Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 24
Articles | Volume 18, issue 24
https://doi.org/10.5194/amt-18-7767-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-7767-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 24
Research article
 | 
19 Dec 2025
Research article |  | 19 Dec 2025

Unsupervised classification of absorbing aerosols detected by the Single Particle Soot Photometer

Aaryan Doshi and Kara D. Lamb

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Short summary
Aerosols that absorb sunlight play key role in Earth's climate. To improve detection of light-absorbing aerosols measured by the Single Particle Soot Photometer, we explore unsupervised machine learning. Unlike earlier methods that require labeled training data, our approach learns patterns directly from unlabeled data. This makes it more applicable to atmospheric observations. We show this method can be used to quantify how similar aerosols are to one another and improve aerosol classification.
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