Analytical Prediction of Scattering Properties of Spheroidal Dust Particles With Machine Learning

Abstract

Dust aerosol has a widespread impact on air quality and visibility (Huang et al., 2008;Moulin et al., 1998) and modulates the Earth's radiation budget (Pachauri et al., 2014) via scattering and absorbing both shortwave and thermal infrared radiation (Xu et al., 2017), leading to large uncertainties in climate projection. The radiative forcing and spectral fingerprints of dust particles depend on their bulk optical properties, including the extinction coefficient, single-scattering albedo, and phase matrix. While these properties can be computed exactly based on the Lorenz-Mie theory for spherical particles (Mishchenko et al., 2002), most mineral dust particles have highly irregular shapes with a wide range of particle sizes, posing significant challenges to both remote sensing and climate modeling (Wang et al., 2003(Wang et al., , 2004.Various techniques have been developed for estimating non-spherical particle properties, such as the discrete dipole approximation (DDA;Draine & Flatau, 1994;Yurkin et al., 2007), the extended boundary condition method (EBCM) as an implementation of the T-matrix (Mishchenko et al., 1997;Mishchenko & Travis, 1994),

Faster Calculations of Optical Trapping Using Neural Networks Trained by T-Matrix Data: An Application to Micro- and Nanoplastics

“…6 Recently, NNs have also been used for the calculation of the scattering properties of spheroidal aerosols with prospects for applications to cosmic dust studies. 14,15 However, despite the great potential of NNs, only a few studies have been reported about their use for the characterization of microplastics. 16−19 The widespread presence of plastic debris in terrestrial and aquatic environments has caused growing concern in the past decade and stimulated both political and scientific debate.…”

Single‐Scattering Properties of Encapsulated Fractal Black Carbon Particles Computed Using the Invariant Imbedding T‐Matrix Method and Deep Learning Approaches

“…The DNN method has emerged as a more viable solution, offering efficient computations and significantly smaller storage requirements for network parameters than the original database. For example, deep learning methods have already been applied to predict the single‐scattering properties and derivatives of dust aerosols (Chen et al., 2022; Yu et al., 2022). In the case of bare BC aggregates, a support vector machine method has been utilized for direct parameterization, establishing a relationship between optical properties and particle morphology (Luo, Zhang, Wang, et al., 2018).…”

Linearized Single-Scattering Property Database for Hexagonal Prism Ice Particles

“…Scattering for a non-spherical particle of large size can be solved by approximate methods, such as the physical-geometric optics method (PGOM), that require fewer computational resources than rigorous methods [20]. The fundamental integral and differential single scattering properties required for radiative transfer computation can be generated using these algorithms or obtained using deep learning approaches [21,22]. Plenty of single scattering property databases for application in remote sensing and the simulation of radiative transfer have been developed to satisfy spectral and scale continuity, and they employ a variety of algorithms, such as those of the Lorenz-Mie theory [23][24][25][26], the discrete dipole approximation [26][27][28], the T-matrix methods [25,[28][29][30][31][32], the geometric optics method [33][34][35][36][37], the finite-difference time-domain method [35,38], etc.…”