Articles | Volume 13, issue 5
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020
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.

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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.: Observing the Microstructure of Orographic Clouds with HoloGondel, PhD thesis, ETH Zurich, Zurich, https://doi.org/10.3929/ethz-b-000250847, 2017. a
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Short summary
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.