How machine learning approaches are useful in computing the optical properties of non-spherical particles across a broad range of size parameters?-Reference-Cited by-同舟云学术

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How machine learning approaches are useful in computing the optical properties of non-spherical particles across a broad range of size parameters?

Published:2024-09 Issue: Volume:323 Page:109057
ISSN:0022-4073
Container-title:Journal of Quantitative Spectroscopy and Radiative Transfer
language:en
Short-container-title:Journal of Quantitative Spectroscopy and Radiative Transfer

Author:

Bi Lei^ORCID,Xi Yue,Han Wei,Du Zhenhong

Publisher

Elsevier BV

Reference82 articles.

1. Atmospheric radiation: theoretical basis;Goody,1989

2. An introduction to atmospheric radiation;Liou,2002

3. Light scattering by nonspherical particles: theory, measurements, and applications;Mishchenko,2000

4. On the radiative properties of ice clouds: light scattering, remote sensing, and radiation parameterization;Yang;Adv Atmos Sci,2015

5. Advances in atmospheric radiation: theory, model, and applications. Part I: atmospheric gas absorption and particle scattering;Zhang;J Meteor Res,2024

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