Articles | Volume 22, issue 8
https://doi.org/10.5194/acp-22-5175-2022
https://doi.org/10.5194/acp-22-5175-2022
Research article
 | 
20 Apr 2022
Research article |  | 20 Apr 2022

Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification

Xianda Gong, Heike Wex, Thomas Müller, Silvia Henning, Jens Voigtländer, Alfred Wiedensohler, and Frank Stratmann

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Latest update: 16 Apr 2024
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Short summary
We conducted 10 yr measurements to characterize the atmospheric aerosol at Cabo Verde. An unsupervised machine learning algorithm, K-means, was implemented to study the aerosol types. Cloud condensation nuclei number concentrations during dust periods were 2.5 times higher than marine periods. The long-term data sets, together with the aerosol classification, can be used as a basis to improve understanding of annual cycles of aerosol, and aerosol-cloud interactions in the North Atlantic.
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