Mr. R. Andrew McGill Profile

R. Andrew McGill

at US Naval Research Lab

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Publications (1)

Proceedings Article | 7 June 2024 Presentation + Paper

Modeling of infrared scattering signatures of liquid and solid aerosol clouds

Robert Furstenberg, Andrew Shabaev, Tyler Huffman, Christopher Kendziora, R. Andrew McGill

Proceedings Volume 13056, 130561E (2024) https://doi.org/10.1117/12.3013880

KEYWORDS: Aerosols, Atmospheric particles, Atmospheric modeling, Clouds, Infrared signatures, Mie scattering, Solids, Scattering, Modeling, Liquids

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The size of particles typically present in aerosol clouds are in the range of 0.1-10 μm, which is within one order of magnitude of the infrared (IR) wavelengths in the molecular “fingerprint” region (approx. 6-12 μm). This length scale is also close to the optical absorption depth for materials of interest. Consequently, IR scattering signatures of aerosols differ from those associated with reflectance from surfaces of bulk materials. Furthermore, the shape of particles is also a factor that affects IR scattering spectra. Accordingly, both aerosol particle size and morphology must be considered in the development of accurate models and reliable detection algorithms. This report describes recent advances concerning modeling of IR scattering signatures of micron-sized spherical particles (found in liquid aerosols) and irregularly-shaped particles (found in solid aerosols). In our model, spherical particles are modeled using Mie scattering theory while non-spherical ones require numerical modeling, using finite-difference time-domain (FDTD) solvers. The model inputs involve particle optical constants (complex index of refraction - n and k), aerosol concentration, particle size (diameter) distribution and various shape parameters (for solid aerosols only). Our model addresses two detection scenarios: one where the signatures consist of back-scattered light only and the other where the portion of the forward scattered light which (diffusely) reflects off background surfaces is also collected. We discuss the effect of particle size and shape distribution on the IR signatures of aerosol clouds. We report on efforts to optimize our model such that a large number of spectra can be generated quickly and efficiently, which is a requirement for use in detection algorithms, both for training and usage. We also present preliminary results on machine learning approaches to develop detection algorithms capable of detecting aerosol clouds that have variable IR signatures due to different particle/size distributions. In this paper, we focus on liquid aerosols, while solid aerosols are discussed in a related conference paper.

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