DOI: 10.1175/JCLI-D-13-00333.1
Scopus记录号: 2-s2.0-84899123353
论文题名: Probabilistic gaussian copula regression model for multisite and multivariable downscaling
作者: Ben Alaya M.A. ; Chebana F. ; Ouarda T.B.M.J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 9 起始页码: 3331
结束页码: 3347
语种: 英语
Scopus关键词: Gaussian distribution
; Mathematical models
; Multivariant analysis
; Regression analysis
; Atmosphere ocean general circulation models
; Climate characteristics
; Daily precipitations
; Marginal distribution
; Maximum temperature
; Probabilistic framework
; Several variables
; Statistical downscaling
; Multivariable systems
; air temperature
; atmospheric general circulation model
; downscaling
; Gaussian method
; modeling
; multivariate analysis
; oceanic general circulation model
; precipitation (climatology)
; probability
; regression analysis
; Canada
; Quebec [Canada]
英文摘要: Atmosphere-ocean general circulation models (AOGCMs) are useful to simulate large-scale climate evolutions. However, AOGCM data resolution is too coarse for regional and local climate studies. Downscaling techniques have been developed to refine AOGCM data and provide information at more relevant scales. Among a wide range of available approaches, regression-based methods are commonly used for downscaling AOGCM data. When several variables are considered at multiple sites, regression models are employed to reproduce the observed climate characteristics at small scale, such as the variability and the relationship between sites and variables. This study introduces a probabilistic Gaussian copula regression (PGCR) model for simultaneously downscaling multiple variables at several sites. The proposed PGCR model relies on a probabilistic framework to specify the marginal distribution for each downscaled variable at a given day through AOGCM predictors, and handles multivariate dependence between sites and variables using a Gaussian copula. The proposed model is applied for the downscaling of AOGCM data to daily precipitation and minimum and maximum temperatures in the southern part of Quebec, Canada. Reanalysis products are used in this study to assess the potential of the proposed method. Results of the study indicate the superiority of the proposed model over classical regression-based methods and a multivariate multisite statistical downscaling model. © 2014 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51232
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec, Quebec, Canada; Institute Center for Water and Environment (iWater), Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates
Recommended Citation:
Ben Alaya M.A.,Chebana F.,Ouarda T.B.M.J.. Probabilistic gaussian copula regression model for multisite and multivariable downscaling[J]. Journal of Climate,2014-01-01,27(9)