Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 7
Articles | Volume 19, issue 7
https://doi.org/10.5194/amt-19-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 7
Research article
 | 
16 Apr 2026
Research article |  | 16 Apr 2026

An ensemble machine learning method to retrieve aerosol parameters from ground-based Sun-sky photometer measurements

Qiurui Li, Zhongxia Sun, Meijing Liu, Huizheng Che, Yu Zheng, and Jing Li

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Short summary
We present a fast, interpretable machine learning method to retrieve key aerosol parameters from ground-based Sun-sky photometer measurements. Trained on simulated data covering diverse aerosol and atmospheric conditions, ensuring robustness and physical consistency. Applied to real observations, it agrees well with AERONET products and reduces computation time by orders of magnitude, offering a practical tool for monitoring aerosols and their effects on air quality and climate.
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