Research article
08 May 2020
Research article | 08 May 2020
A convolutional neural network for classifying cloud particles recorded by imaging probes
Georgios Touloupas et al.
Related authors
On the drivers of droplet variability in Alpine mixed-phase clouds
Paraskevi Georgakaki, Aikaterini Bougiatioti, Jörg Wieder, Claudia Mignani, Fabiola Ramelli, Zamin A. Kanji, Jan Henneberger, Maxime Hervo, Alexis Berne, Ulrike Lohmann, and Athanasios Nenes
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1036,https://doi.org/10.5194/acp-2020-1036, 2020
Preprint under review for ACP
Short summary
Continuous secondary ice production initiated by updrafts through the melting layer in mountainous regions
Annika Lauber, Jan Henneberger, Claudia Mignani, Fabiola Ramelli, Julie T. Pasquier, Jörg Wieder, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-986,https://doi.org/10.5194/acp-2020-986, 2020
Preprint under review for ACP
Short summary
Microphysical investigation of the seeder and feeder region of an Alpine mixed-phase cloud
Fabiola Ramelli, Jan Henneberger, Robert O. David, Johannes Bühl, Martin Radenz, Patric Seifert, Jörg Wieder, Annika Lauber, Julie T. Pasquier, Ronny Engelmann, Claudia Mignani, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-772,https://doi.org/10.5194/acp-2020-772, 2020
Preprint under review for ACP
Short summary
Influence of low-level blocking and turbulence on the microphysics of a mixed-phase cloud in an inner-Alpine valley
Fabiola Ramelli, Jan Henneberger, Robert Oscar David, Annika Lauber, Julie Thérèse Pasquier, Jörg Wieder, Johannes Bühl, Patric Seifert, Ronny Engelmann, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-774,https://doi.org/10.5194/acp-2020-774, 2020
Preprint under review for ACP
Short summary
Impact of surface and near-surface processes on ice crystal concentrations measured at mountain-top research stations
Alexander Beck, Jan Henneberger, Jacob P. Fugal, Robert O. David, Larissa Lacher, and Ulrike Lohmann
Atmos. Chem. Phys., 18, 8909–8927, https://doi.org/10.5194/acp-18-8909-2018,https://doi.org/10.5194/acp-18-8909-2018, 2018
Short summary
Polarimetric radar and in situ observations of riming and snowfall microphysics during CLACE 2014
J. Grazioli, G. Lloyd, L. Panziera, C. R. Hoyle, P. J. Connolly, J. Henneberger, and A. Berne
Atmos. Chem. Phys., 15, 13787–13802, https://doi.org/10.5194/acp-15-13787-2015,https://doi.org/10.5194/acp-15-13787-2015, 2015
Short summary
The origins of ice crystals measured in mixed-phase clouds at the high-alpine site Jungfraujoch
G. Lloyd, T. W. Choularton, K. N. Bower, M. W. Gallagher, P. J. Connolly, M. Flynn, R. Farrington, J. Crosier, O. Schlenczek, J. Fugal, and J. Henneberger
Atmos. Chem. Phys., 15, 12953–12969, https://doi.org/10.5194/acp-15-12953-2015,https://doi.org/10.5194/acp-15-12953-2015, 2015
Short summary
Related subject area
Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements
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
Short summary
Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice
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
Short summary
Study of the diffraction pattern of cloud particles and the respective responses of optical array probes
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
A new method for calculating number concentrations of cloud condensation nuclei based on measurements of a three-wavelength humidified nephelometer system
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
Short summary
Evaluation of radar reflectivity factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems
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
Short summary
From pixels to patches: a cloud classification method based on a bag of micro-structures
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
Short summary
Evaluation of cloud base height measurements from Ceilometer CL31 and MODIS satellite over Ahmedabad, India
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
Short summary
Software to analyze the relationship between aerosol, clouds, and precipitation: SAMAC
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
Short summary
Assessment of cloud supersaturation by size-resolved aerosol particle and cloud condensation nuclei (CCN) measurements
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
Cited articles
Abdelmonem, A., Schnaiter, M., Amsler, P., Hesse, E., Meyer, J., and Leisner, T.: First correlated measurements of the shape and light scattering properties of cloud particles using the new Particle Habit Imaging and Polar Scattering (PHIPS) probe, Atmos. Meas. Tech., 4, 2125–2142,
https://doi.org/10.5194/amt-4-2125-2011, 2011.
a
Abdelmonem, A., Järvinen, E., Duft, D., Hirst, E., Vogt, S., Leisner, T., and Schnaiter, M.: PHIPS–HALO: the airborne Particle Habit Imaging and Polar Scattering probe – Part 1: Design and operation, Atmos. Meas. Tech., 9, 3131–3144,
https://doi.org/10.5194/amt-9-3131-2016, 2016.
a
Baumgardner, D., Jonsson, H., Dawson, W., O'Connor, D., and Newton, R.: The cloud, aerosol and precipitation spectrometer: a new instrument for cloud investigations, Atmos. Res., 59–60, 251–264,
https://doi.org/10.1016/S0169-8095(01)00119-3, 2001.
a,
b
Baumgardner, D., Abel, S. J., Axisa, D., Cotton, R., Crosier, J., Field, P., Gurganus, C., Heymsfield, A., Korolev, A., Krämer, M., Lawson, P., McFarquhar, G., Ulanowski, Z., and Um, J.: Cloud Ice Properties: In Situ Measurement Challenges, Meteor. Mon., 58, 9.1–9.23,
https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0011.1, 2017.
a,
b,
c,
d
Beck, A., Henneberger, J., Schöpfer, S., Fugal, J., and Lohmann, U.: HoloGondel: in situ cloud observations on a cable car in the Swiss Alps using a holographic imager, Atmos. Meas. Tech., 10, 459–476,
https://doi.org/10.5194/amt-10-459-2017, 2017.
a,
b,
c
Bernauer, F., Hürkamp, K., Rühm, W., and Tschiersch, J.: Snow event classification with a 2D video disdrometer – A decision tree approach, Atmos. Res., 172–173, 186–195,
https://doi.org/10.1016/j.atmosres.2016.01.001, 2016.
a
Beswick, K., Baumgardner, D., Gallagher, M., Volz-Thomas, A., Nedelec, P., Wang, K.-Y., and Lance, S.: The backscatter cloud probe – a compact low-profile autonomous optical spectrometer, Atmos. Meas. Tech., 7, 1443–1457,
https://doi.org/10.5194/amt-7-1443-2014, 2014.
a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X.: Clouds and Aerosols, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, United Kingdom and New York, NY, USA, 571–658,
https://doi.org/10.1017/CBO9781107415324.016, 2013.
a
Breiman, L.: Classification and regression trees, The Wadsworth statistics/probability series, Wadsworth, Belmont, CA, 1984. a
Cord, M.: heuritech Le Blog, available at:
https://web.archive.org/web/20171229040635/https:/blog.heuritech.com/2016/02/29/a-brief-report-of-the-heuritech-deep-learning-meetup-5/
(last access: May 2018), 2016. a
Cotton, R., Osborne, S., Ulanowski, Z., Hirst, E., Kaye, P. H., and Greenaway, R. S.: The Ability of the Small Ice Detector (SID-2) to Characterize Cloud Particle and Aerosol Morphologies Obtained during Flights of the FAAM BAe-146 Research Aircraft, J. Atmos. Ocean. Tech., 27, 290–303,
https://doi.org/10.1175/2009JTECHA1282.1, 2010.
a
Crosier, J., Bower, K. N., Choularton, T. W., Westbrook, C. D., Connolly, P. J., Cui, Z. Q., Crawford, I. P., Capes, G. L., Coe, H., Dorsey, J. R., Williams, P. I., Illingworth, A. J., Gallagher, M. W., and Blyth, A. M.: Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus, Atmos. Chem. Phys., 11, 257–273,
https://doi.org/10.5194/acp-11-257-2011, 2011.
a
Fugal, J. P. and Shaw, R. A.: Cloud particle size distributions measured with an airborne digital in-line holographic instrument, Atmos. Meas. Tech., 2, 259–271,
https://doi.org/10.5194/amt-2-259-2009, 2009.
a
Fugal, J. P., Schulz, T. J., and Shaw, R. A.: Practical methods for automated reconstruction and characterization of particles in digital in-line holograms, Meas. Sci. Technol., 20, 075501,
https://doi.org/10.1088/0957-0233/20/7/075501, 2009.
a,
b
Glorot, X. and Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy, 13–15 May 2010, PMLR, 9, 249–256, 2010. a
oodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, The MIT Press, Cambridge, 2016.
a,
b
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.
a
Hagan, M. T. and Menhaj, M. B.: Training feedforward networks with the Marquardt algorithm, IEEE T. Neural Networ., 5, 989–993,
https://doi.org/10.1109/72.329697, 1994.
a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, IEEE, 770–778, 2016. a
Henneberger, J., Fugal, J. P., Stetzer, O., and Lohmann, U.: HOLIMO II: a digital holographic instrument for ground-based in situ observations of microphysical properties of mixed-phase clouds, Atmos. Meas. Tech., 6, 2975–2987,
https://doi.org/10.5194/amt-6-2975-2013, 2013.
a,
b
Hsu, C.-W., Chang, C.-C., and Lin, C.-L.: A practical guide to support vector classification. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, available at:
https://www.csie.ntu.edu.tw/~cjlin/libsvm/ (last access: May 2019), 2003. a
Huang, G., Liu, Z., Weinberger, K. Q., and van der Maaten, L.: Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017,
IEEE, 2261–2269, 2017. a
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteor. Mon., 58, 5.1–5.50,
https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
a,
b
Lauber, A. and Touloupas, G.: A Convolutional Neural Network for Classifying Cloud Particles Recorded by Imaging Probes (Version v.1.0.0), Atmospheric Measurements Techniques, Zenodo,
https://doi.org/10.5281/zenodo.3715984, 2019.
a
Lawson, R. P., Baker, B. A., Schmitt, C. G., and Jensen, T. L.: An overview of microphysical properties of Arctic clouds observed in May and July 1998 during FIRE ACE, J. Geophys. Res., 106, 14989–15014,
https://doi.org/10.1029/2000JD900789, 2001.
a,
b
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D.: Backpropagation applied to handwritten zip code
recognition, Neural Comput., 1, 541–551, 1989. 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., 117, D16206,
https://doi.org/10.1029/2012JD017573, 2012.
a
McCoy, D. T., Tan, I., Hartmann, D. L., Zelinka, M. D., and Storelvmo, T.: On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in GCMs, J. Adv. Model. Earth Sy., 8, 650–668,
https://doi.org/10.1002/2015MS000589, 2016.
a
Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J.: Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502–6509,
https://doi.org/10.1002/2015GL064604, 2015.
a
Nair, V. and Hinton, G. E.: Rectified linear units improve restricted boltzmann machines, in: Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, June 2010 (ICML-10), 807–814, 2010. a
O'Shea, S. J., Choularton, T. W., Lloyd, G., Crosier, J., Bower, K. N., Gallagher, M., Abel, S. J., Cotton, R. J., Brown, P. R. A., Fugal, J. P., Schlenczek, O., Borrmann, S., and Pickering, J. C.: Airborne observations of the microphysical structure of two contrasting cirrus clouds, J. Geophys. Res.-Atmos., 121, 13510–13536,
https://doi.org/10.1002/2016JD025278, 2016.
a
Schlenczek, O.: Airborne and Ground-based Holographic Measurement of Hydrometeors in Liquid-phase, Mixed-phase and Ice Clouds, PhD thesis, Universitätsbibliothek Mainz, Mainz, Germany, 2018.
a,
b
Scott, D. W.: On optimal and data-based histograms, Biometrika, 66, 605–610, 1979. a
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H.: How transferable are features in deep neural networks?, Adv. Neur. In., 27, 3320–3328, 2014. a
Young, G., Connolly, P. J., Jones, H. M., and Choularton, T. W.: Microphysical sensitivity of coupled springtime Arctic stratocumulus to modelled primary ice over the ice pack, marginal ice, and ocean, Atmos. Chem. Phys., 17, 4209–4227,
https://doi.org/10.5194/acp-17-4209-2017, 2017.
a
Zhou, Y. and Chellappa, R.: Computation of optical flow using a neural network, in: IEEE International Conference on Neural Networks, San Diego, CA, USA, 24–27 July 1988, IEEE, 27, 71–78, 1988. a
