ALBERT

Description: 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.

Description: 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.

Description: The formation and growth of ice in clouds are essential for precipitation formation. Despite advances in our understanding of ice phase processes arising from laboratory and modeling studies, a major gap exists in representative in-situ field observations. CLOUDLAB aims to fill this gap by performing targeted glaciogenic cloud seeding experiments (using silver iodide injections) in supercooled, predominantly liquid stratus clouds over the Swiss Plateau to induce ice crystal formation and subsequent growth processes. Downwind of the seeding location, the freshly generated ice crystals are observed using two scanning cloud radars and a tethered balloon system equipped with an optical particle counter and holographic imager. The holographic imager captures phase-resolved information about the number, size, and spatial distributions of hydrometeors with high spatio-temporal resolution (〉6 µm, 40 Hz). We present in-situ and remote sensing data observed during a series of cloud seeding experiments. The ice crystal and aerosol number concentrations increased by several orders of magnitudes during the passage of the seeding plume. Simultaneously at the same location, the radar reflectivity increased by 10 to 20 dB compared to the unseeded background cloud. The observed ice crystals formed three to fifteen minutes before the detection and grew to a diameter of around 100-200 µm. We will also assess how the seeding influences the liquid phase (e.g. liquid water content, cloud droplet size) and answer the question: Does the seeding produce fully glaciated patches? This dataset provides unique insights into early-stage ice processes and broadens our understanding of precipitation formation.

Description: Ice formation and growth play a critical role in the initiation of precipitation. However, fundamental knowledge gaps in microphysical processes exist, for example, in the efficiency of diffusional growth of ice crystals, which leads to uncertainties in weather forecasts and climate projections. The CLOUDLAB project aims to bridge this gap by using supercooled stratus clouds as a natural laboratory for glaciogenic cloud seeding experiments. Ice nucleating particles (particles containing mainly silver iodide) are dispersed into these clouds via an Uncrewed Aerial Vehicle (UAV), triggering ice crystal formation and growth. The use of a UAV for seeding in conjunction with the persistent nature of stratus clouds enables repeated seeding experiments under similar and well-constrained initial conditions. So far, 50 seeding experiments with seeding temperatures between -10°C and -3°C were conducted in clouds over the Swiss plateau. The seeding-induced microphysical changes were monitored using in-situ and ground-based remote sensing equipment positioned 3-15 minutes downstream of the seeding location. The seeding plume had an extent of multiple hundreds of metres and was detected by increased reflectivity in the vertically pointing and scanning cloud radars (additional instrumentation was provided by TROPOS in the frame of the accompanying PolarCAP project). Simultaneously, high concentrations of small ice crystals were detected with a holographic imager mounted on a tethered balloon. The findings are contextualized with simulations using the numerical weather model (ICON).

Description: Ice crystals play a crucial role in cloud optical properties and precipitation formation, as their shape affects their radiative properties, diffusional growth rate, fall speed, and collision efficiency. While the habit of ice crystals is determined by the ambient environment (i.e., temperature and humidity) in which they grow, it is also shaped by microphysical processes such as riming and aggregation. However, existing single-label classification algorithms face limitations when it comes to assigning multiple labels to a single ice crystal (i.e., a rimed column) or classifying the various components of an aggregated ice crystal.To overcome these limitations, this study introduces a novel multi-label classification system that considers both basic habits and physical processes leading to the observed ice crystal shapes. An object detection algorithm is presented that classifies each component of an aggregated ice crystal individually (including both basic habits and physical processes). The algorithm was trained on 18’000 ice crystal images and tested on 2300 ice crystal images captured by a holographic imager during the NASCENT campaign in Ny-Alesund, Svalbard. The algorithm offers a classification of both basic habits and microphysical processes with an accuracy of 86.5% and 81.4% respectively. The study results provide a deeper understanding of ice crystal shapes, which can improve precipitation and radiation estimates, and further contribute to advances in weather forecasting and climate research. Additionally, the algorithm's performance has shown a better generalization ability to predict ice crystal habits in new datasets compared to traditional deep learning algorithms.

Description: Cloud radar Doppler spectra contain vertically highly resolved information about the hydrometeors present in a cloud. A mixture of different hydrometeor types can lead to several peaks in the spectrum due to their different fall speeds, giving a hint about the size/ density/ number of the respective particles. Here we present the effort of joining two methods to separate and interpret peaks in cloud radar Doppler spectra (Kalesse et al., 2019; Radenz et al., 2019). The overarching goal is to make them insensitive to instrument type and settings, and applicable to all cloud radars which are part of the ACTRIS CloudNet network. A supervised machine learning peak detection algorithm (PEAKO, Kalesse et al., 2019) is used to derive the optimal parameters to detect peaks in cloud radar Doppler spectra for each set of instrument settings. In the next step, these parameters are used by peakTree (Radenz et al., 2019), which is a tool for converting multi-peaked Doppler spectra into a binary tree structure. peakTree yields the (polarimetric) radar moments of each detected (sub-)peak and can thus be used to classify hydrometeor types. This allows us to analyze Doppler spectra with respect to, e.g. the occurrence of supercooled liquid water or ice needles with high linear depolarisation ratio (LDR). The new toolkit is evaluated using observations from two field campaigns, the DACAPO-PESO experiment and the NASCENT campaign. For DACAPO-PESO, two different radar systems are compared, and for NASCENT, detected peaks are validated against in-situ cloud observations collected with a holographic imager.

Description: Low stratus clouds are a commonly occurring phenomenon over the Swiss Plateau during wintertime and can last up to several days. This almost stable characteristic is exploited in the CLOUDLAB project, which aims to gain a more profound understanding of ice crystal formation and growth by introducing seeding particles into a supercooled liquid cloud, thus initiating glaciation of the cloud. The seeding particles (silver iodide) are injected into the dynamically stable cloud from a drone enabling a highly precise and repetitive experimental setup. By observing the microphysical changes via an extensive observational setup of remote sensing and in-situ instrumentation, the low stratus clouds effectively serve as a natural laboratory for seeding experiments. To further our understanding of the occurring processes, we employ the ICON-NWP model in large-eddy mode with a horizontal resolution of up to 65 m to simulate the conducted experimental conditions. The existing two-moment microphysics scheme is supplemented by a seeding parameterization allowing us to mimic the ice nucleation behavior of the seeding particles. While it remains a challenge for numerical models to adequately represent low stratus, we successfully simulated several seeding experiments. Here, we present first comparisons between model simulations and observational data in the form of case studies and show the potential of ICON-NWP for interpreting our seeding signals.

Description: How cloud droplet activation and ice nucleation occur in clouds is still an open question that is important for predicting cloud occurrence, weather, and climate. More measurements in real clouds are vital to supplement laboratory studies to further our understanding of cloud microphysical processes. Here, we present results from CLOUDLAB, a field study deploying glaciogenic cloud seeding experiments, using a drone to release silver-iodide-containing particles into supercooled stratus clouds over the Swiss Plateau. We measure the downstream effects on aerosol and cloud particle populations in-situ with two portable optical particle spectrometers (POPS) mounted on a second drone and on a tethered balloon, as well as an in-situ holographic imager and ground-based remote-sensing instruments. With more than 50 in-cloud and 30 out-of-cloud experiments, covering a range of environmental conditions (temperature, wind speed, liquid water content), we address how the seeding material induces freezing in-cloud, specifically: Do the particles first activate into cloud droplets and then nucleate ice via immersion freezing, or do the particles collide with existing cloud droplets and cause contact freezing? By comparing POPS size distributions of the seeding material measured in- and out-of-cloud, at different distances from the seeding drone, we can identify how much hygroscopic growth occurs under different conditions to infer cloud droplet activation. Meanwhile, supporting in-situ cloud particle measurements and cloud radar reflectivity show the presence and extent of ice nucleation. These novel methods and data offer unique insights into cloud droplet activation and ice nucleation in real environmental conditions.

Description: The project Polarimetric Radar Signatures of Ice Formation Pathways from Controlled Aerosol Perturbations (PolarCAP) aims at tackling the complex problem of the evolution of the ice phase at slightly supercooled conditions by means of observations in a thermodynamically and aerosolcontrolled natural environment using radar polarimetry and spectral‐bin modelling. PolarCAP is implemented in close collaboration with the external ERC research project CLOUDLAB of ETH Zurich. The targets of the study are predominantly liquid supercooled stratiform cloud layers which frequently form during wintertime in the temperature range from ‐10 to 0°C over the Swiss Plateau. The observations will build on a solid foundation of previous achievements of TROPOS and previous collaborations. Doppler peak separation and multi‐wavelength techniques, retrievals of ice‐crystal size distributions, determination of particle habits techniques, all based on scanning polarimetric cloud radar observations, will be combined with fall‐streak tracking and liquid‐water retrievals to obtain a comprehensive picture of the cloud evolution. In collaboration with CLOUDLAB, evaluation data will be obtained in‐situ with UAV and holographic ice particle imagers, which will be used to challenge the remote‐sensing‐based retrievals. Within the PolarCAP project the remote sensing equipment of LACROS (Leipzig Aerosol and Cloud Remote Observations System) is installed in Eriswil, Switzerland during three winter campaigns 2022‐ 2025. This contribution will introduce PolarCAP. We will further present first results of our cloud microphysical measurements and their evaluation against CLOUDLAB in‐situ observations, as well as associated spectral‐bin model simulations, with the focus on hydrometeor characterization and retrieval evaluation.