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A new comprehensive cloud–precipitation– meteorological observatory has been established at Princess Elisabeth base, located in the escarpment zone of Dronning Maud Land (DML), East Antarctica. The observatory consists of a set of ground-based remote-sensing instruments (ceilometer, infrared pyrometer and vertically profiling precipitation radar) combined with automatic weather station measurements of near-surface meteorology, radiative fluxes, and snow height. In this paper, the observatory is presented and the potential for studying the evolution of clouds and precipitating systems is illustrated by case studies. It is shown that the synergetic use of the set of instruments allows for distinguishing ice, liquid-containing clouds and precipitating clouds, including some information on their vertical extent. In addition, wind-driven blowing snow events can be distinguished from deeper precipitating systems. Cloud properties largely affect the surface radiative fluxes, with liquidcontaining clouds dominating the radiative impact. A statistical analysis of all measurements (in total 14 months mainly during summer–beginning of winter) indicates that these liquid-containing clouds occur during as much as 20% of the cloudy periods. The cloud occurrence shows a strong bimodal distribution with clear-sky conditions 51% of the time and complete overcast conditions 35% of the time. Snowfall occurred during 17% of the cloudy periods with a predominance of light precipitation and only rare events with snowfall > 1mmh–1 water equivalent (w.e.). Three of such intense snowfall events occurred during 2011 contributing to anomalously large annual surface mass balance (SMB). Large accumulation events (> 10mmw.e. day–1) during the radar-measurement period of 26 months were always associated with snowfall, but at the same time other snowfall events did not always lead to accumulation. The multiyear deployment of a precipitation radar in Antarctica allows for assessing the contribution of the snowfall to the local SMB and comparing it to the other SMB components. During 2012, snowfall rate was 110±20mmw.e. yr–1, from which surface and drifting snow sublimation removed together 23 %. Given the measured yearly SMB of 52±3mmw.e., the residual term of 33±21mmw.e. yr–1 was attributed to the winddriven snow erosion. In general, this promising set of robust instrumentation allows for improved insight into cloud and precipitation processes in Antarctica and can be easily deployed at other Antarctic stations. [ABSTRACT FROM AUTHOR]

Clouds and associated precipitation are the largest source of uncertainty in current weather and future climate simulations. Observations of the microphysical, dynamical and radiative processes that act at cloud-scales are needed to improve our understand- ing of clouds. The rapid expansion of ground-based super-sites and the availability of continuous profiling and scanning multi-frequency radar observations at 35 and 94GHz have significantly improved our ability to probe the internal structure of clouds in high temporal-spatial resolution, and to retrieve quantitative cloud and precipitation properties. However, there are still gaps in our ability to probe clouds due to large uncertainties in the retrievals. The present work discusses the potential of G-band (frequency between 110 and 300 GHz) Doppler radars in combination with lower frequencies to further improve the retrievals of microphysical properties. Our results show that, thanks to a larger dynamic range in dual-wavelength reflectivity, dual-wavelength attenuation and dual-wavelength Doppler velocity (with respect to a Rayleigh reference), the inclusion of frequencies in the G-band can significantly improve current profiling capabilities in three key areas: boundary layer clouds, cirrus and mid-level ice clouds, and precipitating snow. [ABSTRACT FROM AUTHOR]

Retrievals of ice and snow are made from Ka‐ and W‐band zenith‐pointing Doppler radars at Hyytiälä, Finland, during the snow experiment component of the Biogenic Aerosols: Effects on Clouds and Climate (2014) field campaign. In a novel optimal estimation retrieval, mean Doppler velocity is exploited to retrieve a density factor parameter, which modulates the mass, shape, terminal velocity, and backscatter cross sections of ice particles. In a case study including aggregate snow and graupel we find that snow rate and ensemble mean ice density can be retrieved to within 50% of in situ measurements at the surface using dual‐frequency Doppler radar retrievals. While Doppler measurements are essential to the retrieval of particle density, the dual‐frequency ratio provides a strong constraint on particle size. The retrieved density factor is strongly correlated with liquid water path, indicating that riming is the primary process by which the density factor is modulated. Using liquid water path as a proxy for riming, profiles classified as unrimed snow, rimed snow, and graupel exhibit distinct features characteristic of aggregation and riming processes, suggesting the potential to make estimates of process rates from these retrievals. We discuss the potential application of the technique to future satellite missions. Plain Language Summary: Ground‐based radar measurements of ice clouds and snow are used to estimate the size, number, and density of snowflakes. Doppler velocity measurements of particle fall speeds are used to estimate the mass and shape of the snow particles. The properties of snow estimated using radar compare well against measurements of particles at the surface and estimates of the amount of liquid water in the atmosphere; the presence of liquid water relates to the potential for riming, in which snowflakes increase in density and fall speed by collecting and freezing liquid droplets. More accurate estimates of snow density from ground‐based and satellite radars help to improve global estimates of precipitation and snow accumulation and the representation of clouds and snow in weather and climate models. Key Points: The CAPTIVATE retrieval algorithm is applied to two Doppler cloud radars deployed during the BAECC 2014 field campaign in FinlandThe mass, area, and radar backscatter of ice particles from unrimed snowflakes to graupel are estimated from Doppler velocityThe retrieval provides insights into aggregation and riming processes, and riming can be used to diagnose embedded mixed‐phase cloud layers [ABSTRACT FROM AUTHOR]

Snowfall rate (SR) estimates over Antarctica are sparse and characterised by large uncertainties. Yet, observations by precipitation radar offer the potential to get better insight in Antarctic SR. Relations between radar reflectivity (Ze) and snowfall rate (Ze-SR relations) are however not available over Antarctica. Here, we analyse observations from the first Micro Rain Radar (MRR) in Antarctica together with an optical disdrometer (Precipitation Imaging Package; PIP), deployed at the Princess Elisabeth station. The relation Ze = A*SR B was derived using PIP observations and its uncertainty was quantified using a bootstrapping approach, randomly sampling within the range of uncertainty. This uncertainty was used to assess the uncertainty in snowfall rates derived by the MRR. We find a value of A = 18 [11–43] and B = 1.10 [0.97–1.17]. The uncertainty on snowfall rates of the MRR based on the Ze-SR relation are limited to 40%, due to the propagation of uncertainty in both Ze as well as SR, resulting in some compensation. The prefactor (A) of the Ze-SR relation is sensitive to the median diameter of the snow particles. Larger particles, typically found closer to the coast, lead to an increase of the value of the prefactor (A = 44). Smaller particles, typical of more inland locations, obtain lower values for the prefactor (A = 7). The exponent (B) of the Ze-SR relation is insensitive to the median diameter of the snow particles. In contrast with previous studies for various locations, shape uncertainty is not the main source of uncertainty of the Ze-SR relation. Parameter uncertainty is found to be the most dominant term, mainly driven by the uncertainty in mass-size relation of different snow particles. Uncertainties on the snow particle size distribution are negligible in this study as they are directly measured. Future research aiming at reducing the uncertainty of Ze-SR relations should therefore focus on obtaining reliable estimates of the mass-size relations of snow particles. [ABSTRACT FROM AUTHOR]