Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data

•Zernike moments (ZM) are suitable for the analysis of 2DLSP with high symmetry.•ZM and machine learning methods recover particles’ properties from their 2DLSPs.•Orientation, aspect ratio, size are recovered from 2DLSP for smooth hexagonal prisms. Better understanding and characterization of cloud p...

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Bibliographic Details
Published in:Journal of quantitative spectroscopy & radiative transfer 2017-11, Vol.201, p.115-127
Main Authors: Salawu, Emmanuel Oluwatobi, Hesse, Evelyn, Stopford, Chris, Davey, Neil, Sun, Yi
Format: Article
Language:English
Subjects:
Aspect ratio
Hexagonal prisms
Ice crystals
Machine learning
Ray tracing with diffraction on facets
Scattering pattern
Size
Zernike moments
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Summary:•Zernike moments (ZM) are suitable for the analysis of 2DLSP with high symmetry.•ZM and machine learning methods recover particles’ properties from their 2DLSPs.•Orientation, aspect ratio, size are recovered from 2DLSP for smooth hexagonal prisms. Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter
ISSN:0022-4073
1879-1352
DOI:10.1016/j.jqsrt.2017.07.001