Recently published studies of triple-frequency radar observations of snowfall have demonstrated that naturally occurring snowflakes exhibit scattering signatures that are in some cases consistent with spheroidal particle models and in others can only be explained by complex aggregates. Until recently, no in situ observations have been available to investigate links between microphysical snowfall properties and their scattering properties. In this study, we investigate for the first time relations between collocated ground-based triple-frequency observations with in situ measurements of snowfall at the ground. The three analyzed snowfall cases obtained during a recent field campaign in Finland cover light to moderate snowfall rates with transitions from heavily rimed snow to open-structured, low-density snowflakes. The observed triple-frequency signatures agree well with the previously published findings from airborne radar observations. A rich spatiotemporal structure of triple-frequency observations throughout the cloud is observed during the three cases, which often seems to be related to riming and aggregation zones within the cloud. The comparison of triple-frequency signatures from the lowest altitudes with the ground-based in situ measurements reveals that in the presence of large (>5 mm) snow aggregates, a bending away in the triple-frequency space from the curve of classical spheroid scattering models is always observed. Rimed particles appear along an almost horizontal line in the triple-frequency space, which was not observed before. Overall, the three case studies indicate a close connection of triple-frequency signatures and snow particle structure, bulk snowfall density, and characteristic size of the particle size distribution.

Measured ice crystal concentrations in natural clouds at modest supercooling (temperature ;.2108C) are often orders of magnitude greater than the number concentration of primary ice nucleating particles. Therefore, it has long been proposed that a secondary ice production process must exist that is able to rapidly enhance the number concentration of the ice population following initial primary ice nucleation events. Secondary ice production is important for the prediction of ice crystal concentration and the subsequent evolution of some types of clouds, but the physical basis of the process is not understood and the production rates are not well constrained. In November 2015 an international workshop was held to discuss the current state of the science and future work to constrain and improve our understanding of secondary ice production processes. Examples and recommendations for in situ observations, remote sensing, laboratory investigations, and modeling approaches are presented.

Abstract. In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiälä are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic massdimensional relations of snow are retrieved. For snow rates more than 0.2 mm h −1 , a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.

This study uses snow events from the Biogenic Aerosols–Effects on Clouds and Climate (BAECC) 2014 campaign to investigate the connection between properties of snow and radar observations. The general hydrodynamic theory is applied to video-disdrometer measurements to retrieve masses of falling ice particles. Errors associated with the observation geometry and the measured particle size distribution (PSD) are addressed by devising a simple correction procedure. The value of the correction factor is determined by comparison of the retrieved precipitation accumulation with weighing-gauge measurements. Derived mass–dimensional relations are represented in the power-law form m = . It is shown that the retrieved prefactor am and exponent bm react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Ze and snowfall precipitation rate S (Ze = ) are determined. For the studied events, the prefactor of the Ze–S relation varied between 53 and 782 and the exponent was in the range of 1.19–1.61. The dependence of the factors azs and bzs on the m(D) relation and PSD are investigated. The exponent of the Ze–S relation mainly depends on the exponent of the m(D) relation, whereas the prefactor azs depends on both the intercept parameter N0 of the PSD and the prefactors of the m(D) and υ(D) relations. Changes in azs for a given N0 are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming.

Ground‐based observations of ice particle size distribution and ensemble mean density are used to quantify the effect of riming on snowfall. The rime mass fraction is derived from these measurements by following the approach that is used in a single ice‐phase category microphysical scheme proposed for the use in numerical weather prediction models. One of the characteristics of the proposed scheme is that the prefactor of a power law relation that links mass and size of ice particles is determined by the rime mass fraction, while the exponent does not change. To derive the rime mass fraction, a mass‐dimensional relation representative of unrimed snow is also determined. To check the validity of the proposed retrieval method, the derived rime mass fraction is converted to the effective liquid water path that is compared to microwave radiometer observations. Since dual‐polarization radar observations are often used to detect riming, the impact of riming on dual‐polarization radar variables is studied for differential reflectivity measurements. It is shown that the relation between rime mass fraction and differential reflectivity is ambiguous, other factors such as change in median volume diameter need also be considered. Given the current interest on sensitivity of precipitation to aerosol pollution, which could inhibit riming, the importance of riming for surface snow accumulation is investigated. It is found that riming is responsible for 5% to 40% of snowfall mass. The study is based on data collected at the University of Helsinki field station in Hyytiälä during U.S. Department of Energy Biogenic Aerosols Effects on Clouds and Climate (BAECC) field campaign and the winter 2014/2015. In total 22 winter storms were analyzed, and detailed analysis of two events is presented to illustrate the study.

In this article a potential role of snowflake growth by aggregation on formation of dual‐polarization radar signatures in winter storms is discussed. We advocate that the observed bands of increased values of specific differential phase (Kdp) can be linked to the onset of aggregation. These bands are caused by high number concentrations of oblate relatively dense ice particles and take place in regions where an ice phase “seeder‐feeder” is active. On the other hand, the differential reflectivity (Zdr) bands, in absence of detectable Kdp values, are observed in the areas where crystal growth is the dominating snow growth mechanism and ice particle number concentration is lower. This distinction in underlying processes explains why Kdp and Zdr bands are not always observed at the same time. Furthermore, based on surface observations of snowflakes, it is determined that early aggregates, consisting of a small number of ice crystals, are oblate. These oblate particles could contribute to the reported dual‐polarization radar signatures in snow, especially to the Kdp. This could help to explain why, where observed at the same type, Kdp and Zdr bands do not match and the altitude of the peak value of Kdp is usually lower than the Zdr one. It also means that dual‐polarization radar signatures of snowflakes may depend on a stage of aggregation.

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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.