气候变化领域集成服务门户(CCPortal): Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques

CCPortal

DOI	10.1029/2020GL091236
	Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques
	Chiu J.C.; Yang C.K.; van Leeuwen P.J.; Feingold G.; Wood R.; Blanchard Y.; Mei F.; Wang J.
发表日期	2021
ISSN	0094-8276
卷号	48 期号:2
英文摘要	We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine-learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process. © 2020. The Authors.
英文关键词	Atmospheric radiation; Machine learning; Parameterization; Uncertainty analysis; Accretion rate; Atmospheric radiation measurement programs; Cloud microphysical process; Droplet number; Field campaign; Number concentration; Optimization techniques; Probe measurements; Parameter estimation
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169170
作者单位	Department of Atmospheric Science, Colorado State University, Fort Collins, CO, United States; Department of Meteorology, University of ReadingReading, United Kingdom; NOAA Earth System Research Laboratory, Boulder, CO, United States; Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States; Department of Earth and Atmospheric Sciences, ESCER Centre, University of Quebec at Montreal, Montreal, QC, Canada; Pacific Northwest National Laboratory, Richland, WA, United States; Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in Saint Louis, Saint Louis, MO, United States
推荐引用方式 GB/T 7714	Chiu J.C.,Yang C.K.,van Leeuwen P.J.,et al. Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques[J],2021,48(2).
APA	Chiu J.C..,Yang C.K..,van Leeuwen P.J..,Feingold G..,Wood R..,...&Wang J..(2021).Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.Geophysical Research Letters,48(2).
MLA	Chiu J.C.,et al."Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques".Geophysical Research Letters 48.2(2021).