Abstract. The accurate representation of ice particles is essential for both remotely sensed estimates of clouds and precipitation and numerical models of the atmosphere. As it is typical in radar retrievals to assume that all snow is composed of aggregate snowflakes, both denser rimed snow and the mixed-phase cloud in which riming occurs may be under-diagnosed in retrievals and therefore difficult to evaluate in weather and climate models. Recent experimental and numerical studies have yielded methods for using triple-frequency radar measurements to interrogate the internal structure of aggregate snowflakes and to distinguish more dense and homogeneous rimed particles from aggregates. In this study we investigate which parameters of the morphology and size distribution of ice particles most affect the triple-frequency radar signature and must therefore be accounted for in order to carry out triple-frequency radar retrievals of snow. A range of ice particle morphologies are represented, using a fractal representation for the internal structure of aggregate snowflakes and homogeneous spheroids to represent graupel-like particles; the mass–size and area–size relations are modulated by a density factor. We find that the particle size distribution (PSD) shape parameter and the parameters controlling the internal structure of aggregate snowflakes both have significant influences on triple-frequency radar signature and are at least as important as that of the density factor. We explore how these parameters may be allowed to vary in order to prevent triple-frequency radar retrievals of snow from being over-constrained, using two case studies from the Biogenic Aerosols – Effects of Clouds and Climate (BAECC) 2014 field campaign at Hyytiälä, Finland. In a case including heavily rimed snow followed by large aggregate snowflakes, we show that triple-frequency radar measurements provide a strong constraint on the PSD shape parameter, which can be estimated from an ensemble of retrievals; however, resolving variations in the PSD shape parameter has a limited impact on estimates of snowfall rate from radar. Particle density is more effectively constrained by the Doppler velocity than triple-frequency radar measurements, due to the strong dependence of particle fall speed on density. Due to the characteristic signatures of aggregate snowflakes, a third radar frequency is essential for effectively constraining the size of large aggregates. In a case featuring rime splintering, differences in the internal structures of aggregate snowflakes are revealed in the triple-frequency radar measurements. We compare retrievals assuming different aggregate snowflake models against in situ measurements at the surface and show significant uncertainties in radar retrievals of snow rate due to changes in the internal structure of aggregates. The importance of the PSD shape parameter and snowflake internal structure to triple-frequency radar retrievals of snow highlights that the processes by which ice particles interact may need to be better understood and parameterized before triple-frequency radar measurements can be used to constrain retrievals of ice particle morphology.

Vertically pointing radar observations combining multiple frequencies and Doppler measurements have been recently shown to contain valuable information about ice particle growth processes, such as aggregation and riming. In this study, we use a two-months X, Ka, W-Band Doppler radar dataset of midlatitude winter clouds to infer statistical growth signatures of ice and snow particles. The observational statistics are compared to forward-simulated radar moments based on simulations of the campaign time period with a high-resolution version of the ICON model and a two-moment microphysical scheme. The statistical comparison shows very good agreement of the simulated vertical structure of radar reflectivity and surface precipitation rate. The dual-wavelength ratios, which are closely related to the mean particle size, also show consistently a major increase at temperatures higher than-15 • C. However, at temperatures higher than-7 • C, ICON increasingly overestimates the mean particle size. The statistics of mean Doppler velocities also reveal that the model overestimates the terminal velocity of snow particles, especially at larger sizes. We discuss possible reasons for the identified discrepancies, such as an unrealistic temperature dependence of the sticking efficiency or the non-saturation of terminal velocities at larger sizes caused by the implemented power law relations. Our study demonstrates examples of the importance of combining various radar techniques for identifying issues in simulated microphysical processes, which can otherwise be hidden due to compensating errors.

Abstract. More detailed observational capabilities in the microwave (MW) range and advancements in the details of microphysical schemes for ice and snow demand increasing complexity to be included in scattering databases. The majority of existing databases rely on the discrete dipole approximation (DDA) whose high computational costs limit either the variety of particle types or the range of parameters included, such as frequency, temperature, and particle size. The snowScatt tool is innovative in that it provides consistent microphysical and scattering properties of an ensemble of 50 000 snowflake aggregates generated with different physical particle models. Many diverse snowflake types, including rimed particles and aggregates of different monomer composition, are accounted for. The scattering formulation adopted by snowScatt is based on the self-similar Rayleigh–Gans approximation (SSRGA), which is capable of modeling the scattering properties of large ensembles of particles. Previous comparisons of SSRGA and DDA are extended in this study by including unrimed and rimed aggregates up to centimeter sizes and frequencies up to the sub-millimeter spectrum. The results generally reveal the wide applicability of the SSRGA method for active and passive MW applications. Unlike DDA databases, the set of SSRGA parameters can be used to infer scattering properties at any frequency and refractive index; snowScatt also provides tools to derive the SSRGA parameters for new sets of particle structures, which can be easily included in the library. The flexibility of the snowScatt tool with respect to applications that require continuously changing definitions of snow properties is demonstrated in a forward simulation example based on the output of the predicted particle properties (P3) scheme. The snowScatt tool provides the same level of flexibility as commonly used T-matrix solutions, while the computed scattering properties reach the level of accuracy of detailed discrete dipole approximation calculations.

Abstract. Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme “Polarimetric Radar Observations meet Atmospheric Modelling (PROM)”, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes”, this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years.

Abstract. This study investigates the link between rain and ice microphysics across the melting layer in stratiform rain systems using measurements from vertically pointing multi-frequency Doppler radars. A novel methodology to examine the variability of the precipitation rate and the mass-weighted melted diameter (Dm) across the melting region is proposed and applied to a 6 h long case study, observed during the TRIPEx-pol field campaign at the Jülich Observatory for Cloud Evolution Core Facility and covering a gamut of ice microphysical processes. The methodology is based on an optimal estimation (OE) retrieval of particle size distributions (PSDs) and dynamics (turbulence and vertical motions) from observed multi-frequency radar Doppler spectra applied both above and below the melting layer. First, the retrieval is applied in the rain region; based on a one-to-one conversion of raindrops into snowflakes, the retrieved drop size distributions (DSDs) are propagated upward to provide the mass-flux-preserving PSDs of snow. These ice PSDs are used to simulate radar reflectivities above the melting layer for different snow models and they are evaluated for a consistency with the actual radar measurements. Second, the OE snow retrieval where Doppler spectra are simulated based on different snow models, which consistently compute fall speeds and electromagnetic properties, is performed. The results corresponding to the best-matching models are then used to estimate snow fluxes and Dm, which are directly compared to the corresponding rain quantities. For the case study, the total accumulation of rain (2.30 mm) and the melted equivalent accumulation of snow (1.93 mm) show a 19 % difference. The analysis suggests that the mass flux through the melting zone is well preserved except the periods of intense riming where the precipitation rates were higher in rain than in the ice above. This is potentially due to additional condensation within the melting zone in correspondence to high relative humidity and collision and coalescence with the cloud droplets whose occurrence is ubiquitous with riming. It is shown that the mean mass-weighted diameter of ice is strongly related to the characteristic size of the underlying rain except the period of extreme aggregation where breakup of melting snowflakes significantly reduces Dm. The proposed methodology can be applied to long-term observations to advance our knowledge of the processes occurring across the melting region; this can then be used to improve assumptions underpinning spaceborne radar precipitation retrievals.