气候变化领域集成服务门户(CCPortal): New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra

CCPortal

DOI	10.5194/acp-22-7405-2022
	New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra
	Zhu, Zeen; Kollias, Pavlos; Luke, Edward; Yang, Fan
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	7405
结束页码	7416
卷号	22 期号:11 页码:12
英文摘要	The detection of the early growth of drizzle particles in marine stratocumulus clouds is important for studying the transition from cloud water to rainwater. Radar reflectivity is commonly used to detect drizzle; however, its utility is limited to larger drizzle particles. Alternatively, radar Doppler spectrum skewness has proven to be a more sensitive quantity for the detection of drizzle embryos. Here, a machine learning (ML)-based technique that uses radar reflectivity and skewness for detecting small drizzle particles is presented. Aircraft in situ measurements are used to develop and validate the ML algorithm. The drizzle detection algorithm is applied to three Atmospheric Radiation Measurement (ARM) observational campaigns to investigate the drizzle occurrence in marine boundary layer clouds. It is found that drizzle is far more ubiquitous than previously thought; the traditional radar-reflectivity-based approach significantly underestimates the drizzle occurrence, especially in thin clouds with liquid water paths lower than 50 g m(-2). Furthermore, the drizzle occurrence in marine boundary layer clouds differs among the three ARM campaigns, indicating that the drizzle formation, which is controlled by the microphysical process, is regime dependent. A complete understanding of the drizzle distribution climatology in marine stratocumulus clouds calls for more observational campaigns and continuing investigations.
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:000808033900001
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/272995
作者单位	State University of New York (SUNY) System; State University of New York (SUNY) Stony Brook
推荐引用方式 GB/T 7714	Zhu, Zeen,Kollias, Pavlos,Luke, Edward,et al. New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra[J],2022,22(11):12.
APA	Zhu, Zeen,Kollias, Pavlos,Luke, Edward,&Yang, Fan.(2022).New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(11),12.
MLA	Zhu, Zeen,et al."New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.11(2022):12.