Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 15
Articles | Volume 24, issue 15
https://doi.org/10.5194/acp-24-8821-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-8821-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 15
Research article
 | 
12 Aug 2024
Research article |  | 12 Aug 2024

Improving the predictions of black carbon (BC) optical properties at various aging stages using a machine-learning-based approach

Baseerat Romshoo, Jaikrishna Patil, Tobias Michels, Thomas Müller, Marius Kloft, and Mira Pöhlker

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Short summary

Through the use of our machine-learning-based optical model, realistic BC morphologies can be incorporated into atmospheric science applications that require highly accurate results with minimal computational resources. The results of the study demonstrate that the predictions of single-scattering albedo (ω) and mass absorption cross-section (MAC) were improved over the conventional Mie-based predictions when using the machine learning method.

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