In this study, the vertical distribution of particle shape (VDPS) method for retrieval of the vertical distribution of particle shapes and the identification of riming and aggregation processes is evaluated through comparison with in-situ measurements and co-located multi-frequency radar observations collected during the CLOUDLAB campaign in Eriswil, Switzerland. Additionally, a novel aspect of the VDPS method is introduced, enabling the derivation of the polarizability ratio using slanted-mode linear depolarization ratio (SLDR) calculated from the main peaks of the Doppler spectra of the signal-to-noise ratio (SNR) from the co- and cross-polarized channels, respectively. This enhancement allows for the detection of secondary ice production and the coexistence of multiple hydrometeor types, which would stay undetected when the retrieval is only applied to the main peak of the Doppler spectrum in the co-channel. Finally, the susceptibility of the VDPS method to the effects of non-Rayleigh scattering (particle sizes close or larger than the radar wavelength) is examined. The obtained results were found to demonstrate the potential of the VDPS method using a Ka-band scanning cloud radar in SLDR mode for operational hydrometeor classification, even under non-Rayleigh scattering conditions.

Ice aggregation in clouds plays a crucial role in cloud development and precipitation formation. Despite the significance of ice aggregation, direct in situ quantification of aggregation rates in natural clouds has been challenging due to the difficulty of tracking ice crystals. Here, we present in situ measurements of ice aggregation rates in persistent supercooled stratiform clouds. Using novel glaciogenic seeding experiments (CLOUDLAB), ice crystals are nucleated upwind and subsequently measured downwind after a known advection time in cloud, allowing us to estimate their age. A deep-learning-based detection algorithm (IceDetectNet) counts the individual monomers of aggregates to derive the initial ice crystal number concentration (ICNCt0). We considered several factors that may influence ice aggregation, including ICNCt0, temperature, ice crystal size, aspect ratio, and turbulence. Among these, ICNCt0 was found to be the dominant factor controlling aggregation rates by three independent approaches: causal inference, a physical equation, and machine learning models. We report, however, a subquadratic dependence of the aggregation rate on ICNCt0 (mean exponent similar to 0.92; 95 % CI: 0.88-0.97), in contrast to theoretical expectations (quadratic dependence). One possible explanation is that aggregation may also involve smaller ice crystals, but this remains hypothetical. To predict aggregation rates, we evaluated 11 machine learning models and a physically based formulation. CatBoost achieved the best statistical performance, while the physical model proved more robust in sensitivity tests. These findings provide new insights into the microphysical and environmental controls of ice aggregation and establish a robust methodological foundation for studying aggregation processes in natural clouds.

For decades, silver iodide (AgI) has been widely used for laboratory ice nucleation experiments and glaciogenic cloud-seeding operations due to its ability to nucleate ice at relatively warm temperatures (up to −3 °C). Despite being one of the most well-characterized ice-nucleating substances, gaps remain in the understanding of how its ice nucleation behavior in the laboratory translates to natural clouds. Here, we present, for the first time, measurements of the ice-nucleated fractions (INFs) of AgI-containing seeding particles, derived from in situ measurements of ice crystal number concentrations (ICNC) and seeding particle number concentrations during glaciogenic cloud-seeding experiments. The experiments were performed as part of the CLOUDLAB project, in which we used targeted cloud seeding with an uncrewed aerial vehicle to try to answer fundamental questions about ice-phase cloud microphysics. Data from 16 seeding experiments show strong linear correlations between ICNC and seeding particle concentration, indicating relatively constant INFs throughout each experiment. Median INFs (0.07 %–1.63 %) were found to weakly increase with decreasing cloud temperature at seeding height (range of −5.1 to −8.3 °C). We compare our results with previous key laboratory experiments and discuss the possible freezing mechanisms. This study can help to bridge the gap in understanding of AgI ice nucleation behavior between laboratory and field experiments which further helps to inform future cloud-seeding operations.

The ice phase is a major contributor to precipitation formation over continents due to its efficiency in growing hydrometeors to large enough sizes for sedimentation. One prominent growth mechanism is the vapor deposition onto ice crystals. However, its actual growth rates remain ambiguous. In the CLOUDLAB project, we conducted field experiments in supercooled clouds with the goal to infer ice crystal growth rates through local perturbations from cloud seeding. In this study, we combine a high‐resolution model setup of 65 m with Lagrangian trajectories to achieve a more straightforward comparison to the observations. We first show that the chosen field experiments can be reproduced in the model in terms of ice crystal number concentration. Second, we perform a series of sensitivity studies by perturbing two parameters in the vapor depositional growth equation. The goal is to understand what change is needed to achieve an agreement between simulated and observed ice crystal growth rates since the default model configuration fails to do so. Increasing the vapor deposition efficiency by a factor of up to three yields comparable growth rates to the observations. Last, we try to quantify the different contributions to the vertical motions within the seeding plume, such as the large‐scale forcing, the underlying topography, and latent heat release upon ice nucleation and growth. We show the different factors are superposed with the large‐scale forcing being a dominant factor. The Lagrangian trajectories proved to be crucial to bridge dynamics and cloud microphysical processes.

A comprehensive approach to study the seeder-feeder mechanism in unprecedented detail from a combined remote-sensing, in situ, and model perspective is shown. This publication aims at investigating the role of the interplay of a seeder-feeder cloud system and its influence on precipitation formation based on a case study from 8 January 2024 observed over the Swiss Plateau in Switzerland.This case study offers an ideal setup for applying several advanced remote-sensing techniques and retrieval algorithms, including fall streak tracking, radar Doppler peak separation, dual-wavelength radar applications, a liquid detection retrieval, a riming retrieval, and an ice crystals shape retrieval. Results indicate that a large portion of ice mass was rimed, which is attributed to persistent coexistence of falling ice crystals and supercooled water within low-level supercooled liquid water layers. Interaction of seeder and feeder clouds results in a significant precipitation enhancement. This has implications on the water cycle. From the anti-correlation between surface precipitation and liquid water path we estimated that 20 %-40 % of the precipitation stems from the feeder cloud. However, we have to note that the value of 20 %-40 % is strongly dependent on the assumed reproduction rate of liquid water in the feeder cloud. This study aims at giving an overview from a remote-sensing, in situ and model perspective on a seeder-feeder event in an unprecedented detail by exploiting a big set of retrievals applicable to remote-sensing and in situ data. Utilizing different retrievals gives a consistent view on the seeder-feeder case study which is an important basis for future studies. It is demonstrated how improved understanding of seeder-feeder interactions can contribute to enhancing weather forecast models, particularly in regions affected by persistent low-level supercooled stratus clouds.

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 mu ms-1 (major axis of pristine ice crystals) from 14 seeding experiments between -5.1 and -8.3 degrees 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.

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 degrees 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 mu m 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.

The ice phase in clouds is essential for precipitation formation over continents. The underlying processes for ice growth are still poorly understood, leading to large uncertainties in precipitation forecasts and climate simulations. One crucial aspect is the Wegener–Bergeron–Findeisen (WBF) process, which describes the growth of ice crystals at the expense of cloud droplets, leading to a partial or full glaciation of the cloud. In the CLOUDLAB project, we employ glaciogenic cloud seeding to initiate the ice phase in supercooled low-level clouds in Switzerland using uncrewed aerial vehicles with the goal of investigating the WBF process. An extensive setup of ground-based remote-sensing and balloon-borne in situ instrumentation allows us to observe the formation and subsequent growth of ice crystals in great detail. In this study, we compare the seeding signals observed in the field to those simulated using a numerical weather model in large-eddy mode (ICON-LEM). We first demonstrate the capability of the model to accurately simulate and reproduce the seeding experiments across different environmental conditions. Second, we investigate the WBF process in the model by comparing the simulated cloud droplet and ice crystal number concentration changes to in situ measurements. In the field experiments, simultaneous reductions in cloud droplet number concentrations with increased ice crystal number concentrations were observed, with periods showing a full depletion of cloud droplets. The model can reproduce the observed ice crystal number concentrations most of the time; however, it cannot reproduce the observed fast reductions in cloud droplet number concentrations. Our detailed analysis shows that the WBF process appears to be less efficient in the model than in the field. In the model, exaggerated ice crystal number concentrations are required to produce comparable changes in cloud droplet number concentrations, highlighting the inefficiency of the WBF process in the numerical weather model ICON.

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.

The representation of cloud processes in models is one of the largest sources of uncertainty in weather forecast and climate projections. While laboratory settings offer controlled conditions for studying cloud processes, they cannot reproduce the full range of conditions and interactions present in natural cloud systems. To bridge this gap, here we leverage weather modification, specifically glaciogenic cloud seeding, to investigate ice growth rates within natural clouds. Seeding experiments were conducted in supercooled stratus clouds (at -8 to -5 degrees C) using an uncrewed aerial vehicle, and the created ice crystals were measured 4-10 min downwind by in situ and ground-based remote sensing instrumentation. We observed substantial variability in ice crystal growth rates within natural clouds, attributed to variations in ice crystal number concentrations and in the supersaturation, which is difficult to reproduce in the laboratory and which implies faster precipitation initiation than previously thought. We found that for the experiments conducted at -5.2 degrees C, the ice crystal populations grew nearly linearly during the time interval from 6 to 10 min. Our results demonstrate that the targeted use of weather modification techniques can be employed for fundamental cloud research (e.g. ice growth processes, aerosol-cloud interactions), helping to advance cloud microphysics parameterizations and to improve weather forecasts and climate projections.

The PolarCAP project aims to decode the interplay between aerosol and cloud-microphysical processes by investigating the evolution of the ice phase under slightly supercooled conditions (T > -10°C) within a thermodynamically and aerosol-controlled environment, employing radar polarimetry and spectral-bin modelling. In collaboration with the CLOUDLAB project at the ETH Zurich, PolarCAP investigates the development of an artificially initiated ice phase within supercooled stratus clouds. Utilizing cloud seeding with silver iodide, CLOUDLAB initiates the freezing process of cloud droplets. The subsequent evolution is monitored using in-situ measurements and conventional ground-based cloud remote sensing tools. The collaboration has yielded a unique dataset, incorporating polarimetric radar and lidar observations from the Leipzig Aerosol and Cloud Remote Observing System (LACROS) in tandem with data from the cloud-resolving spectral bin model COSMO-SPECS. We will present a comparative evaluation between observational data from CLOUDLAB and LACROS, and the innovative COSMO-SPECS simulations featuring an artificial INP source, termed COSMO-SPECS-FLARE (CSF). The inclusion of this source within the COSMO-SPECS framework is pivotal for precipitating ice nucleation, thereby significantly improving the accuracy of cloud microphysical process simulations. A notable advancement of this research is the coupling of CFS output with the radar-forward-simulator PAMTRA. This integration is instrumental in translating the model output into observational parameters, enabling intricate analysis of radar reflectivity, mean Doppler velocity, and other vital atmospheric metrics. Preliminary results are centered on the alignment of radar-forward-simulation predictions based on CSF output with standard remote sensing assessments, including ice and liquid water content retrievals. The analysis highlights the enhanced capacity of the CSF model to replicate observed cloud responses, thus providing comprehensive insights into cloud microphysical processes, particularly in the context of induced ice nucleation.

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.

<p>Wintertime stratus clouds over the Swiss Plateau can last for days.  They dissipate either due to airmass changes, absorption of solar radiation during the day, or after glaciation when a sufficiently large number of cloud droplets freezes. After formation, the ice crystals grow in an ice-supersaturated environment via vapor deposition until they are large enough to sediment from the cloud as drizzle or freezing drizzle.</p> <p>To better understand how quickly ice crystals of various habits grow in real clouds with turbulence, we conduct glaciogenic seeding experiments in wintertime stratus clouds over the Swiss Plateau in our project CLOUDLAB[1]. During these experiments, a drone releases silver iodide (AgI) particles into the cloud, upwind of our measurement site, and we use various ground-based remote sensing and in-situ cloud and aerosol instruments to detect the microphysical changes induced by seeding. Preliminary results from the first CLOUDLAB field campaign proved that our method successfully allows us to detect the seeding signal in the cloud radar. In addition to our field measurements, we conduct numerical model simulations with ICON at different horizontal resolutions and different seeding particle concentrations to understand which seeding AgI concentration is theoretically needed for partial or full glaciation of the cloud, i.e. how fast the ice crystals grow at the expense of the evaporating cloud droplets due to the Wegener-Bergeron-Findeisen process.</p> <p>First results will be presented in this talk.</p> <div><br /> <div> <p>[1] https://cloudlab.ethz.ch/</p> </div> </div>

The Namib Turbulence EXperiment (NamTEX) was a multi-national micrometeorological campaign conducted in the Central Namib Desert to investigate three-dimensional surface layer turbulence and the spatio-temporal patterns of heat transfer between the sub-surface, surface, and atmosphere. The Namib provides an ideal location for fundamental research that revisits some key assumptions in micrometeorology that are implicitly included in the parameterizations describing energy exchange in weather forecasting and climate models: Homogenous flat surfaces, no vegetation, little moisture, and cloud-free skies create a strong and consistent diurnal forcing, resulting in a wide range of atmospheric stabilities. A novel combination of instruments was used to simultaneously measure variables and processes relevant to heat transfer: A three km fibre-optic distributed temperature sensor (DTS) was suspended in a pseudo-three-dimensional array within a 300 m x 300 m domain to provide vertical cross-sections of air temperature fluctuations. Aerial and ground-based thermal imagers recorded high resolution surface temperature fluctuations within the domain and revealed the spatial thermal imprint of atmospheric structures responsible for heat exchange. High-resolution soil temperature and moisture profiles together with heat flux plates provided information on near-surface soil dynamics. Turbulent heat exchange was measured with a vertical array of five eddy-covariance point measurements on a 21-m mast, as well as by co-located small- and large-aperture scintillometers. This contribution first details the scientific goals and experimental set-up of the NamTEX campaign. Then using a typical day, we demonstrate i) the coupling of surface layer, surface, and soil temperatures using high-frequency temperature measurements, ii) differences in spatial and temporal standard deviations of the horizontal temperature field using spatially distributed measurements, and iii) horizontal anisotropy of the turbulent temperature field.

In the arid Central Namib desert, rainfall is rare, but fog frequently occurs at night and exhibits a seasonal pattern. We deployed an eddycovariance setup at two different inland locations, namely Gobabeb and Vogelfederberg. The setup consisted of a cloud droplet probe and at times of up to two sonic anemometerthermometers. It was deployed for 1.5 years, collecting data during more than 150 fog events at either one of the sites. We characterize the different fog regimes at the two stations with co-located measurements of visibility, fog collector mesh precipitation by fog collectors, and non-rainfall water input by microlysimeters for selected periods. At similar levels of visibility, fog collector mesh precipitation at Vogelfederberg is generally higher, likely due to its higher elevation and exposed location. Fog events appear front-like with a decrease of visibility and an increase of droplet numbers above 10 mu m and the associated liquid water content. Fog events regularly lasted 6 h, with the first two to three hours exhibiting higher liquid water content. The droplet distribution, especially droplets above 20 mu m, and thus liquid water content varied in numbers between the locations. Liquid water content correlates with fog collector mesh precipitation at Gobabeb, where the droplet distribution spans the whole resolved spectrum. While liquid water flux was bi-directional, the net sum was still a net gain for the surface. We found only a low correlation between liquid water flux and non-rainfall water input, which demonstrates that the liquid water flux can only partially connect the measured input in the fog collectors and the microlysimeters despite similar values. We therefore presume that drizzle, that is droplets out of the CDP sampling range, may play a role in the Central Namib fog, especially when fog is intermittent as fog deposition in microlysimeters continues when no droplets of the respective size range are present near the ground. Furthermore, the contrast of higher fog collector mesh precipitation at Vogelfederberg compared to Gobabeb despite lower liquid water content is potentially a result of the more numerous small droplets as a consequence of the interception with the upper parts of the stratus.

Visualization is a major part of any scientific work, serving to communicate results in an inclusive way. Whether figures, plots, or graphs, they all play a central role in publications, teaching, and public outreach. Furthermore, visualization is one of the integral parts besides automatic tests of the data that help to ensure the quality of meteorological data from one or more weather stations of measurement networks. This last aspect, in particular, requires the automatic generation of figures and their dynamic presentation.Our homepage, named "Dolueg" for "here, look" in German dialect/Swiss-German, allows researchers and the public to view relevant and Visualization is a major part of any scientific work, serving to communicate results in an inclusive way. Whether figures, plots, or graphs, they all play a central role in publications, teaching, and public outreach. Furthermore, visualization is one of the integral parts besides automatic tests of the data that help to ensure the quality of meteorological data from one or more weather stations of measurement networks. This last aspect, in particular, requires the automatic generation of figures and their dynamic presentation.Our homepage, named "Dolueg" for "here, look" in German dialect/Swiss-German, allows researchers and the public to view relevant and © 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).CORRESPONDING AUTHOR: Robert Spirig, r.spirig@unibas.ch

The measurement of non-rainfall atmospheric water input (NRWI) in arid environments requires instruments that are capable to detect even smallest amounts of total daily water input of less than 0.1 mm. Microlysimeters yield robust and high precision data of such low NRWI. We provide a technical description of a self-constructed microlysimeter and demonstrate its excellent performance regarding the analysis of NRWI in the Central Namib Desert. Three stations of the FogNet measurement network have been equipped with microlysimeters in order to measure fog deposition. NRWI and evaporation for days/nights without fog shows a persistent diurnal course. Deviations from this baseline define the amount of fog deposition, intensity and duration of a fog events. A more detailed analysis of a five-day period reveals the complex nature and variation between individual fog events with respect to the different patterns of fog deposition and fog precipitation and the contribution of adsorption, dew and fog to NRWI. The relation between fog precipitation and fog deposition is not straightforward and a simple parameterization of the processes that quantifies the amount of the water sampled by fog collectors and its connection to NRWI is still lacking.

<p>Regular, nocturnal fog is a defining and seasonally varying feature in the Namib desert. Historical observations were limited to the binary measure of fog occurrence and the concurrent fog water input is quantified only since 2014 via the FogNet using Juvik fog collectors. This installation opened new avenues of research such as the efficiency of the transport mechanism, sampling and spatial variation thereof. An eddy covariance setup of a cloud droplet probe and collocated sonic(s) was installed in turns at the two FogNet stations Vogelfederberg (23.10°S, 15.03°E, 515 m above sea level) and Gobabeb (23.56°S, 15.04°E, 406 m above sea level) for 2 years in the frame of the Namib Fog Life Cycle Analysis Field Measurements (NaFoLiCA-F) project. With this setup, we gathered duration, droplet size distribution, droplet concentration, liquid water content, turbulent liquid water flux and the fog water input via the Juvik fog collector with a total of over 150 fog events. We found that fog appears suddenly and front-like as seen by an increase of droplet numbers by several magnitudes and dissolves more gradually towards the morning. All droplet classes of the resolved range of 2 to 50 µm are present, but at the Vogelfederberg with around 2 to 3 times larger fog water input, the mean and median of the distribution are lower due to comparably fewer large droplets. Liquid water fluxes at both sites resulted in a net gain for the surface but the spatial discrepancy between fog water input recorded by fog collectors and the liquid water content indicates that drizzle, i.e. droplets outside the resolved range, may contribute to the larger total water deposition at Vogelfederberg.</p>