DOI: 10.1175/JCLI-D-13-00604.1
Scopus记录号: 2-s2.0-84907042721
论文题名: Stochastic model output statistics for bias correcting and downscaling precipitation including extremes
作者: Wong G. ; Maraun D. ; Vrac M. ; Widmann M. ; Eden J.M. ; Kent T.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 18 起始页码: 6940
结束页码: 6959
语种: 英语
Scopus关键词: Down-scaling
; Model output statistics
; boundary condition
; climate modeling
; downscaling
; extreme event
; precipitation assessment
; precipitation intensity
; raingauge
; regional climate
; stochasticity
英文摘要: Precipitation is highly variable in space and time; hence, rain gauge time series generally exhibit additional random small-scale variability compared to area averages. Therefore, differences between daily precipitation statistics simulated by climate models and gauge observations are generally not only caused by model biases, but also by the corresponding scale gap. Classical bias correction methods, in general, cannot bridge this gap; they do not account for small-scale random variability and may produce artifacts. Here, stochastic model output statistics is proposed as a bias correction framework to explicitly account for random small-scale variability. Daily precipitation simulated by a regional climate model (RCM) is employed to predict the probability distribution of local precipitation. The pairwise correspondence between predictor and predictand required for calibration is ensured by driving the RCM with perfect boundary conditions. Wet day probabilities are described by a logistic regression, and precipitation intensities are described by a mixture model consisting of a gamma distribution for moderate precipitation and a generalized Pareto distribution for extremes. The dependence of the model parameters on simulated precipitation is modeled by a vector generalized linear model. The proposed model effectively corrects systematic biases and correctly represents local-scale random variability for most gauges. Additionally, a simplified model is considered that disregards the separate tail model. This computationally efficient model proves to be a feasible alternative for precipitation up to moderately extreme intensities. The approach sets a new framework for bias correction that combines the advantages of weather generators and RCMs. © 2014 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51307
Appears in Collections: 气候变化事实与影响
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作者单位: GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany; Laboratoire des Sciences du Climat et de l'Environnement, CEA Saclay, Gif-sur-Yvette, France; School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom; School of Mathematics, University of Leeds, Leeds, United Kingdom
Recommended Citation:
Wong G.,Maraun D.,Vrac M.,et al. Stochastic model output statistics for bias correcting and downscaling precipitation including extremes[J]. Journal of Climate,2014-01-01,27(18)