Articles | Volume 17, issue 24
https://doi.org/10.5194/amt-17-7109-2024
© Author(s) 2024. 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-17-7109-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Xia Li
CORRESPONDING AUTHOR
Institute for Machine Learning, ETH Zurich, Zurich, Switzerland
Fabiola Ramelli
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Robert O. David
Department of Geosciences, University of Oslo, Oslo, Norway
Julie Pasquier
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Meteomatics AG, St. Gallen, Switzerland
Jan Henneberger
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Related authors
Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger
Atmos. Meas. Tech., 18, 2781–2801, https://doi.org/10.5194/amt-18-2781-2025, https://doi.org/10.5194/amt-18-2781-2025, 2025
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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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2482, https://doi.org/10.5194/egusphere-2025-2482, 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.
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.
Christopher Fuchs, Fabiola Ramelli, Anna J. Miller, Nadja Omanovic, Robert Spirig, Huiying Zhang, Patric Seifert, Kevin Ohneiser, Ulrike Lohmann, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-688, https://doi.org/10.5194/egusphere-2025-688, 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. This data fills the gap in quantitative in situ observation of ice crystal growth, helping to validate models and laboratory experiments.
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.
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
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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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.
Filip Severin von der Lippe, Tim Carlsen, Trude Storelvmo, and Robert Oscar David
EGUsphere, https://doi.org/10.5194/egusphere-2025-3711, https://doi.org/10.5194/egusphere-2025-3711, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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This paper investigates how clouds associated with Arctic marine cold air outbreaks (CAOs) respond to climate change. By utilizing machine learning methods and remote sensing data from the past 25 years, the study identifies trends indicating a shortening of the CAO season. This has implications for the Arctic energy balance, underscoring the importance of further investigating these clouds to understand the trajectory of future Arctic climate.
Gianluca Di Natale, Helen Brindley, Laura Warwick, Sanjeevani Panditharatne, Ping Yang, Robert Oscar David, Tim Carlsen, Sorin Nicolae Vâjâiac, Alex Vlad, Sorin Ghemulet, Richard Bantges, Andreas Foth, Martin Flügge, Reidar Lyngra, Hilke Oetjen, Dirk Schuettemeyer, Luca Palchetti, and Jonathan Murray
EGUsphere, https://doi.org/10.5194/egusphere-2025-3547, https://doi.org/10.5194/egusphere-2025-3547, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Cirrus clouds play a vital role in regulating the energy balance of our planet. Unfortunately, these are still not completely understood representing the major source of error in the predictive performance of climate models. We show that a good consinstency between in situ measurements of cirrus cloud microphysics and remote sensing observations from ground base is achievable by simulating the emitted spectrum with the current parameterization of cirrus optical properties.
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.
Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger
Atmos. Meas. Tech., 18, 2781–2801, https://doi.org/10.5194/amt-18-2781-2025, https://doi.org/10.5194/amt-18-2781-2025, 2025
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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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2482, https://doi.org/10.5194/egusphere-2025-2482, 2025
Short summary
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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.
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
Short summary
Short summary
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.
Christopher Fuchs, Fabiola Ramelli, Anna J. Miller, Nadja Omanovic, Robert Spirig, Huiying Zhang, Patric Seifert, Kevin Ohneiser, Ulrike Lohmann, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-688, https://doi.org/10.5194/egusphere-2025-688, 2025
Short summary
Short summary
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. This data fills the gap in quantitative in situ observation of ice crystal growth, helping to validate models and laboratory experiments.
Astrid B. Gjelsvik, Robert O. David, Tim Carlsen, Franziska Hellmuth, Stefan Hofer, Zachary McGraw, Harald Sodemann, and Trude Storelvmo
Atmos. Chem. Phys., 25, 1617–1637, https://doi.org/10.5194/acp-25-1617-2025, https://doi.org/10.5194/acp-25-1617-2025, 2025
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Ice formation in clouds has a substantial impact on radiation and precipitation and must be realistically simulated in order to understand present and future Arctic climate. Rare aerosols known as ice-nucleating particles can play an important role in cloud ice formation, but their representation in global climate models is not well suited for the Arctic. In this study, the simulation of cloud phase is improved when the representation of these particles is constrained by Arctic observations.
Franziska Hellmuth, Tim Carlsen, Anne Sophie Daloz, Robert Oscar David, Haochi Che, and Trude Storelvmo
Atmos. Chem. Phys., 25, 1353–1383, https://doi.org/10.5194/acp-25-1353-2025, https://doi.org/10.5194/acp-25-1353-2025, 2025
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This article compares the occurrence of supercooled liquid-containing clouds (sLCCs) and their link to surface snowfall in CloudSat–CALIPSO, ERA5, and the CMIP6 models. Significant discrepancies were found, with ERA5 and CMIP6 consistently overestimating sLCC and snowfall frequency. This bias is likely due to cloud microphysics parameterization. This conclusion has implications for accurately representing cloud phase and snowfall in future climate projections.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Britta Schäfer, Robert Oscar David, Paraskevi Georgakaki, Julie Thérèse Pasquier, Georgia Sotiropoulou, and Trude Storelvmo
Atmos. Chem. Phys., 24, 7179–7202, https://doi.org/10.5194/acp-24-7179-2024, https://doi.org/10.5194/acp-24-7179-2024, 2024
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Mixed-phase clouds, i.e., clouds consisting of ice and supercooled water, are very common in the Arctic. However, how these clouds form is often not correctly represented in standard weather models. We show that both ice crystal concentrations in the cloud and precipitation from the cloud can be improved in the model when aerosol concentrations are prescribed from observations and when more processes for ice multiplication, i.e., the production of new ice particles from existing ice, are added.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Sorin Nicolae Vâjâiac, Andreea Calcan, Robert Oscar David, Denisa-Elena Moacă, Gabriela Iorga, Trude Storelvmo, Viorel Vulturescu, and Valeriu Filip
Atmos. Meas. Tech., 14, 6777–6794, https://doi.org/10.5194/amt-14-6777-2021, https://doi.org/10.5194/amt-14-6777-2021, 2021
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Warm clouds (with liquid droplets) play an important role in modulating the amount of incoming solar radiation to Earth’s surface and thus the climate. The most efficient way to study them is by in situ optical measurements. This paper proposes a new methodology for providing more detailed and reliable structural analyses of warm clouds through post-flight processing of collected data. The impact fine aerosol incorporation in water droplets might have on such measurements is also discussed.
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.
Anna J. Miller, Killian P. Brennan, Claudia Mignani, Jörg Wieder, Robert O. David, and Nadine Borduas-Dedekind
Atmos. Meas. Tech., 14, 3131–3151, https://doi.org/10.5194/amt-14-3131-2021, https://doi.org/10.5194/amt-14-3131-2021, 2021
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To characterize atmospheric ice nuclei, we present (1) the development of our home-built droplet freezing technique (DFT), which involves the Freezing Ice Nuclei Counter (FINC), (2) an intercomparison campaign using NX-illite and an ambient sample with two other DFTs, and (3) the application of lignin as a soluble and commercial ice nuclei standard with three DFTs. We further compiled the growing number of DFTs in use for atmospheric ice nucleation since 2000 and add FINC.
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.
Anna J. Miller, Killian P. Brennan, Claudia Mignani, Jörg Wieder, Assaf Zipori, Robert O. David, and Nadine Borduas-Dedekind
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-361, https://doi.org/10.5194/amt-2020-361, 2020
Preprint withdrawn
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For characterizing atmospheric ice nuclei, we present (1) the development of our home-built droplet freezing technique (DFT), the Freezing Ice Nuclei Counter (FINC), (2) an intercomparison campaign using NX-illite and an ambient sample with three DFTs, and (3) the application of lignin as a soluble and commercial ice nuclei standard with four DFTs. We further compiled the growing number of DFTs in use for atmospheric ice nucleation since 2000, to which we add FINC.
Cited articles
Albawi, S., Mohammed, T. A., and Al-Zawi, S.: Understanding of a convolutional neural network, in: 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, 21–23 August 2017, IEEE, 1–6, https://doi.org/10.1109/ICEngTechnol.2017.8308186, 2017. a, b
Arlot, S. and Celisse, A.: A survey of cross-validation procedures for model selection, Statistics Surveys, 4, 40–79, https://doi.org/10.1214/09-SS054, 2010. a, b
Bishop, C. M. and Nasrabadi, N. M.: Pattern recognition and machine learning, Springer, 4, 2 pp., ISBN 978-1-4939-3843-8, 2016. a
Browne, M. W.: Cross-Validation Methods, J. Math. Psychol., 44, 108–132, https://doi.org/10.1006/jmps.1999.1279, 2000. a
Cunningham, R. M.: Analysis of particle spectral data from optical array (PMS) 1D and 2D sensors, in: Proceedings of the Symposium on Meteorological Observations and Instrumentation (4th), 10–14 April 1978, Denver, CO, 1978. a
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.: ImageNet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009, IEEE, 248–255, https://doi.org/10.1109/CVPR.2009.5206848, 2009. a
Ding, J., Xue, N., Long, Y., Xia, G.-S., and Lu, Q.: Learning RoI transformer for oriented object detection in aerial images, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15–20 June 2019, IEEE, 2849–2858, https://doi.org/10.1109/CVPR.2019.00296, 2019. a
Duroure, C., Larsen, H., Isaka, H., and Personne, P.: 2D image population analysis, Atmos. Res., 34, 195–205, https://doi.org/10.1016/0169-8095(94)90091-4, 1994. a
Ehrlich, A., Wendisch, M., Bierwirth, E., Herber, A., and Schwarzenböck, A.: Ice crystal shape effects on solar radiative properties of Arctic mixed-phase clouds – Dependence on microphysical properties, Atmos. Res., 88, 266–276, https://doi.org/10.1016/j.atmosres.2007.11.018, 2008. a
Findeisen, W.: Kolloid meteorologische Vorgänge bei Neiderschlagsbildung, Meteor. Z, 55, 121–133, 1938. a
Flanner, M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J.: Present-day climate forcing and response from black carbon in snow, J. Geophys. Res.-Atmos., 112, D11202, https://doi.org/10.1029/2006JD008003, 2007. a, b
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., and Chen, T.: Recent advances in convolutional neural networks, Pattern Recogn., 77, 354–377, 2018. a
Han, J., Ding, J., Li, J., and Xia, G.-S.: Align deep features for oriented object detection, IEEE T. Geosci. Remote, 60, 1–11, 2021. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html (last access: 6 December 2024), 2016. a, b, c, d
Heymsfield, A.: Ice crystal terminal velocities, J. Atmos. Sci., 29, 1348–1357, 1972. a
Jabbar, H. and Khan, R. Z.: Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study), Computer Science, Communication and Instrumentation Devices, 70, 978–981, 2015. a
Jaffeux, L., Schwarzenböck, A., Coutris, P., and Duroure, C.: Ice crystal images from optical array probes: classification with convolutional neural networks, Atmos. Meas. Tech., 15, 5141–5157, https://doi.org/10.5194/amt-15-5141-2022, 2022. a, b, c
Järvinen, E., Jourdan, O., Neubauer, D., Yao, B., Liu, C., Andreae, M. O., Lohmann, U., Wendisch, M., McFarquhar, G. M., Leisner, T., and Schnaiter, M.: Additional global climate cooling by clouds due to ice crystal complexity, Atmos. Chem. Phys., 18, 15767–15781, https://doi.org/10.5194/acp-18-15767-2018, 2018. a, b
Jensen, A. A., Harrington, J. Y., Morrison, H., and Milbrandt, J. A.: Predicting ice shape evolution in a bulk microphysics model, J. Atmos. Sci., 74, 2081–2104, 2017. a
Kalina, M. and Puxbaum, H.: A study of the influence of riming of ice crystals on snow chemistry during different seasons in precipitating continental clouds, Atmos. Environ., 28, 3311–3328, 1994. a
Keppas, S. C., Crosier, J., Choularton, T., and Bower, K.: Ice lollies: An ice particle generated in supercooled conveyor belts, Geophys. Res. Lett., 44, 5222–5230, 2017. a
Khvorostyanov, V. I. and Curry, J. A.: Terminal velocities of droplets and crystals: Power laws with continuous parameters over the size spectrum, J. Atmos. Sci., 59, 1872–1884, 2002. a
Kikuchi, K., Kameda, T., Higuchi, K., and Yamashita, A.: A global classification of snow crystals, ice crystals, and solid precipitation based on observations from middle latitudes to polar regions, Atmos. Res., 132, 460–472, 2013. a
Korolev, A., Isaac, G., and Hallett, J.: Ice particle habits in Arctic clouds, Geophys. Res. Lett., 26, 1299–1302, 1999. a
Korolev, A., Isaac, G., and Hallett, J.: Ice particle habits in stratiform clouds, Q. J. Roy. Meteor. Soc., 126, 2873–2902, 2000. a
Le-Khac, P. H., Healy, G., and Smeaton, A. F.: Contrastive representation learning: A framework and review, IEEE Access, 8, 193907–193934, 2020. a
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects, IEEE T. Neur. Net. Lear., 33, 6999–7019, https://doi.org/10.1109/TNNLS.2021.3084827, 2021. a
Libbrecht, K.: Ken Libbrecht's Field Guide to Snowflakes, Voyageur Press, ISBN 9781616739478, 2016. a
Lindqvist, H., Muinonen, K., Nousiainen, T., Um, J., McFarquhar, G. M., Haapanala, P., Makkonen, R., and Hakkarainen, H.: Ice-cloud particle habit classification using principal components, J. Geophys. Res.-Atmos., 117, D16206, https://doi.org/10.1029/2012JD017573, 2012. a
Lohmann, U., Lüönd, F., and Mahrt, F.: An introduction to clouds: From the microscale to climate, Cambridge University Press, https://doi.org/10.1017/CBO9781139087513, 2016. a
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's radiation budget, J. Geophys. Res.-Atmos., 122, 2559–2578, 2017. a
Mitchell, D. L.: Use of mass-and area-dimensional power laws for determining precipitation particle terminal velocities, J. Atmos. Sci., 53, 1710–1723, 1996. a
Mitchell, D. L., Zhang, R., and Pitter, R. L.: Mass-dimensional relationships for ice particles and the influence of riming on snowfall rates, J. Appl. Meteorol. Clim., 29, 153–163, 1990. a
Mosimann, L., Steiner, M., and Henrich, W.: Prediction of snow crystal shape and riming by vertical Doppler radar, Atmos. Res., 29, 85–98, 1993. a
Pan, S. J. and Yang, Q.: A survey on transfer learning, IEEE T. Knowl. Data En., 22, 1345–1359, 2009. a
Pasquier, J. T., David, R. O., Freitas, G., Gierens, R., Gramlich, Y., Haslett, S., Li, G., Schäfer, B., Siegel, K., Wieder, J., Adachi, K., Belosi, F., Carlsen, T., Decesari, S., Ebell, K., Gilardoni, S., Gysel-Beer, M., Henneberger, J., Inoue, J., Kanji, Z. A., Koike, M., Kondo, Y., Krejci, R., Lohmann, U., Maturilli, M., Mazzolla, M., Modini, R., Mohr, C., Motos, G., Nenes, A., Nicosia, A., Ohata, S., Paglione, M., Park, S., Pileci, R. E., Ramelli, F., Rinaldi, M., Ritter, C., Sato, K., Storelvmo, T., Tobo, Y., Traversi, R., Viola, A., and Zieger, P.: The Ny-Ålesund aerosol cloud experiment (NASCENT): Overview and first results, B. Am. Meteorol. Soc., 103, E2533–E2558, 2022a. a, b, c
Pasquier, J. T., Henneberger, J., Ramelli, F., Lauber, A., David, R. O., Wieder, J., Carlsen, T., Gierens, R., Maturilli, M., and Lohmann, U.: Conditions favorable for secondary ice production in Arctic mixed-phase clouds, Atmos. Chem. Phys., 22, 15579–15601, https://doi.org/10.5194/acp-22-15579-2022, 2022b. a, b, c
Pasquier, J. T., Henneberger, J., Korolev, A., Ramelli, F., Wieder, J., Lauber, A., Li, G., David, R. O., Carlsen, T., Gierens, R., Maturilli, M., and Lohmann, U.: Understanding the history of two complex ice crystal habits deduced from a holographic imager, Geophys. Res. Lett., 50, e2022GL100247, https://doi.org/10.1029/2022GL100247, 2023. a, b
Praz, C., Roulet, Y.-A., and Berne, A.: Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera, Atmos. Meas. Tech., 10, 1335–1357, https://doi.org/10.5194/amt-10-1335-2017, 2017. a
Rahman, M. M., Quincy, E. A., Jacquot, R. G., and Magee, M. J.: Feature Extraction and Selection for Pattern Recognition of Two-Dimensional Hydrometeor Images, J. Appl. Meteorol. Clim., 20, 521–535, https://doi.org/10.1175/1520-0450(1981)020<0521:FEASFP>2.0.CO;2, 1981. a
Ramelli, F., Beck, A., Henneberger, J., and Lohmann, U.: Using a holographic imager on a tethered balloon system for microphysical observations of boundary layer clouds, Atmos. Meas. Tech., 13, 925–939, https://doi.org/10.5194/amt-13-925-2020, 2020. a
Rawat, W. and Wang, Z.: Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review, Neural Comput., 29, 2352–2449, https://doi.org/10.1162/neco_a_00990, 2017. a
Schmitt, C. G. and Heymsfield, A. J.: Observational quantification of the separation of simple and complex atmospheric ice particles, Geophys. Res. Lett., 41, 1301–1307, 2014. a
Stacke, K., Eilertsen, G., Unger, J., and Lundström, C.: Measuring domain shift for deep learning in histopathology, IEEE J. Biomed. Health, 25, 325–336, 2020. a
Sterzinger, L. J. and Igel, A. L.: The Effects of Ice Habit on Simulated Orographic Snowfall, J. Hydrometeorol., 22, 1649–1661, 2021. a
Sun, Z. and Shine, K. P.: Studies of the radiative properties of ice and mixed-phase clouds, Q. J. Roy. Meteor. Soc., 120, 111–137, https://doi.org/10.1002/qj.49712051508, 1994. a
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J.: Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?, IEEE T. Med. Imaging, 35, 1299–1312, https://doi.org/10.1109/TMI.2016.2535302, 2016. a
Touloupas, G., Lauber, A., Henneberger, J., Beck, A., and Lucchi, A.: A convolutional neural network for classifying cloud particles recorded by imaging probes, Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020, 2020. a, b, c, d
Wang, P. K. and Ji, W.: Collision efficiencies of ice crystals at low–intermediate Reynolds numbers colliding with supercooled cloud droplets: A numerical study, J. Atmos. Sci., 57, 1001–1009, 2000. a
Wegener, A.: Thermodynamik der Atmosphäre, JA Barth, https://books.google.ch/books?id=BMMxAAAAMAAJ (last access: 6 December 2024), 1911. a
Woods, C. P., Stoelinga, M. T., and Locatelli, J. D.: The IMPROVE-1 storm of 1–2 February 2001. Part III: Sensitivity of a mesoscale model simulation to the representation of snow particle types and testing of a bulk microphysical scheme with snow habit prediction, J. Atmos. Sci., 64, 3927–3948, 2007. a
Yang, P., Liou, K.-N., Bi, L., Liu, C., Yi, B., and Baum, B. A.: On the radiative properties of ice clouds: Light scattering, remote sensing, and radiation parameterization, Adv. Atmos. Sci., 32, 32–63, 2015. a
Zhang, H., Binder, A., Pasquier, J., Krummenacher, B., Ramelli, F., Storelvmo, T., David, R. O., and Henneberger, J.: Deep-learning based classification of ice crystals: habits and microphysical processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8098, https://doi.org/10.5194/egusphere-egu22-8098, 2022. a
Zhang, H., Li, X., Ramelli, F., David, R., Villinger, J., and Henneberger, J.: Code for publication “IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme”, Zenodo [code], https://doi.org/10.5281/zenodo.14288779, 2024a. a
Zhang, H., Li, X., Ramelli, F., David, R., Villinger, J., and Henneberger, J.: Data for publication “IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme”, Zenodo [data set], https://doi.org/10.5281/zenodo.14288739, 2024b. a
Zhang, H., Li, X., Ramelli, F., David, R., Villinger, J., and Henneberger, J.: Models for publication “IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme”, Zenodo [code], https://doi.org/10.5281/zenodo.14288804, 2024c. a
Zhao, Z.-Q., Zheng, P., Xu, S.-T., and Wu, X.: Object detection with deep learning: A review, IEEE T. Neur. Net. Lear., 30, 3212–3232, 2019. a
Zou, Z., Chen, K., Shi, Z., Guo, Y., and Ye, J.: Object detection in 20 years: A survey, arXiv [preprint], https://doi.org/10.48550/arXiv.1905.05055, 18 January 2023. a
Short summary
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.
Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals,...