Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (∼127 GB) to that of the DNN parameters (∼20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.
辐射传输模拟和遥感反演需要准确和快速地计算非球形粒子的光学特性. 传统上一般采用查找表方法来解决电磁散射计算效率低的问题. 但随着粒子参数增加, 查找表数据体量变大, 不便于模式使用. 本文提出了一种深度学习方法用于存储和计算非球形粒子光学特性. 我们将基于不变嵌入T-矩阵方法计算的超椭球粒子光学特性作为训练数据库, 选取长宽比、 圆滑度、 粒径和复折射指数作为训练参数, 设计了四种最优的神经网络架构分别计算或预测消光效率因子、 单次散射消光比、 不对称因子以及相矩阵元素. 结果表明: 神经网络预测值与数据库真值之间的决定系数大于0.999, 可以准确再现数据库的光学特性信息. 另外, 神经网络模型还能够可靠预测出未知参数(尺寸和折射指数)的粒子光学特性值. 通过将大型数据库近乎无损压缩为四个神经网络后, 可将小巧的网络模型代替原始查找表接入辐射传输算法中, 从而实现非球形粒子光学特性的高效计算.
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We acknowledge Ms. Rui LIU from the Training Center of Atmospheric Sciences of Zhejiang University for her efforts managing computing resources and Dr. Wushao LIN for organizing the II-TM optical properties. A portion of the computations was performed in the National Supercomputer Center in Guangzhou (NSCC-GZ), Tianjin (NSCC-TJ), and Wuxi (NSCC-WX), as well as the cluster at State Key Lab of CAD&CG at Zhejiang University. This research was supported by the NSFC Major Project (Grant Nos. 42090030, and 42090032), the National Natural Science Foundation of China (Grant Nos. 42022038, and 42075155), and the National Key Research and Development Program (2019YFC1510400).
• An optical property dataset computed through the T-matrix method can be highly compressed using an optimized deep neural network.
• The optical properties of super-spheroid models can be accurately and efficiently computed through a neural network.
• The neural network can be used instead of a conventional look-up table in atmospheric radiative transfer and related atmospheric models.
This paper is a contribution to the special issue on Cloud—Aerosol—Radiation—Precipitation Interaction: Progress and Challenges.
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Yu, J., Bi, L., Han, W. et al. Application of a Neural Network to Store and Compute the Optical Properties of Non-Spherical Particles. Adv. Atmos. Sci. 39, 2024–2039 (2022). https://doi.org/10.1007/s00376-021-1375-5
- non-spherical particles
- light scattering
- super-spheroid model
- deep learning
- neural network