Articles | Volume 18, issue 12
https://doi.org/10.5194/amt-18-2781-2025
© Author(s) 2025. 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-18-2781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jun 2025
Research article |
| 27 Jun 2025
| 27 Jun 2025
Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm
Yunpei Chu
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Department of Geosciences and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Xia Li
Institute for Machine Learning, ETH Zurich, Zurich, Switzerland
Jan Henneberger
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
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Kevin Ohneiser, Patric Seifert, Willi Schimmel, Fabian Senf, Tom Gaudek, Martin Radenz, Audrey Teisseire, Veronika Ettrichrätz, Teresa Vogl, Nina Maherndl, Nils Pfeifer, Jan Henneberger, Anna J. Miller, Nadja Omanovic, Christopher Fuchs, Huiying Zhang, Fabiola Ramelli, Robert Spirig, Anton Kötsche, Heike Kalesse-Los, Maximilian Maahn, Heather Corden, Alexis Berne, Majid Hajipour, Hannes Griesche, Julian Hofer, Ronny Engelmann, Annett Skupin, Albert Ansmann, and Holger Baars
Atmos. Chem. Phys., 25, 17363–17386, https://doi.org/10.5194/acp-25-17363-2025, https://doi.org/10.5194/acp-25-17363-2025, 2025
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This study focuses on a seeder-feeder cloud system on 8 Jan 2024 in Eriswil, Switzerland. It is shown how the interaction of these cloud systems changes the cloud microphysical properties and the precipitation patterns. A big set of advanced remote-sensing techniques and retrieval algorithms are applied, so that a detailed view on the seeder-feeder cloud system is available. The gained knowledge can be used to improve weather models and weather forecasts.
Christopher Fuchs, Fabiola Ramelli, Anna J. Miller, Nadja Omanovic, Robert Spirig, Huiying Zhang, Patric Seifert, Kevin Ohneiser, Ulrike Lohmann, and Jan Henneberger
Atmos. Chem. Phys., 25, 12177–12196, https://doi.org/10.5194/acp-25-12177-2025, https://doi.org/10.5194/acp-25-12177-2025, 2025
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We quantify diffusional ice crystal growth in natural clouds using cloud seeding experiments. We report growth rates for 14 experiments between −5.1 °C and −8.3 °C and observe strong variations depending on the cloud characteristics. Comparing our growth rates to laboratory data, we found similar temperature-dependent trends, but the laboratory rates are higher. These data fill the gap in quantitative in situ observation of ice crystal growth, helping to validate models and laboratory experiments.
Tom Gaudek, Cristofer Jimenez, Kevin Ohneiser, Christopher Fuchs, Jan Henneberger, Johannes Bühl, Andi Klamt, Albert Ansmann, Ronny Engelmann, and Patric Seifert
EGUsphere, https://doi.org/10.5194/egusphere-2025-4105, https://doi.org/10.5194/egusphere-2025-4105, 2025
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This study introduces the maximum diameter (Dmax) of precipitation particles measured by a two-dimensional video disdrometer (2DVD) as a novel parameter. Dmax is applied in a cloud seeding study during the Cloudlab campaign and, for the first time, in a MOSAiC case to evaluate the LIRAS-ice remote-sensing retrieval of in-cloud ice crystal size and number. Both quantities agreed well with the 2DVD measurements under ideal conditions, highlighting the potential of Dmax for precipitation studies.
Huiying Zhang, Fabiola Ramelli, Christopher Fuchs, Nadja Omanovic, Anna J. Miller, Robert Spirig, Zhaolong Wu, Yunpei Chu, Xia Li, Ulrike Lohmann, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4397, https://doi.org/10.5194/egusphere-2025-4397, 2025
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Ice crystals in clouds aggregate, shaping snow and rain, yet rates are hard to measure. Using cloud seeding, we sampled crystals downwind after known times. A deep-learning algorithm quantified aggregation by counting crystal components. Initial ice concentration was the main driver, confirmed by causal analysis, physics, and machine learning, though weaker than theory predicts. Temperature, size, and shape also mattered, while turbulence was negligible.
Audrey Teisseire, Patric Seifert, Kevin Ohneiser, Maximilian Maahn, Robert Spirig, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-3923, https://doi.org/10.5194/egusphere-2025-3923, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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This study evaluates the vertical distribution of particle shape (VDPS) method for identifying particle shapes and riming/aggregation using in-situ and multi-frequency radar data from the CLOUDLAB campaign. Despite non-Rayleigh scattering effects, results show that the VDPS method using a Ka-band scanning cloud radar in slanted depolarization ratio (SLDR) mode is effective for hydrometeor classification.
Kevin Ohneiser, Markus Hartmann, Heike Wex, Patric Seifert, Anja Hardt, Anna Miller, Katharina Baudrexl, Werner Thomas, Veronika Ettrichrätz, Maximilian Maahn, Tom Gaudek, Willi Schimmel, Fabian Senf, Hannes Griesche, Martin Radenz, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-3675, https://doi.org/10.5194/egusphere-2025-3675, 2025
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This study highlights the efficiency of supercooled stratus clouds to remove ice-nucleating particles (INPs). In our measurement scenarios within the planetary boundary layer lower concentrations of INP under supercooled stratus conditions were found than with temperatures above freezing. Within the free troposphere a lot more INPs were found to be available which means that the free troposphere must be taken into account as an important source of INPs.
Christopher Fuchs, Fabiola Ramelli, David Schweizer, Ulrike Lohmann, and Jan Henneberger
Atmos. Meas. Tech., 18, 2969–2986, https://doi.org/10.5194/amt-18-2969-2025, https://doi.org/10.5194/amt-18-2969-2025, 2025
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We present a new instrument based on digital in-line holography (SmHOLIMO) for in situ cloud measurements. SmHOLIMO is designed to specifically measure small cloud droplets with diameters > 3.7 μm. This way we retrieve accurate cloud droplet size distributions, which are crucial to understand the evolution and governing microphysical processes of a cloud. Results of a field study are compared to co-located measurements of a second holographic imager, microwave radiometer, and cloud radar.
Anna J. Miller, Christopher Fuchs, Fabiola Ramelli, Huiying Zhang, Nadja Omanovic, Robert Spirig, Claudia Marcolli, Zamin A. Kanji, Ulrike Lohmann, and Jan Henneberger
Atmos. Chem. Phys., 25, 5387–5407, https://doi.org/10.5194/acp-25-5387-2025, https://doi.org/10.5194/acp-25-5387-2025, 2025
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We analyzed the ability of silver iodide particles (a commonly used cloud-seeding agent) to form ice crystals in naturally occurring liquid clouds at −5 to −8 °C and found that only ≈ 0.1 %−1 % of particles nucleate ice, with a negative dependence on temperature. By contextualizing our results with previous laboratory studies, we help to bridge the gap between laboratory and field experiments, which also helps to inform future cloud-seeding projects.
Huiying Zhang, Xia Li, Fabiola Ramelli, Robert O. David, Julie Pasquier, and Jan Henneberger
Atmos. Meas. Tech., 17, 7109–7128, https://doi.org/10.5194/amt-17-7109-2024, https://doi.org/10.5194/amt-17-7109-2024, 2024
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Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals, considering both their basic shape and physical processes. Trained on ice crystal images from the Arctic taken by a holographic camera, it correctly classifies over 92 % of the ice crystals. These more detailed insights into the components of aggregated ice crystals have the potential to improve our estimates of microphysical properties such as riming rate, aggregation rate, and ice water content.
Nadja Omanovic, Sylvaine Ferrachat, Christopher Fuchs, Jan Henneberger, Anna J. Miller, Kevin Ohneiser, Fabiola Ramelli, Patric Seifert, Robert Spirig, Huiying Zhang, and Ulrike Lohmann
Atmos. Chem. Phys., 24, 6825–6844, https://doi.org/10.5194/acp-24-6825-2024, https://doi.org/10.5194/acp-24-6825-2024, 2024
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We present simulations with a high-resolution numerical weather prediction model to study the growth of ice crystals in low clouds following glaciogenic seeding. We show that the simulated ice crystals grow slower than observed and do not consume as many cloud droplets as measured in the field. This may have implications for forecasting precipitation, as the ice phase is crucial for precipitation at middle and high latitudes.
Anna J. Miller, Fabiola Ramelli, Christopher Fuchs, Nadja Omanovic, Robert Spirig, Huiying Zhang, Ulrike Lohmann, Zamin A. Kanji, and Jan Henneberger
Atmos. Meas. Tech., 17, 601–625, https://doi.org/10.5194/amt-17-601-2024, https://doi.org/10.5194/amt-17-601-2024, 2024
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We present a method for aerosol and cloud research using two uncrewed aerial vehicles (UAVs). The UAVs have a propeller heating mechanism that allows flights in icing conditions, which has so far been a limitation for cloud research with UAVs. One UAV burns seeding flares, producing a plume of particles that causes ice formation in supercooled clouds. The second UAV measures aerosol size distributions and is used for measuring the seeding plume or for characterizing the boundary layer.
Ghislain Motos, Gabriel Freitas, Paraskevi Georgakaki, Jörg Wieder, Guangyu Li, Wenche Aas, Chris Lunder, Radovan Krejci, Julie Thérèse Pasquier, Jan Henneberger, Robert Oscar David, Christoph Ritter, Claudia Mohr, Paul Zieger, and Athanasios Nenes
Atmos. Chem. Phys., 23, 13941–13956, https://doi.org/10.5194/acp-23-13941-2023, https://doi.org/10.5194/acp-23-13941-2023, 2023
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Low-altitude clouds play a key role in regulating the climate of the Arctic, a region that suffers from climate change more than any other on the planet. We gathered meteorological and aerosol physical and chemical data over a year and utilized them for a parameterization that help us unravel the factors driving and limiting the efficiency of cloud droplet formation. We then linked this information to the sources of aerosol found during each season and to processes of cloud glaciation.
Guangyu Li, Elise K. Wilbourn, Zezhen Cheng, Jörg Wieder, Allison Fagerson, Jan Henneberger, Ghislain Motos, Rita Traversi, Sarah D. Brooks, Mauro Mazzola, Swarup China, Athanasios Nenes, Ulrike Lohmann, Naruki Hiranuma, and Zamin A. Kanji
Atmos. Chem. Phys., 23, 10489–10516, https://doi.org/10.5194/acp-23-10489-2023, https://doi.org/10.5194/acp-23-10489-2023, 2023
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In this work, we present results from an Arctic field campaign (NASCENT) in Ny-Ålesund, Svalbard, on the abundance, variability, physicochemical properties, and potential sources of ice-nucleating particles (INPs) relevant for mixed-phase cloud formation. This work improves the data coverage of Arctic INPs and aerosol properties, allowing for the validation of models predicting cloud microphysical and radiative properties of mixed-phase clouds in the rapidly warming Arctic.
Julie Thérèse Pasquier, Jan Henneberger, Fabiola Ramelli, Annika Lauber, Robert Oscar David, Jörg Wieder, Tim Carlsen, Rosa Gierens, Marion Maturilli, and Ulrike Lohmann
Atmos. Chem. Phys., 22, 15579–15601, https://doi.org/10.5194/acp-22-15579-2022, https://doi.org/10.5194/acp-22-15579-2022, 2022
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It is important to understand how ice crystals and cloud droplets form in clouds, as their concentrations and sizes determine the exact radiative properties of the clouds. Normally, ice crystals form from aerosols, but we found evidence for the formation of additional ice crystals from the original ones over a large temperature range within Arctic clouds. In particular, additional ice crystals were formed during collisions of several ice crystals or during the freezing of large cloud droplets.
Guangyu Li, Jörg Wieder, Julie T. Pasquier, Jan Henneberger, and Zamin A. Kanji
Atmos. Chem. Phys., 22, 14441–14454, https://doi.org/10.5194/acp-22-14441-2022, https://doi.org/10.5194/acp-22-14441-2022, 2022
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The concentration of ice-nucleating particles (INPs) is atmospherically relevant for primary ice formation in clouds. In this work, from 12 weeks of field measurement data in the Arctic, we developed a new parameterization to predict INP concentrations applicable for pristine background conditions based only on temperature. The INP parameterization could improve the cloud microphysical representation in climate models, aiding in Arctic climate predictions.
Jörg Wieder, Nikola Ihn, Claudia Mignani, Moritz Haarig, Johannes Bühl, Patric Seifert, Ronny Engelmann, Fabiola Ramelli, Zamin A. Kanji, Ulrike Lohmann, and Jan Henneberger
Atmos. Chem. Phys., 22, 9767–9797, https://doi.org/10.5194/acp-22-9767-2022, https://doi.org/10.5194/acp-22-9767-2022, 2022
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Ice formation and its evolution in mixed-phase clouds are still uncertain. We evaluate the lidar retrieval of ice-nucleating particle concentration in dust-dominated and continental air masses over the Swiss Alps with in situ observations. A calibration factor to improve the retrieval from continental air masses is proposed. Ice multiplication factors are obtained with a new method utilizing remote sensing. Our results indicate that secondary ice production occurs at temperatures down to −30 °C.
Jörg Wieder, Claudia Mignani, Mario Schär, Lucie Roth, Michael Sprenger, Jan Henneberger, Ulrike Lohmann, Cyril Brunner, and Zamin A. Kanji
Atmos. Chem. Phys., 22, 3111–3130, https://doi.org/10.5194/acp-22-3111-2022, https://doi.org/10.5194/acp-22-3111-2022, 2022
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We investigate the variation in ice-nucleating particles (INPs) relevant for primary ice formation in mixed-phased clouds over the Alps based on simultaneous in situ observations at a mountaintop and a nearby high valley (1060 m height difference). In most cases, advection from the surrounding lower regions was responsible for changes in INP concentration, causing a diurnal cycle at the mountaintop. Our study underlines the importance of the planetary boundary layer as an INP reserve.
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., 21, 10993–11012, https://doi.org/10.5194/acp-21-10993-2021, https://doi.org/10.5194/acp-21-10993-2021, 2021
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Aerosol and cloud observations coupled with a droplet activation parameterization was used to investigate the aerosol–cloud droplet link in alpine mixed-phase clouds. Predicted droplet number, Nd, agrees with observations and never exceeds a characteristic “limiting droplet number”, Ndlim, which depends solely on σw. Nd becomes velocity limited when it is within 50 % of Ndlim. Identifying when dynamical changes control Nd variability is central for understanding aerosol–cloud interactions.
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., 21, 6681–6706, https://doi.org/10.5194/acp-21-6681-2021, https://doi.org/10.5194/acp-21-6681-2021, 2021
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Orographic mixed-phase clouds are an important source of precipitation, but the ice formation processes within them remain uncertain. Here we investigate the origin of ice crystals in a mixed-phase cloud in the Swiss Alps using aerosol and cloud data from in situ and remote sensing observations. We found that ice formation primarily occurs in cloud top generating cells. Our results indicate that secondary ice processes are active in the feeder region, which can enhance orographic precipitation.
Fabiola Ramelli, Jan Henneberger, Robert O. David, Annika Lauber, Julie T. Pasquier, Jörg Wieder, Johannes Bühl, Patric Seifert, Ronny Engelmann, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys., 21, 5151–5172, https://doi.org/10.5194/acp-21-5151-2021, https://doi.org/10.5194/acp-21-5151-2021, 2021
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Interactions between dynamics, microphysics and orography can enhance precipitation. Yet the exact role of these interactions is still uncertain. Here we investigate the role of low-level blocking and turbulence for precipitation by combining remote sensing and in situ observations. The observations show that blocked flow can induce the formation of feeder clouds and that turbulence can enhance hydrometeor growth, demonstrating the importance of local flow effects for orographic precipitation.
Annika Lauber, Jan Henneberger, Claudia Mignani, Fabiola Ramelli, Julie T. Pasquier, Jörg Wieder, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys., 21, 3855–3870, https://doi.org/10.5194/acp-21-3855-2021, https://doi.org/10.5194/acp-21-3855-2021, 2021
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An accurate prediction of the ice crystal number concentration (ICNC) is important to determine the radiation budget, lifetime, and precipitation formation of clouds. Even though secondary-ice processes can increase the ICNC by several orders of magnitude, they are poorly constrained and lack a well-founded quantification. During measurements on a mountain slope, a high ICNC of small ice crystals was observed just below 0 °C, attributed to a secondary-ice process and parametrized in this study.
Claudia Mignani, Jörg Wieder, Michael A. Sprenger, Zamin A. Kanji, Jan Henneberger, Christine Alewell, and Franz Conen
Atmos. Chem. Phys., 21, 657–664, https://doi.org/10.5194/acp-21-657-2021, https://doi.org/10.5194/acp-21-657-2021, 2021
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Most precipitation above land starts with ice in clouds. It is promoted by extremely rare particles. Some ice-nucleating particles (INPs) cause cloud droplets to already freeze above −15°C, a temperature at which many clouds begin to snow. We found that the abundance of such INPs among other particles of similar size is highest in precipitating air masses and lowest when air carries desert dust. This brings us closer to understanding the interactions between land, clouds, and precipitation.
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Short summary
Our study improves ice crystal shape classification, key for understanding weather and climate. By adding unsupervised pre-training before supervised classification, our algorithm reduces manual labeling effort while maintaining high accuracy. It outperforms fully supervised models across datasets of varying sizes and categories, showing strong generalization ability. This method improves ice crystal classification techniques, making it adaptable to different environmental datasets.
Our study improves ice crystal shape classification, key for understanding weather and climate....
