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DOI	10.1016/j.atmosres.2020.105269
	A two-moment machine learning parameterization of the autoconversion process
	Alfonso L.; Zamora J.M.
发表日期	2021
ISSN	0169-8095
卷号	249
英文摘要	Autoconversion is the mass transfer from cloud to precipitation water in an early stage of cloud development, and is the dominant process in the formation of embryonic droplets that trigger precipitation formation. The accurate parameterization of this process is key, in order to improve the interaction between cloud microphysics and cloud dynamics for models from cloud scale to the global climate scale. For model based parameterizations of the auto-conversion process, the usual approach to develop an autoconversion parameterization is by curve fitting the autoconversion rates obtained from simulations or numerical solutions of the kinetic collection equation under a wide range of initial conditions. However, in this case, the autoconversion is modeled by a function that is a nonlinear product of liquid water content and droplet concentration and depends on a small number of parameters. As a result, a large amount of scatter around the actual values can be obtained, indicating a weak relationship between actual and fitted autoconversion rates. The purpose of this paper is to analyze whether neural networks are better than traditional curve fitting or regression to obtain parameterizations of autoconversion. Then, a deep neural network was trained from an autconversion rates dataset generated by solving the kinetic collection equation for a wide range of droplet concentrations and liquid water contents. The obtained machine learned parameterization shows a very good match with actual rates calculated from the kinetic collection equation. © 2020 Elsevier B.V.
英文关键词	Autoconversion process; Bulk microphysical parameterizations; Cloud microphysics; Machine learning; Machine learning parameterizations; Neural networks
语种	英语
scopus关键词	Climate models; Curve fitting; Deep learning; Deep neural networks; Drops; Kinetics; Mass transfer; Neural networks; Precipitation (meteorology); Autoconversion parameterization; Cloud microphysics; Conversion process; Droplet concentration; Initial conditions; Liquid water content; Numerical solution; Precipitation formation; Parameterization; artificial neural network; cloud microphysics; concentration (composition); embryo; machine learning; parameterization; water content
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141661
作者单位	Autonomous University of Mexico City, Prolongacion San Isidro 151, Mexico City, 09790, Mexico; LUFAC Computación SA de CV, Savona 30, Fracc. Residencial Acoxpa, Mexico City, 14300, Mexico
推荐引用方式 GB/T 7714	Alfonso L.,Zamora J.M.. A two-moment machine learning parameterization of the autoconversion process[J],2021,249.
APA	Alfonso L.,&Zamora J.M..(2021).A two-moment machine learning parameterization of the autoconversion process.Atmospheric Research,249.
MLA	Alfonso L.,et al."A two-moment machine learning parameterization of the autoconversion process".Atmospheric Research 249(2021).