DOI: 10.5194/hess-21-1693-2017
Scopus记录号: 2-s2.0-85016154908
论文题名: A combined statistical bias correction and stochastic downscaling method for precipitation
作者: Volosciuk C ; , Maraun D ; , Vrac M ; , Widmann M
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 3 起始页码: 1693
结束页码: 1719
语种: 英语
Scopus关键词: Calibration
; Pareto principle
; Regression analysis
; Stochastic systems
; Generalized Pareto Distributions
; Heavy precipitation
; Mediterranean region
; Mixture distributions
; Regional climate models
; Regression-based model
; Scale precipitation
; Summer precipitation
; Climate models
; calibration
; climate modeling
; correction
; downscaling
; experimental design
; precipitation (climatology)
; regional climate
; Mediterranean Region
; Scandinavia
英文摘要: Much of our knowledge about future changes in precipitation relies on global (GCMs) and/or regional climate models (RCMs) that have resolutions which are much coarser than typical spatial scales of precipitation, particularly extremes. The major problems with these projections are both climate model biases and the gap between gridbox and point scale. Wong et al. (2014) developed a model to jointly bias correct and downscale precipitation at daily scales. This approach, however, relied on pairwise correspondence between predictor and predictand for calibration, and, thus, on nudged simulations which are rarely available. Here we present an extension of this approach that separates the downscaling from the bias correction and in principle is applicable to free-running GCMs/RCMs. In a first step, we bias correct RCM-simulated precipitation against gridded observations at the same scale using a parametric quantile mapping (QMgrid) approach. In a second step, we bridge the scale gap: we predict local variance employing a regression-based model with coarse-scale precipitation as a predictor. The regression model is calibrated between gridded and point-scale (station) observations. For this concept we present one specific implementation, although the optimal model may differ for each studied location. To correct the whole distribution including extreme tails we apply a mixture distribution of a gamma distribution for the precipitation mass and a generalized Pareto distribution for the extreme tail in the first step. For the second step a vector generalized linear gamma model is employed. For evaluation we adopt the perfect predictor experimental setup of VALUE. We also compare our method to the classical QM as it is usually applied, i.e., between RCM and point scale (QMpoint). Precipitation is in most cases improved by (parts of) our method across different European climates. The method generally performs better in summer than in winter and in winter best in the Mediterranean region, with a mild winter climate, and worst for continental winter climate in Mid- and eastern Europe or Scandinavia. While QMpoint performs similarly (better for continental winter) to our combined method in reducing the bias and representing heavy precipitation, it is not capable of correctly modeling point-scale spatial dependence of summer precipitation. A strength of this two-step method is that the best combination of bias correction and downscaling methods can be selected. This implies that the concept can be extended to a wide range of method combinations. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79216
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; Wegener Center for Climate and Global Change, University of Graz, Graz, Austria; Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CNRS/IPSL, Gif-sur-Yvette, France; School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
Recommended Citation:
Volosciuk C,, Maraun D,, Vrac M,et al. A combined statistical bias correction and stochastic downscaling method for precipitation[J]. Hydrology and Earth System Sciences,2017-01-01,21(3)