气候变化领域集成服务门户(CCPortal): A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds

CCPortal

DOI	10.1029/2019MS001798
	A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds
	Hagos S.; Feng Z.; Plant R.S.; Protat A.
发表日期	2020
ISSN	19422466
卷号	12 期号:3
英文摘要	Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly varying large-scale environment is in statistical equilibrium with a large number of small and short-lived convective clouds. They fail to capture nonequilibrium transitions such as the diurnal cycle and the formation of mesoscale convective systems as well as observed precipitation statistics and extremes. Informed by analysis of radar observations, cloud-permitting model simulation, theory, and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds, with the goal of informing the representation of these interactions in global climate models. Fifteen wet seasons of precipitating cloud observations by a C-band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equations relating large-scale forcing, mass flux, the convective cell size distribution, and the stratiform area. Under realistic large-scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a few convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems. ©2020. The Authors.
英文关键词	convective clouds; machine learning; organization; population dynamics; stratiform clouds; tropical convection
语种	英语
scopus关键词	Climate models; Cytology; Embedded systems; Learning algorithms; Radar; Stochastic models; Stochastic systems; Storms; Convective mass flux; Global climate model; Mesoscale Convective System; Nonequilibrium transition; Population dynamics models; Precipitating clouds; Statistical equilibrium; Transition functions; Machine learning; algorithm; atmospheric convection; atmospheric forcing; computer simulation; global climate; machine learning; mesoscale meteorology; precipitation assessment; radar altimetry; stratiform cloud; Australia
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156743
作者单位	Atmospheric Sciences and Global Change, Pacific Northwest National Laboratory, Richland, WA, United States; Department of Meteorology, University of Reading, Reading, United Kingdom; Australian Bureau of Meteorology, Melbourne, VIC, Australia
推荐引用方式 GB/T 7714	Hagos S.,Feng Z.,Plant R.S.,et al. A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds[J],2020,12(3).
APA	Hagos S.,Feng Z.,Plant R.S.,&Protat A..(2020).A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds.Journal of Advances in Modeling Earth Systems,12(3).
MLA	Hagos S.,et al."A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds".Journal of Advances in Modeling Earth Systems 12.3(2020).