Collaborative Research: Using ARM Observations to Evaluate GCM Cloud Statistics for Development of Stochastic Cloud-Radiation Parameterizations

doi:10.2172/1327846

GSTDTAP > 地球科学

DOI	10.2172/1327846
报告编号	DE--FG02-09ER64765
来源ID	OSTI ID: 1327846
	Collaborative Research: Using ARM Observations to Evaluate GCM Cloud Statistics for Development of Stochastic Cloud-Radiation Parameterizations
	Shen, Samuel S. P. [San Diego State Univ., CA (United States)]
	2013-09-01
出版年	2013
页数	4
语种	英语
国家	美国
领域	地球科学
英文摘要	The long-range goal of several past and current projects in our DOE-supported research has been the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data, and the implementation and testing of these parameterizations in global models. The main objective of the present project being reported on here has been to develop and apply advanced statistical techniques, including Bayesian posterior estimates, to diagnose and evaluate features of both observed and simulated clouds. The research carried out under this project has been novel in two important ways. The first is that it is a key step in the development of practical stochastic cloud-radiation parameterizations, a new category of parameterizations that offers great promise for overcoming many shortcomings of conventional schemes. The second is that this work has brought powerful new tools to bear on the problem, because it has been an interdisciplinary collaboration between a meteorologist with long experience in ARM research (Somerville) and a mathematician who is an expert on a class of advanced statistical techniques that are well-suited for diagnosing model cloud simulations using ARM observations (Shen). The motivation and long-term goal underlying this work is the utilization of stochastic radiative transfer theory (Lane-Veron and Somerville, 2004; Lane et al., 2002) to develop a new class of parametric representations of cloud-radiation interactions and closely related processes for atmospheric models. The theoretical advantage of the stochastic approach is that it can accurately calculate the radiative heating rates through a broken cloud layer without requiring an exact description of the cloud geometry.
英文关键词	cloud statistics stochastic cloud-radiation parameterizations ARM data Bayesian method
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来源平台	US Department of Energy (DOE)
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/6046
专题	地球科学
推荐引用方式 GB/T 7714	Shen, Samuel S. P. [San Diego State Univ., CA . Collaborative Research: Using ARM Observations to Evaluate GCM Cloud Statistics for Development of Stochastic Cloud-Radiation Parameterizations,2013.