气候变化领域集成服务门户(CCPortal): Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

CCPortal

DOI	10.1029/2019MS001689
	Confronting the Challenge of Modeling Cloud and Precipitation Microphysics
	Morrison H.; van Lier-Walqui M.; Fridlind A.M.; Grabowski W.W.; Harrington J.Y.; Hoose C.; Korolev A.; Kumjian M.R.; Milbrandt J.A.; Pawlowska H.; Posselt D.J.; Prat O.P.; Reimel K.J.; Shima S.-I.; van Diedenhoven B.; Xue L.
发表日期	2020
ISSN	19422466
卷号	12 期号:8
英文摘要	In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle-based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next-generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process-level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle-based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods. ©2020. The Authors.
英文关键词	clouds; microphysics; modeling
语种	英语
scopus关键词	Climate models; Earth atmosphere; Inverse problems; Investments; Lagrange multipliers; Weather forecasting; Hierarchical approach; Laboratory experiments; Lagrangian particles; Microphysical process; Numerical weather forecasts; Precipitation microphysics; Probabilistic framework; Rigorous constraints; Precipitation (meteorology); cloud microphysics; hierarchical system; inverse problem; Lagrangian analysis; parameterization; precipitation assessment; probability; weather forecasting
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/156656
作者单位	National Center for Atmospheric Research, Boulder, CO, United States; NASA Goddard Institute for Space Studies and Center for Climate Systems Research, Columbia University, New York, NY, United States; NASA Goddard Institute for Space Studies, New York, NY, United States; Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, United States; Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany; Observation Based Research Section, Environment and Climate Change Canada, Toronto, ON, Canada; Atmospheric Numerical Prediction Research, Environment and Climate Change Canada, Dorval, QC, Canada; Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; North Carolina Institute for Climate Studies, North Carolina State University, Asheville, NC, United States; University of Hyogo and RIKEN Center for ...
推荐引用方式 GB/T 7714	Morrison H.,van Lier-Walqui M.,Fridlind A.M.,et al. Confronting the Challenge of Modeling Cloud and Precipitation Microphysics[J],2020,12(8).
APA	Morrison H..,van Lier-Walqui M..,Fridlind A.M..,Grabowski W.W..,Harrington J.Y..,...&Xue L..(2020).Confronting the Challenge of Modeling Cloud and Precipitation Microphysics.Journal of Advances in Modeling Earth Systems,12(8).
MLA	Morrison H.,et al."Confronting the Challenge of Modeling Cloud and Precipitation Microphysics".Journal of Advances in Modeling Earth Systems 12.8(2020).