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

Autor:	Evelyn Hesse, Emmanuel Oluwatobi Salawu, Yi Sun, Neil Davey, Chris Stopford
Rok vydání:	2017
Předmět:	Radiation Materials science 010504 meteorology & atmospheric sciences Ice crystals Hexagonal crystal system business.industry Scattering Resolution (electron density) Climate change Cloud computing 01 natural sciences Aspect ratio (image) Atomic and Molecular Physics and Optics Physics::Geophysics Characterization (materials science) Computational physics 010309 optics Optics 0103 physical sciences business Physics::Atmospheric and Oceanic Physics Spectroscopy 0105 earth and related environmental sciences
Zdroj:	Journal of Quantitative Spectroscopy and Radiative Transfer. 201:115-127
ISSN:	0022-4073
Popis:	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
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::91deb370720abf2dbd7b7248cd76ab92 https://doi.org/10.1016/j.jqsrt.2017.07.001 Zobrazit plný text záznamu Full Text from ScienceDirect