Jan Henneberger

Last Name

Henneberger

First Name

Jan

ORCID

0000-0001-6979-3174

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03690 - Lohmann, Ulrike / Lohmann, Ulrike

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Publications 1 - 10 of 33

Two new multirotor uncrewed aerial vehicles (UAVs) for glaciogenic cloud seeding and aerosol measurements within the CLOUDLAB project
Miller, Anna J.; Ramelli, Fabiola; Fuchs, Christopher; et al. (2024)
Atmospheric Measurement Techniques
Uncrewed aerial vehicles (UAVs) have become widely used in a range of atmospheric science research applications. Because of their small size, flexible range of motion, adaptability, and low cost, multirotor UAVs are especially well-suited for probing the lower atmosphere. However, their use so far has been limited to conditions outside of clouds, first because of the difficulty of flying beyond visual line of sight and second because of the challenge of flying in icing conditions in supercooled clouds. Here, we present two UAVs for cloud microphysical research: one UAV (the measurement UAV) equipped with a Portable Optical Particle Spectrometer (POPS) and meteorological sensors to probe the aerosol and meteorological properties in the boundary layer and one UAV (the seeding UAV) equipped with seeding flares to produce a plume of particles that can nucleate ice in supercooled clouds. A propeller heating mechanism on both UAVs allows for operating in supercooled clouds with icing conditions. These UAVs are an integral part of the CLOUDLAB project in which glaciogenic cloud seeding of supercooled low stratus clouds is utilized for studying aerosol–cloud interactions and ice crystal formation and growth. In this paper, we first show validations of the POPS on board the measurement UAV, demonstrating that the rotor turbulence has a small effect on measured particle number concentrations. We then exemplify the applicability for profiling the planetary boundary layer, as well as for sampling and characterizing aerosol plumes, in this case, the seeding plume. We also present a new method for filtering out high-concentration data to ensure good data quality of POPS. We explain the different flight patterns that are possible for both UAVs, namely horizontal or vertical leg patterns or hovering, with an extensive and flexible parameter space for designing the flight patterns according to our scientific goals. Finally, we show two examples of seeding experiments: first characterizing an out-of-cloud seeding plume with the measurement UAV flying horizontal transects through the plume and, second, characterizing an in-cloud seeding plume with downstream measurements from a POPS and a holographic imager mounted on a tethered balloon. Particle number concentrations and particle number size distributions of the seeding plume from the experiments reveal that we can successfully produce and measure the seeding plume, both in-cloud (with accompanying elevated ice crystal number concentrations) and out-of-cloud. The methods presented here will be useful for probing the lower atmosphere, for characterizing aerosol plumes, and for deepening our cloud microphysical understanding through cloud seeding experiments, all of which have the potential to benefit the atmospheric science community.
Putting the spotlight on small cloud droplets with SmHOLIMO - a new holographic imager for in situ measurements of clouds
Fuchs, Christopher; Ramelli, Fabiola; Schweizer, David; et al. (2025)
Atmospheric Measurement Techniques
The microstructure of liquid and mixed-phase clouds is characterized by the cloud droplet size distribution (CDSD), which influences the cloud evolution and its interaction with radiation. However, state-of-the-art cloud probes still face challenges because they require either platforms that move at constant speed or inlets that can directly alter the actual CDSD. Therefore, precise and accurate in situ measurements of CDSDs, especially of cloud droplets smaller than 6 µm, are still lacking. This can lead to uncertainties in the microphysics and thus in weather and climate models, which are based on parameterizations often derived from these measurements. We present a new in situ instrument, the Small Holographic Imager for Microscopic Objects (SmHOLIMO), specifically designed to measure a broad spectrum of the CDSDs, i.e., from 3.7 to ≈100 µm, with a sample volume rate of 2.5 cm3 s−1. Thereby, SmHOLIMO pushes the resolution limit towards the limits seen with forward-scattering probes, while still maintaining the advantages of open-path holography, i.e., a well-defined sample volume (operation at variable wind speed); no need for an inlet; independence of particle size, phase, refractive index, and shape; and the potential of spatial analyses. After calibrating SmHOLIMO in the laboratory, the instrument was deployed in the field, on a tethered balloon system, probing a dissipating low stratus. We demonstrate its ability to measure the cloud microphysical properties at high spatio-temporal resolution. Furthermore, we compare the SmHOLIMO observations to those of another holographic imager (resolution: 6 µm) and to co-located remote sensing measurements. We unequivocally show the importance of SmHOLIMO's skills to capture the lower tail of the CDSD, which significantly affects the derived quantities of cloud droplet mean diameter (up to 1.6 times smaller), number concentration (up to 4 times higher), and cloud optical depth (up to 2.7 times higher). SmHOLIMO's high-resolution in situ data of cloud droplets will help us to better interpret observations and to refine the representation of clouds in climate and weather models.
A convolutional neural network for classifying cloud particles recorded by imaging probes
Touloupas, Georgios; Lauber, Annika; Henneberger, Jan; et al. (2020)
Atmospheric Measurement Techniques
During typical field campaigns, millions of cloud particle images are captured with imaging probes. Our interest lies in classifying these particles in order to compute the statistics needed for understanding clouds. Given the large volume of collected data, this raises the need for an automated classification approach. Traditional classification methods that require extracting features manually (e.g., decision trees and support vector machines) show reasonable performance when trained and tested on data coming from a unique dataset. However, they often have difficulties in generalizing to test sets coming from other datasets where the distribution of the features might be significantly different. In practice, we found that for holographic imagers each new dataset requires labeling a huge amount of data by hand using those methods. Convolutional neural networks have the potential to overcome this problem due to their ability to learn complex nonlinear models directly from the images instead of pre-engineered features, as well as by relying on powerful regularization techniques. We show empirically that a convolutional neural network trained on cloud particles from holographic imagers generalizes well to unseen datasets. Moreover, fine tuning the same network with a small number (256) of training images improves the classification accuracy. Thus, the automated classification with a convolutional neural network not only reduces the hand-labeling effort for new datasets but is also no longer the main error source for the classification of small particles.
Quantifying ice crystal growth rates in natural clouds from glaciogenic cloud seeding experiments
Fuchs, Christopher; Ramelli, Fabiola; Miller, Anna J.; et al. (2025)
Atmospheric Chemistry and Physics
Ice crystals are essential in the evolution of mixed-phase clouds, as ice crystals can quickly grow to large sizes by vapor diffusion and thereby trigger precipitation formation. Vapor diffusional growth rates of ice crystals were quantitatively studied in the laboratory for several decades, forming the basis of various ice crystal growth models. Since field measurements generally only provide snapshots that lack information on ice crystal age or changes induced by cloud processes, significant gaps remain in quantitative field observations, impeding the validation of laboratory experiments and models. Our study addresses this gap through innovative glaciogenic cloud seeding experiments in persistent low-level stratus clouds in the CLOUDLAB project. The controllability and repeatability of our seeding experiments facilitates the quantification of diffusional ice crystal growth rates in natural clouds via in situ measurements. We report growth rates of 0.17–0.81 µm s−1 (major axis of pristine ice crystals) from 14 seeding experiments between −5.1 and −8.3 °C. We also observe how microphysical characteristics induce strong variations in the growth rates, e.g., reduced growth rates in seeding-induced regions of high ice crystal number concentrations. For better comparison to laboratory and non-seeded clouds, we developed two filtering methods to isolate growth conditions less affected by the experimental setup. The comparison shows that the temperature-dependent growth rate variations align with laboratory data, whereas absolute laboratory values are higher. Our findings provide valuable insights into the vapor diffusional growth of ice crystals in natural clouds and connect in situ observations with laboratory and modeling studies.
The Ny-Ålesund Aerosol Cloud Experiment (NASCENT) Overview and First Results
Pasquier, Julie T.; David, Robert O.; Freitas, Gabriel P.; et al. (2022)
Bulletin of the American Meteorological Society
The Arctic is warming at more than twice the rate of the global average. This warming is influenced by clouds, which modulate the solar and terrestrial radiative fluxes and, thus, determine the surface energy budget. However, the interactions among clouds, aerosols, and radiative fluxes in the Arctic are still poorly understood. To address these uncertainties, the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) study was conducted from September 2019 to August 2020 in Ny-Ålesund, Svalbard. The campaign's primary goal was to elucidate the life cycle of aerosols in the Arctic and to determine how they modulate cloud properties throughout the year. In situ and remote sensing observations were taken on the ground at sea level, at a mountaintop station, and with a tethered balloon system. An overview of the meteorological and the main aerosol seasonality encountered during the NASCENT year is introduced, followed by a presentation of first scientific highlights. In particular, we present new findings on aerosol physicochemical and molecular properties. Further, the role of cloud droplet activation and ice crystal nucleation in the formation and persistence of mixed-phase clouds, and the occurrence of secondary ice processes, are discussed and compared to the representation of cloud processes within the regional Weather Research and Forecasting Model. The paper concludes with research questions that are to be addressed in upcoming NASCENT publications.
Multirotor UAV icing correlated to liquid water content measurements in natural supercooled clouds
Miller, Anna J.; Fuchs, Christopher; Omanovic, Nadja; et al. (2024)
Cold Regions Science and Technology
Atmospheric icing, the accumulation of ice on surfaces, is a severe concern for the aviation industry. Deicing and icing prediction tools are necessary for pilots to ensure flight safety, and while there is established technology for large aircraft icing, more research is needed for smaller uncrewed aerial vehicles (UAVs). Here, we present measurements from 59 flights of a multirotor UAV into wintertime low stratus clouds of temperatures between − 3 and − 10 ◦C. The UAV is equipped with rotor heating to allow flights up to 10 min in icing conditions. Icing severity was quantified by using the rate of increase in battery current during icing, and was then compared with simultaneous, co-located measurements of liquid water content (LWC). LWC measurements were (a) calculated from cloud droplets measured with an in situ holographic imager on a tethered balloon system and (b) retrieved from remote sensing observations (microwave radiometer, ceilometer, cloud radar). We show that, for these environmental conditions, icing was strongly positively correlated to LWC over an LWC range of 0.02 to 0.5 g m− 3 , independent of temperature and mean droplet size, though droplets > 50 μm in diameter may contribute to increased icing severity. We also show that the LWC retrieved from remote sensing agrees well with the in situ measurements, indicating that remote sensing measurements can effectively be used to assess icing conditions. These are the first known measurements of multirotor UAV icing with co-located LWC measurements in natural clouds.
Understanding the History of Two Complex Ice Crystal Habits Deduced From a Holographic Imager
Pasquier, Julie T.; Henneberger, Jan; Korolev, Alexei; et al. (2023)
Geophysical Research Letters
The sizes and shapes of ice crystals influence the radiative properties of clouds, as well as precipitation initiation and aerosol scavenging. However, ice crystal growth mechanisms remain only partially characterized. We present the growth processes of two complex ice crystal habits observed in Arctic mixed-phase clouds during the Ny-Ålesund AeroSol Cloud ExperimeNT campaign. First, are capped-columns with multiple columns growing out of the plates' corners that we define as columns on capped-columns. These ice crystals originated from cycling through the columnar and plate temperature growth regimes, during their vertical transport by in-cloud circulation. Second, is aged rime on the surface of ice crystals having grown into faceted columns or plates depending on the environmental conditions. Despite their complexity, the shapes of these ice crystals allow to infer their growth history and provide information about the in-cloud conditions. Additionally, these ice crystals exhibit complex shapes and could enhance aggregation and secondary ice production.
Seeding of Supercooled Low Stratus Clouds with a UAV to Study Microphysical Ice Processes: An Introduction to the CLOUDLAB Project
Henneberger, Jan; Ramelli, Fabiola; Spirig, Robert; et al. (2023)
Bulletin of the American Meteorological Society
Ice formation and growth processes play a crucial role in the evolution of cloud systems and the formation of precipitation. However, the initial formation and growth of ice crystals are challenging to study in the real atmosphere resulting in uncertainties in weather forecasts and climate projections. The CLOUDLAB project tackles this problem by using supercooled stratus clouds as a natural laboratory for targeted glaciogenic cloud seeding to advance the understanding of ice processes: Ice nucleating particles are injected from an uncrewed aerial vehicle (UAV) into supercooled stratus clouds to induce ice crystal formation and subsequent growth processes. Microphysical changes induced by seeding are measured 3-15 min downstream of the seeding location using in situ and ground-based remote sensing instrumentation. The novel application of seeding with a multirotor UAV combined with the persistent nature of stratus clouds enables repeated seeding experiments under similar and well-constrained initial conditions. This article describes the scientific goals, experimental design, and first results of CLOUDLAB. First, the seeding plume is characterized by using measurements of a UAV equipped with an optical particle counter. Second, the seeding-induced microphysical changes observed by cloud radars and a tethered balloon system are presented. The seeding signatures were detected by regions of increased radar reflectivity (>-20 dBZ), which were 10-20 dBZ higher than the natural background. Simultaneously, high concentrations of seeding particles and ice crystals (up to 2,000 L-¹) were observed. A cloud seeding case was simulated with the numerical weather model ICON to contextualize the findings.
Polarimetric radar and in situ observations of riming and snowfall microphysics during CLACE 2014
Grazioli, Jacopo; Lloyd, Glynn; Panziera, Luca; et al. (2015)
Atmospheric Chemistry and Physics
This study investigates the microphysics of winter alpine snowfall occurring in mixed-phase clouds in an inner-Alpine valley during January and February 2014. The available observations include high-resolution polarimetric radar and in situ measurements of the ice-phase and liquid-phase components of clouds and precipitation. Radar-based hydrometeor classification suggests that riming is an important factor to favor an efficient growth of the precipitating mass and correlates with snow accumulation rates at ground level. The time steps during which rimed precipitation is dominant are analyzed in terms of temporal evolution and vertical structure. Snowfall identified as rimed often appears after a short time period during which the atmospheric conditions favor wind gusts and updrafts and supercooled liquid water (SLW) is available. When a turbulent atmospheric layer persists for several hours and ensures continuous SLW generation, riming can be sustained longer and large accumulations of snow at ground level can be generated. The microphysical interpretation and the meteorological situation associated with one such event are detailed in the paper. The vertical structure of polarimetric radar observations during intense snowfall classified as rimed shows a peculiar maximum of specific differential phase shift Kdp, associated with large number concentrations and riming of anisotropic crystals. Below this Kdp peak there is usually an enhancement in radar reflectivity ZH, proportional to the Kdp enhancement and interpreted as aggregation of ice crystals. These signatures seem to be recurring during intense snowfall.
IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme
Zhang, Huiying; Li, Xia; Ramelli, Fabiola; et al. (2024)
Atmospheric Measurement Techniques
The shape of ice crystals affects their radiative properties, growth rate, fall speed, and collision efficiency; thus, it plays a significant role in cloud optical properties and precipitation formation. Ambient conditions, like temperature and humidity, determine the basic habit of ice crystals, while microphysical processes, such as riming and aggregation, further shape them, resulting in a diverse set of ice crystal shapes and effective densities. Current classification algorithms face two major challenges: (1) ice crystals are often classified as a whole (at the image scale), necessitating identification of the dominant component of aggregated ice crystals, and (2) single-label classifications lead to information loss because of the compromise between basic habit and microphysical process information. To address these limitations, we present a two-pronged solution here: (1) a rotated object detection algorithm (IceDetectNet) that classifies each component of an aggregated ice crystal individually and (2) a multi-label classification scheme that considers both basic habits and physical processes simultaneously. IceDetectNet was trained and tested on two independent datasets obtained by a holographic imager during the NASCENT campaign in Ny-Ålesund, Svalbard, in November 2019 and April 2020. The algorithm correctly classified 92 % of the ice crystals as either aggregate or non-aggregate and achieved an overall accuracy of 86 % for basic habits and 82 % for microphysical process classification. At the component scale, IceDetectNet demonstrated high detection and classification accuracy across all sizes, indicating its ability to effectively classify individual components of aggregated ice crystals. Furthermore, the algorithm demonstrated a good generalization ability by classifying ice crystals from an independent generalization dataset with overall accuracies above 70 %. IceDetectNet can provide a deeper understanding of ice crystal shapes, leading to better estimates of ice crystal mass, fall velocity, and radiative properties; therefore, it has the potential to improve precipitation forecasts and climate projections.

Publications 1 - 10 of 33

Jan Henneberger

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