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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- PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations T. Vogl et al. 10.5194/amt-17-6547-2024
- Dependence of Mass–Dimensional Relationships on Median Mass Diameter S. Ding et al. 10.3390/atmos11070756
- Performance of optical sensors for cloud measurements deployed by the ARM Aerial Facility during ACE-ENA S. Glienke et al. 10.1364/AO.491401
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- Repurposing weather modification for cloud research showcased by ice crystal growth F. Ramelli et al. 10.1093/pnasnexus/pgae402
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- Simulations of primary and secondary ice production during an Arctic mixed-phase cloud case from the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) campaign B. Schäfer et al. 10.5194/acp-24-7179-2024
- Soft Contrastive Representation Learning for Cloud-Particle Images Captured In-Flight by the New HVPS-4 Airborne Probe Y. Yassin et al. 10.1109/LGRS.2024.3506483
- Evaluating the Wegener–Bergeron–Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project N. Omanovic et al. 10.5194/acp-24-6825-2024
- Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks V. Przybylo et al. 10.1175/JTECH-D-21-0094.1
- Neural network processing of holographic images J. Schreck et al. 10.5194/amt-15-5793-2022
- Shape Classification of Cloud Particles Recorded by the 2D-S Imaging Probe Using a Convolutional Neural Network R. Zhang et al. 10.1007/s13351-023-2146-2
- Sensitivity of precipitation formation to secondary ice production in winter orographic mixed-phase clouds Z. Dedekind et al. 10.5194/acp-21-15115-2021
- Neural Network Classification of Ice-Crystal Images Observed by an Airborne Cloud Imaging Probe Z. Wu et al. 10.1080/07055900.2020.1843393
- On the drivers of droplet variability in alpine mixed-phase clouds P. Georgakaki et al. 10.5194/acp-21-10993-2021
- 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
- Continuous secondary-ice production initiated by updrafts through the melting layer in mountainous regions A. Lauber et al. 10.5194/acp-21-3855-2021
- 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
- IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme H. Zhang et al. 10.5194/amt-17-7109-2024
- 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
- Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds G. Motos et al. 10.5194/acp-23-13941-2023
- Microphysical investigation of the seeder and feeder region of an Alpine mixed-phase cloud F. Ramelli et al. 10.5194/acp-21-6681-2021
- 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: 21 Jan 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...
