Evaluation of Non-Spherical Particle Models for Mineral Dust in Multi-Wavelength Polarization Lidar Applications: Comparison of Spheroid, Super-Ellipsoid, and Irregular-Hexagonal Models
Mineral dust aerosols play an important role in the Earth’s radiation budget and climate system, and their optical properties strongly influence the accuracy of polarization lidar retrievals. Developing realistic optical models is therefore essential for representing dust scattering and polarization behaviors. This study compares three shape models—spheroid, super-ellipsoid, and irregular-hexagonal—in reproducing the optical characteristics of mineral dust at wavelengths of 355, 532, and 1064 nanometers. By combining theoretical simulations with multi-wavelength field and laboratory observations, the suitability of each model is evaluated. Optimal combinations of particle shape and refractive index are obtained using the sum of squared errors criterion. The results show that both the irregular-hexagonal and super-ellipsoid models reproduce the observed relationships between the particle linear depolarization ratio and the lidar ratio, whereas the spheroid model fails to capture high depolarization values. Shape mixing analyses indicate that the irregular-hexagonal model is relatively insensitive to morphological variation, while the super-ellipsoid model exhibits higher adaptability. When measurement uncertainties are considered, the super-ellipsoid model performs best under low error conditions, whereas the irregular-hexagonal model remains more robust under high error conditions. These findings highlight the importance of realistic shape distributions in dust optical modeling and support more reliable polarization lidar retrievals.
Authors
- Laibin Wang (ORCID: https://orcid.org/0009-0000-8334-6063)
- Dong Liu (ORCID: https://orcid.org/0000-0002-2463-832X)
Institutions
- University of Science and Technology of China (CN)
- Hefei University of Technology (CN)
- Chinese Academy of Sciences (CN)
- Anhui Institute of Optics and Fine Mechanics (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-11-24
- DOI
- https://doi.org/10.3390/rs17233804
- Primary Topic
- Atmospheric aerosols and clouds
- Type
- article
- Field-Weighted Citation Impact
- 0.00