气候变化领域集成服务门户(CCPortal): Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification

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DOI	10.5194/acp-22-5175-2022
	Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification
	Gong, Xianda; Wex, Heike; Mueller, Thomas; Henning, Silvia; Voigtlaender, Jens; Wiedensohler, Alfred; Stratmann, Frank
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	5175
结束页码	5194
卷号	22 期号:8 页码:20
英文摘要	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 Desert. 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 % were roughly 2.5 times higher than during marine periods, but the hygroscopicity (kappa) of particles in the size range from similar to 30 to similar to 175 nm during marine and dust periods were comparable. The long-term data presented here, together with the aerosol classification, can be used as a basis to improve our understanding of annual cycles of the atmospheric aerosol in the eastern tropical Atlantic Ocean and on aerosol-cloud interactions and it can be used as a basis for driving, evaluating and constraining atmospheric model simulations.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000783935600001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273021
作者单位	Leibniz Institut fur Tropospharenforschung (TROPOS)
推荐引用方式 GB/T 7714	Gong, Xianda,Wex, Heike,Mueller, Thomas,et al. Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification[J],2022,22(8):20.
APA	Gong, Xianda.,Wex, Heike.,Mueller, Thomas.,Henning, Silvia.,Voigtlaender, Jens.,...&Stratmann, Frank.(2022).Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(8),20.
MLA	Gong, Xianda,et al."Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.8(2022):20.