Preprints
https://doi.org/10.5194/acp-2021-743
https://doi.org/10.5194/acp-2021-743

  04 Oct 2021

04 Oct 2021

Review status: this preprint is currently under review for the journal ACP.

An unsupervised machine-learning-based classification of aerosol microphysical properties over 10 years at Cabo Verde

Xianda Gong, Heike Wex, Thomas Müller, Silvia Henning, Jens Voigtländer, Alfred Wiedensohler, and Frank Stratmann Xianda Gong et al.
  • Department Experimental Aerosol and Cloud Microphysics, Leibniz-Institute for Tropospheric Research (TROPOS), Leipzig, 04318, Germany

Abstract. The Cape Verde Atmospheric Observatory (CVAO), which is influenced by both, marine and desert dust air masses, has been used for long-term measurements of different properties of the atmospheric aerosol from 2008 to 2017. These properties include particle number size distributions (PNSD), light absorbing carbon (LAC) and concentrations of cloud condensation nuclei (CCN) together with their hygroscopicity. Here we summarize the results obtained for these properties and use an unsupervised machine learning algorithm for the classification of aerosol types. Five types of aerosols, i.e., marine, freshly-formed, mixture, moderate dust and heavy dust, were classified. Air masses during marine periods are from the Atlantic Ocean and during dust periods are from the Sahara. Heavy dust was more frequently present during wintertime, whereas the clean marine periods were more frequently present during springtime. It was observed that during the dust periods CCN number concentrations at a supersaturation of 0.30 % are roughly 2.5 times higher than during marine periods, but the hygroscopicity (κ) of particles in the size range from ∼30 to ∼175 nm during marine and dust periods are comparable. The long-term data presented here, together with the aerosol classification, can be used as a base to improve our understanding of annual cycles of the atmospheric aerosol in the eastern tropical Atlantic and on aerosol-cloud interactions and it can be used as a base for driving, evaluating and constraining atmospheric model simulations.

Xianda Gong et al.

Status: open (until 15 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Xianda Gong et al.

Xianda Gong et al.

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Short summary
We conducted 10-year 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 are 2.5 times higher than marine periods. The long-term data sets, together with the aerosol classification, can be used as a base to improve our understanding of annual cycles of aerosol, and aerosol-cloud interactions in the North Atlantic.
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