Abstract. Observations of orographic mixed-phase clouds (MPCs) have long shown that measured ice crystal number concentrations (ICNCs) can exceed the concentration of ice nucleating particles by orders of magnitude. Additionally, model simulations of alpine clouds are frequently found to underestimate the amount of ice compared with observations. Surface-based blowing snow, hoar frost, and secondary ice production processes have been suggested as potential causes, but their relative importance and persistence remains highly uncertain. Here we study ice production mechanisms in wintertime orographic MPCs observed during the Cloud and Aerosol Characterization Experiment (CLACE) 2014 campaign at the Jungfraujoch site in the Swiss Alps with the Weather Research and Forecasting model (WRF). Simulations suggest that droplet shattering is not a significant source of ice crystals at this specific location, but breakups upon collisions between ice particles are quite active, elevating the predicted ICNCs by up to 3 orders of magnitude, which is consistent with observations. The initiation of the ice–ice collisional breakup mechanism is primarily associated with the occurrence of seeder–feeder events from higher precipitating cloud layers. The enhanced aggregation of snowflakes is found to drive secondary ice formation in the simulated clouds, the role of which is strengthened when the large hydrometeors interact with the primary ice crystals formed in the feeder cloud. Including a constant source of cloud ice crystals from blowing snow, through the action of the breakup mechanism, can episodically enhance ICNCs. Increases in secondary ice fragment generation can be counterbalanced by enhanced orographic precipitation, which seems to prevent explosive multiplication and cloud dissipation. These findings highlight the importance of secondary ice and seeding mechanisms – primarily falling ice from above and, to a lesser degree, blowing ice from the surface – which frequently enhance primary ice and determine the phase state and properties of MPCs.

Snowfall in Antarctica is the main input to ice sheet mass balance (King & Turner, 1997), which determines the contribution of the southernmost continent to sea level rise (Shepherd & Wingham, 2007). On the East Antarctic coast, most of the precipitation comes either from meridional moisture advection by extratropical cyclones or is induced by orographic forcing (King & Turner, 1997). The surface mass balance of the East Antarctic coastal ice sheets is hence heavily influenced by the frequency and intensity of maritime moisture intrusions from lower latitudes, which often result in high precipitation accumulations (Noone et al., 1999;Nuncio & Satheesan, 2014;Welker et al., 2014). A recent study by Turner et al. (2019) showed that extreme precipitation events (EPEs, defined as the largest 10% of daily totals) contribute to more than 40% of the annual precipitation over much of the continent. In particular, the greatest contribution from EPEs is found on the main ice shelves, especially on the Amery Ice Shelf (less than 10 days of the highest-ranked precipitation contributing to 50% of the annual total). Davis station ( 𝐴𝐴 69 • S, 𝐴𝐴 78 • E) is located on the coast of the Vestfold Hills, just north-east of the Amery Ice Shelf (Figure 1). The Vestfold Hills are one of the few ice-free regions in Antarctica, which makes it part of the Antarctic oases (Pickard, 1986). This is due mostly to its precipitation climatology with only 70.9 mm mean annual precipitation and 7.5 mm in December-January-February (DJF, statistics computed over the period 1960-2021, http://www.bom.gov.au/climate/averages/tables/cw_300000_All.shtml). Davis station, with its 1.8 𝐴𝐴 • C mean daily

Abstract. This article describes a 4-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.org/10.1594/PANGAEA.918315, Gehring et al., 2020a). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85 % of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before.

Abstract. This article describes a four-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.pangaea.de/10.1594/PANGAEA.918315, Gehring et al. (2020a)) . Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85 % of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before.

<p>It is well established that the Aerosol-Cloud Interaction (ACI) processes play a key-role in global precipitation and are a strong modulator of cloud radiative forcing and climate, and yet remain poorly understood despite decades of research. Aerosol-cloud interactions are one of the most uncertain aspects of anthropogenic climate change (Seinfeld et al., 2016a; IPCC, 2021).</p><p>Global datasets on cloud microphysical state – especially droplet number concentration and size distribution – provide important constraints that are required for reducing the ACI uncertainty. Recently, Quaas et al. (2020) showed that satellite remote sensing is the only approach that offers the potential of obtaining global datasets with frequent coverage; current retrieval algorithms, however, carry many uncertainties and require constraints that can only be addressed with <em>in situ</em> and/or ground-based remote sensing observations.</p><p>Our study aims to evaluate retrievals of cloud droplet number (<em>Nd</em>), effective radius (<em>r<sub>eff</sub></em>) and optical thickness provided by the CLoud property dAtAset using SEVIRI - Edition 3 (CLAAS-3) cloud products of Satellite Application Facility on Climate Monitoring (CM SAF).  For this reason, we used co-located in-situ measurements of aerosols and cloud dynamical properties in conjunction with remote sensing observations at the high-altitude regional background station Hellenic Atmospheric Aerosol and Climate Change (HAC<sup>2</sup>) during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign, which took place from Fall 2021 to Spring 2022 at Mount Helmos in Peloponnese, Greece (https://calishto.panacea-ri.gr/).</p><p>In this study, we adopt an approach first applied to droplet retrievals in an urban environment (Foskinis et al. 2022). Ground-based remote sensing instrumentation involved includes a Doppler depolarization lidar (HALO) at 1550 nm to provide the vertical velocity (<em>w</em>) of the air masses, a Doppler cloud radar at 94 GHz (RPG) to provide the equivalent reflectivity factor (<em>Z</em>), and the mean Doppler velocity (<em>VD</em>), and a radiometer at 89 GHz provides the liquid water path (LWP). Furthermore, the in-situ instrumentations employed a co-located scanning Mobility Particle Size (SMPS) measuring the size distribution of submicron aerosol, and a Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) to provide the aerosol chemical composition of the aerosols. The in-situ dataset together with the airmass vertical velocity distributions are used as input to a state-of-the art parameterization to predict the droplet number (<em>N<sub>d</sub></em>) in clouds formed in the vicinity of the HAC<sup>2</sup> station. Retrievals with the the CLAAS-3 cloud properties product from CMSAF are then evaluated with in-situ observations carried out with a cloud probe instrument (PVM-100) and the droplet number calculations.</p><p>Compared to our previous study (Foskinis et al. 2022), this study is implemented in a different physical system, where we examined again the dependence of the Spectral Dispersion of Droplets (SDD) on <em>N<sub>d</sub></em> and we found a new optimized expression between SDD-<em>N<sub>d</sub></em> which can be used on the established droplet number retrieval algorithm (Bennartz et al., 2007) for non-precipitating planetary boundary layer clouds in order to mitigate the bias.</p>