Articles | Volume 13, issue 5
https://doi.org/10.5194/amt-13-2219-2020
© Author(s) 2020. 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-13-2219-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2020
Research article |
| 08 May 2020
| 08 May 2020
A convolutional neural network for classifying cloud particles recorded by imaging probes
Georgios Touloupas
Institute for Machine Learning, ETH Zurich, Zurich, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Jan Henneberger
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Alexander Beck
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Aurélien Lucchi
Institute for Machine Learning, ETH Zurich, Zurich, Switzerland
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- On the drivers of droplet variability in alpine mixed-phase clouds P. Georgakaki et al. 10.5194/acp-21-10993-2021
- Classification of Cloud Particle Habits Using Transfer Learning with a Deep Convolutional Neural Network Y. Xu et al. 10.3390/atmos16030294
- Morphological classification of fine particles in transmission electron microscopy images by using pre-trained convolution neural networks J. Khadgi et al. 10.1080/02786826.2024.2322010
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- Understanding the History of Two Complex Ice Crystal Habits Deduced From a Holographic Imager J. Pasquier et al. 10.1029/2022GL100247
- Discrimination and Measurement of Droplet and Ice Crystal by Combining Digital Inline Holography With Interferometric Particle Imaging With Single Color Camera Y. Wu et al. 10.1109/TIM.2022.3147333
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- PHIPS-HALO: the airborne Particle Habit Imaging and Polar Scattering probe – Part 3: Single-particle phase discrimination and particle size distribution based on the angular-scattering function F. Waitz et al. 10.5194/amt-14-3049-2021
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- Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds G. Motos et al. 10.5194/acp-23-13941-2023
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- Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm Y. Chu et al. 10.5194/amt-18-2781-2025
- Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations J. Wieder et al. 10.5194/acp-22-9767-2022
- Conditions favorable for secondary ice production in Arctic mixed-phase clouds J. Pasquier et al. 10.5194/acp-22-15579-2022
- The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning R. Atlas et al. 10.5194/amt-14-7079-2021
Latest update: 08 Jul 2025
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.
Images of cloud particles give important information for improving our understanding of...
