DOI: 10.1175/JCLI-D-14-00237.1
Scopus记录号: 2-s2.0-84926034747
论文题名: Probabilistic multisite statistical downscaling for daily precipitation using a Bernoulli-generalized pareto multivariate autoregressive model
作者: Ben Alaya M.A. ; Chebana F. ; Ouarda T.B.M.J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 6 起始页码: 2349
结束页码: 2364
语种: 英语
Scopus关键词: Errors
; Linear regression
; Mean square error
; Pareto principle
; Probability density function
; Probability distributions
; Regression analysis
; Stochastic systems
; Time series
; Time series analysis
; Conditional probability density
; Generalized Pareto Distributions
; Multiple linear regressions
; Multivariate autoregressive
; Multivariate autoregressive models
; Probability forecasts/models/distribution
; Reanalysis
; Statistical forecasting
; Stochastic models
; climate modeling
; downscaling
; multiple regression
; precipitation (climatology)
; precipitation intensity
; probability
; regression analysis
; stochasticity
; weather forecasting
; Canada
; Quebec [Canada]
英文摘要: A Bernoulli-generalized Pareto multivariate autoregressive (BMAR) model is proposed in this paper for multisite statistical downscaling of daily precipitation. The proposed model relies on a probabilistic framework to describe the conditional probability density function of precipitation at each station for a given day and handles multivariate dependence in both time and space using a multivariate autoregressive model. Within a probabilistic framework, BMAR employs a regression model whose outputs are parameters of the mixed Bernoulli-generalized Pareto distribution. As a stochastic component, the BMAR employs a latent multivariate autoregressive Gaussian field to preserve lag-0 and lag-1 cross correlations of precipitation at multiple sites. The proposed model is applied for the downscaling of AOGCM data to daily precipitation in the southern part of Québec, Canada. Reanalysis products are used in this study to assess the potential of the proposed method. Based on the mean errors (MEs), the root-mean-square errors (RMSEs), precipitation indices, and the ability to preserve lag-0 and lag-1 cross correlation, results of the study indicate the superiority of the proposed model over a multivariate multiple linear regression (MMLR) model and a multisite hybrid statistical downscaling procedure that combines MMLR and stochastic generator schemes. © 2015 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50645
Appears in Collections: 气候变化事实与影响
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作者单位: Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec City, QC, Canada; Institute Center for Water and Environment (iWATER), Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates; Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec City, QC, Canada
Recommended Citation:
Ben Alaya M.A.,Chebana F.,Ouarda T.B.M.J.. Probabilistic multisite statistical downscaling for daily precipitation using a Bernoulli-generalized pareto multivariate autoregressive model[J]. Journal of Climate,2015-01-01,28(6)