globalchange  > 影响、适应和脆弱性
DOI: 10.1002/jgrd.50408
论文题名:
Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method
作者: Shen S.S.P.; Velado M.; Somerville R.C.J.; Kooperman G.J.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期:10
起始页码: 4644
结束页码: 4656
语种: 英语
英文关键词: active remote sensing of clouds ; Bayesian posterior estimate ; cloud fraction ; probabilistic density function ; Southern Great Plains ; total sky image
Scopus关键词: Bayesian networks ; Cameras ; Computer simulation ; Estimation ; Normal distribution ; Planning ; Probability density function ; Regression analysis ; Remote sensing ; Bayesian ; Cloud fraction ; Probabilistic density function ; Southern great plains ; total sky image ; Data processing ; atmospheric modeling ; cloud ; data set ; feasibility study ; image resolution ; probability density function ; regression analysis ; remote sensing ; Great Plains ; United States
英文摘要: We describe and evaluate a novel method to blend two observed cloud fraction (CF) datasets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof-of-concept study, we illustrate the approach using specific observational datasets from the U. S. Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains site in the central United States, but the method is quite general and is readily applicable to other datasets. The total sky image (TSI) camera observations are used to determine the prior distribution. A regression model and the active remote sensing of clouds (ARSCL) radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (pdf) of the CF whose mean is taken to be the optimal blend of the two observations. The data at hourly, daily, 5-day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the CFs are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (a) the Beta distribution is a reasonable model for CF for both short- and long-time means, the 5-day data are skewed right, and the annual data are almost normally distributed, and (b) the Bayesian method developed successfully yields a pdf of CF, rather than a deterministic CF value, and it is feasible to blend the TSI and ARSCL data with a capability for bias correction. Key Points Bayesian blending of camera and radar data to form a cloud fraction pdfFeasibility study of a method on probabilistic assessment of cloud fractionsBeta distribution as a model for cloud fractions ©2013. American Geophysical Union. All Rights Reserved.
资助项目: AGS-1015926 ; DE-FG02-09ER64764 ; DE-FG02-09ER647645 ; DE-SC0002003
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63754
Appears in Collections:影响、适应和脆弱性
气候减缓与适应

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作者单位: Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Drive, GMCS 415, San Diego, CA, 92182-7720, United States; Scripps Institution of Oceanography, University of California San Diego, San Diego CA, United States

Recommended Citation:
Shen S.S.P.,Velado M.,Somerville R.C.J.,et al. Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(10)
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