snowScatt 1.0: Consistent model of microphysical and scattering
properties of rimed and unrimed snowflakes based on the
self-similar Rayleigh-Gans Approximation

Ori, Davide; Terzi, Leonie von; Karrer, Markus; Kneifel, Stefan

doi:10.5194/gmd-2020-359

Cited by 4 publications

(7 citation statements)

References 61 publications

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“…All codes needed to reproduce the results presented in this paper are included in the examples folder of the snowScatt repository. The necessary input data for the reproduction of the presented analysis are also archived on Zenodo (Ori et al, 2020c, https://doi.org/10.5281/zenodo.4118243).…”

Section: Discussionmentioning

confidence: 99%

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

et al. 2021

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“…The SSRGA parameters of aggregates of plates are also used for the new cloud ice categories ("column" and "needle" in Table 2). For Mix2, SSRGA parameters derived from the same 3D models used for the determination of particle properties (Karrer et al, 2020, K20) are available (Ori et al, 2020b). Since we find little influence of SSRGA parameters in Sect.…”

Section: Sb06 Schemementioning

confidence: 91%

“…The best-fitting aggregate model and associated SSRGA parameters were selected based on the best fit in the triple-frequency DWR space. In this section, we ask whether there is an aggregate type in the database of K20 and Ori et al (2020b) that reproduces well both the physical and scattering properties compared to the observations. O20 already noted that the representation of MDV as a function of DWR resembles to some extent the underlying v t -size relation.…”

Section: Selecting a Particle Type Representative For A Large Aggregate Ensemblementioning

confidence: 99%

“…2). ZeKa, MDVKa and DWRX,Ka are calculated using the self-similar-rayleigh-gans approximation (SSRGA) and the SSRGA parameters of Mix2 as provided by Ori et al (2020b). µmax is estimated by the zeroth, third and sixth moment of the distribution.…”

Section: Size Distributionmentioning

confidence: 99%

“…The lines show the theoretical MDV at a given DWR for the vt size relations of snow particles as assumed in the SB default (black) and as modeled in K20. For the dashed lines, the SSRGA parameters have been directly derived from the corresponding aggregate ensemble properties (as found inOri et al (2020b)). The solid lines use SSRGA parameters as used in O20 in order to illustrate the uncertainty due to the scattering parameters.…”

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confidence: 99%

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Improving the Representation of Aggregation in a Two-moment Microphysical Scheme with Statistics of Multi-frequency Doppler Radar Observations

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Abstract. The simulation of aggregation of ice particles is critical for precipitation prediction, but still a major challenge. Its simulation requires assumptions about numerous parameters, many of which are either not well known or difficult to represent accurately in bulk microphysics schemes. However, knowing the sensitivity of aggregation to various simplified assumptions can help to identify critical parameters. By comparison with suitable observations, these critical parameters can even be constrained. We investigate the sensitivity of the model variables, and the modeled multi-frequency and Doppler radar observables to different parameters in a two-moment microphysics scheme. Therefore, we revise hydrometeor parameters by using a recently published dataset of particle properties, modify the formulations of the aggregation process (which allows using an area-based differential sedimentation kernel) and update other ice microphysical parameters in the scheme such as the sticking efficiency Estick and the shape of the size distribution. Overall, particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while Estick and the cloud ice habit have less influence. Finally, we run multi-week simulations with the most promising parameter combinations. The statistical comparison between real and synthetic observables shows a reduction in the velocity and snow particle size. With this study, we show a possible way to revise processes in microphysical schemes by using statistics of detailed cloud radar observations.

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Highly supercooled riming and unusual triple-frequency radar signatures over Antarctica

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et al. 2022

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Abstract. Riming of ice crystals by supercooled water droplets is an efficient ice growth process, but its basic properties are still poorly known. While it has been shown to contribute significantly to surface precipitation at mid-latitudes, little is known about its occurrence at high latitudes. In Antarctica, two competing effects can influence the occurrence of riming: the scarcity of supercooled liquid water clouds due to the extremely low tropospheric temperatures and the low aerosol concentration, which may lead to the formation of fewer and larger supercooled drops potentially resulting in an enhanced riming efficiency. In this work, by exploiting the deployment of an unprecedented number of multi-wavelength active and passive remote sensing systems (including triple-frequency radar measurements) in West Antarctica, during the Atmospheric Radiation Measurements West Antarctic Radiation Experiment (AWARE) field campaign, we evaluate the importance of riming incidence in Antarctica and find that riming occurs at much lower temperatures compared to the mid-latitudes. We then focus on a case study featuring a persistent layer of unexpectedly pronounced triple-frequency radar signatures but only a relatively modest amount of supercooled liquid water. In-depth analysis of the radar observations suggests that such signatures can only be explained by the combined effects of moderately rimed aggregates or similarly shaped florid polycrystals and a narrow particle size distribution (PSD). Simulations of this case study performed with a 1D bin model %by introducing an additional class corresponding to rimed ice indicate that similar triple frequency radar observations can be reproduced when narrow PSDs are simulated. Such narrow PSDs can in turn be explained by two key factors: (i) the presence of a shallow homogeneous droplet or humidified aerosol freezing layer aloft seeding an underlying supercooled liquid layer, and (ii) the absence of turbulent mixing throughout a stable polar atmosphere that sustains narrow PSDs, as hydrometeors grow from the nucleation region aloft to several millimeter ice particles, by vapor deposition and then riming.

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snowScatt 1.0: Consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh-Gans Approximation

Cited by 4 publications

References 61 publications

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

Improving the Representation of Aggregation in a Two-moment Microphysical Scheme with Statistics of Multi-frequency Doppler Radar Observations

Highly supercooled riming and unusual triple-frequency radar signatures over Antarctica

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