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AMT | Articles | Volume 13, issue 5
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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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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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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.
Images of cloud particles give important information for improving our understanding of...
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