A Computationally Efficient 3-D Full-Wave Model For Coherent Em Scattering From Complex-Geometry Hydrometeors Based On Mom/Cbfm-Enhanced Algorithm

Keywords

Adaptive cross approximation (ACA); characteristic basis function method (CBFM); discrete dipole approximation (DDA); discrete dipole scattering (DDScat); electromagnetic scattering by hydrometeors; method of moments (MoM); snow particles; volume integral equation method (VIEM)

Abstract

An accurate representation of the electromagnetic (EM) behavior of precipitation particles requires modeling of realistic complex geometry and a numerically efficient technique to calculate averaged scattering properties over multiple random target orientations. The discrete dipole approximation is commonly used to compute scattering and absorption by snow particles, because of its geometry flexibility and numerical low cost. However, this method becomes inefficient when the scattering quantities need to be calculated for a large number of orientations. To overcome this limitation, we apply, in this paper, a direct solver-based method, known as the characteristic basis function method (CBFM), to the modeling of scattering by randomly oriented and complex-shaped snow particles. This domain decomposition technique is based on the generation of a new set of basis functions adapted to the geometry of the scatterer in order to significantly reduce the numerical size of the EM problem. This enables us to use a direct solver for the resolution of the final compressed system of linear equations, which is better adapted for multiple excitation problems. When applied to numerically large snow particles, our CBFM-based model, named Numerically Efficient Scattering by Complex Particles, has been shown to yield good results, which compare well with those obtained with discrete dipole scattering, while providing a dramatic reduction in the CPU time.

Publication Date

5-1-2018

Publication Title

IEEE Transactions on Geoscience and Remote Sensing

Volume

56

Issue

5

Number of Pages

2674-2688

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TGRS.2017.2781625

Socpus ID

85040040684 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85040040684

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