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. 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.
We generated a large number 105,000 of aggregates composed of various monomer types and sizes using an aggregation model. Combined with hydrodynamic theory, we derived ice particle properties such as mass, projected area, and terminal velocity as a function of monomer number and size. This particle ensemble allows us to study the relation of particle properties with a high level of detail which is often not provided by in situ measurements. The ice particle properties change rather smoothly with monomer number. We find very little differences in all particle properties between monomers and aggregates at sizes below 1 mm which is in contrast to many microphysics schemes. The impact of the monomer type on the particle properties decreases with increasing monomer number. Whether, for example, the terminal velocity of an aggregate is larger or smaller than an equal‐size monomer depends mostly on the monomer type. We fitted commonly used power laws as well as Atlas‐type relations, which represent the saturation of the terminal velocity at large sizes (terminal velocity asymptotically approaching a limiting value) to the data set and tested the impact of incorporating different levels of complexity with idealized simulations using a 1D Lagrangian super particle model. These simulations indicate that it is sufficient to represent the monomer number dependency of ice particle properties with only two categories (monomers and aggregates). The incorporation of the saturation velocity at larger sizes is found to be important to avoid an overestimation of self‐aggregation of larger snowflakes.
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