
M. L. Krüger, S. Mertes, T. Klimach, Y. F. Cheng, H. Su, J. Schneider, M. O. Andreae, U. Pöschl, and D. Rose
Atmos. Meas. Tech., 7, 2615–2629, https://doi.org/10.5194/amt-7-2615-2014, https://doi.org/10.5194/amt-7-2615-2014, 2014
M. I. A. Berghof, G. P. Frank, S. Sjogren, and B. G. Martinsson
Atmos. Meas. Tech., 7, 877–886, https://doi.org/10.5194/amt-7-877-2014, https://doi.org/10.5194/amt-7-877-2014, 2014
A. Schwarzenboeck, G. Mioche, A. Armetta, A. Herber, and J.-F. Gayet
Atmos. Meas. Tech., 2, 779–788, https://doi.org/10.5194/amt-2-779-2009, https://doi.org/10.5194/amt-2-779-2009, 2009
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...
