Preprints
https://doi.org/10.5194/gmd-2020-359
https://doi.org/10.5194/gmd-2020-359

Submitted as: model description paper 09 Nov 2020

Submitted as: model description paper | 09 Nov 2020

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

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

Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel Davide Ori et al.
  • Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany

Abstract. More detailed observational capabilities in the microwave (MW) 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, or particle size.

snowScatt is an innovative tool that provides the consistent microphysical and scattering properties of an ensemble of 50 thousand 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 cm-sizes and frequencies up to the sub-mm spectrum. The results reveal in general the wide applicability of the SSRGA method for active and passive MW applications. Unlike DDA databases, the set of SSRGA coefficients can be used to infer the scattering properties at any frequency and refractive index. snowScatt also provides tools to derive the SSRGA coefficients 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. snowScatt 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.

Davide Ori et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Davide Ori et al.

Data sets

snowScatt-data (Version v1) Ori, Davide, von Terzi, Leonie, Karrer, Markus, and Kneifel Stefan https://doi.org/10.5281/zenodo.4118243

Model code and software

snowScatt (Version v1.0) Ori, Davide, von Terzi, Leonie, Karrer, Markus, and Kneifel Stefan https://doi.org/10.5281/zenodo.4118245

Davide Ori et al.

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Latest update: 26 Feb 2021
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Short summary
Snowflakes have very complex shapes and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging on their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.