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首页> 外文期刊>Journal of Quantitative Spectroscopy & Radiative Transfer >Polarized bidirectional reflectance of optically thick sparse particulate layers: An efficient numerically exact radiative-transfer solution
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Polarized bidirectional reflectance of optically thick sparse particulate layers: An efficient numerically exact radiative-transfer solution

机译:光学上稀疏的微粒层的双向偏振反射:有效的数值精确辐射传输解决方案

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摘要

We describe a simple yet efficient numerical algorithm for computing polarized bidirectional reflectance of an optically thick (semi-infinite), macroscopically flat layer composed of statistically isotropic and mirror symmetric random particles. The spatial distribution of the particles is assumed to be sparse, random, and statistically uniform. The 4 x 4 Stokes reflection matrix is calculated by iterating the Ambartsumian's vector nonlinear integral equation. The result is a numerically exact solution of the vector radiative transfer equation and as such fully satisfies the energy conservation law and the fundamental reciprocity relation. Since this technique bypasses the computation of the internal radiation field, it is very fast and highly accurate. The FORTRAN implementation of the technique is publicly available on the World Wide Web at http://www.giss.nasa.gov/staff/mmishchenko/brf. It can be combined with several existing computer programs providing the requisite single-scattering properties of spherical or morphologically complex particles and applied to a wide range of optical characterization problems. Benchmark results obtained with this program can be used for testing alternative solvers of the vector radiative transfer equation. Published by Elsevier Ltd.
机译:我们描述了一种简单而有效的数值算法,用于计算由统计各向同性和镜像对称随机粒子组成的光学厚(半无限),宏观平坦层的偏振双向反射率。假定粒子的空间分布是稀疏的,随机的和统计上均匀的。通过迭代Ambartsumian的矢量非线性积分方程来计算4 x 4 Stokes反射矩阵。结果是矢量辐射传递方程的精确数值解,因此完全满足能量守恒定律和基本互易关系。由于此技术绕过了内部辐射场的计算,因此非常快速且非常准确。该技术的FORTRAN实施可在万维网(http://www.giss.nasa.gov/staff/mmishchenko/brf)上公开获得。它可以与几个现有的计算机程序结合使用,这些程序提供球形或形态复杂的粒子的必要单散射特性,并应用于各种光学表征问题。通过该程序获得的基准结果可用于测试矢量辐射传递方程的替代求解器。由Elsevier Ltd.发布

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