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

Abstract

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.

Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations

“…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, 2021). Since we find little influence of SSRGA parameters in Sect.…”

“…2). Ze Ka , MDV Ka , and DWR X,Ka are calculated using the self-similar Rayleigh-Gans approximation (SSRGA) and the SSRGA parameters of Mix2 as provided by Ori et al (2021) the particle properties. The advantages of this approach are that particle properties can be studied over a large size range, are physically consistent, and can be studied in great detail.…”

“…The histogram shows the observations from the Tripex campaign (D18).The lines show the theoretical MDV at a given DWR for the v t 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, 2021). The solid lines use SSRGA parameters as used in O20 in order to illustrate the uncertainty due to the scattering parameters.…”

Dual-frequency spectral radar retrieval of snowfall microphysics: a physically constrained deep learning approach

“…Efforts have been devoted to the construction of increasingly accurate forward models, for instance through computationally costly discrete dipole approximation (DDA) calculations (e.g. Draine and Flatau, 1994;Liu, 2004;Kuo et al, 2016;Lu et al, 2016), and through simulations based on the self-similar Rayleigh-Gans approximation (SSRGA), which have been tuned to represent accurately the scattering of various particle types (Hogan and Westbrook, 2014;Hogan et al, 2017;Ori et al, 2021).…”

“…Individual spectra are simulated through PAMTRA for an altitude of 1000 masl, using a standard (PAMTRA default) atmospheric profile with a temperature randomly chosen in [-20 • C, 1 • C]. Scattering calculations are performed using the SSRGA, with coefficients from Ori et al (2021); more detail on this is provided in Appendix B2. These assumptions on scattering properties are not flawless, and constitute a bottleneck in our method, as in virtually any attempt at radar-based retrievals.…”