DOI: 10.1002/jgrd.50628
论文题名: Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: Implications for seasonal hydrologic forecast skill
作者: Shukla S. ; Lettenmaier D.P.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 19 起始页码: 10770
结束页码: 10790
语种: 英语
英文关键词: CFS Forecasts
; Dynamical Downscaling
; Seasonal Hydrologic Forecasts
; Statistical Downscaling
Scopus关键词: Dynamical systems
; Hydrology
; Soil moisture
; Statistics
; Downscaling methods
; Dynamical downscaling
; Hydrologic forecasting
; Hydrology modeling
; National centers for environmental predictions
; Regional climate models
; Snow water equivalent
; Statistical downscaling
; Forecasting
; assessment method
; climate modeling
; downscaling
; forecasting method
; hydrological modeling
; observational method
; prediction
; runoff
; snow water equivalent
; soil moisture
; winter
; United States
英文摘要: We assess the value of dynamical versus statistical downscaling of National Centers for Environmental Prediction's (NCEP) Climate Forecast System (CFS) winter season forecasts for seasonal hydrologic forecasting. Dynamically downscaled CFS forecasts for 1 December to 30 April of 1982-2003 were obtained from the Multi-RCM Ensemble Downscaling (MRED) project that used multiple Regional Climate Models (RCMs) to downscale CFS forecasts. Statistical downscaling of CFS forecasts was achieved by a much simpler bias correction and spatial downscaling method. We evaluate forecast accuracy of runoff (RO), soil moisture (SM), and snow water equivalent produced by a hydrology model forced with dynamically (the MRED forecasts) and statistically downscaled CFS forecasts in comparison with predictions of those variables produced by forcing the same hydrology model with gridded observations (reference data set). Our results show that the MRED forecasts produce modest skill beyond what results from statistical downscaling of CFS. Although the improvement in hydrologic forecast skill associated with the ensemble average of the MRED forecasts (Multimodel) relative to statistical downscaled CFS forecasts is field significant for RO and SM forecasts with up to 3 months lead, the region of improvement is mainly limited to parts of the northwest and north central U.S. In general, one or more RCMs outperform the other RCMs as well as the Multimodel. Hence, we argue that careful selection of RCMs (based on their hindcast skill over any given region) is critical to improving hydrologic forecast skill using dynamical downscaling. Key Points Evaluation of dynamical vs statistical downscaling of CFS Dynamical downscaling does somewhat improves the skill Careful selection of RCMs is critical ©2013. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63241
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 Civil and Environmental Engineering, University of Washington, 202D Wilson Ceramics Laboratory, Box 352700, Seattle, WA, United States; Department of Geography, University of California, Santa Barbara CA, United States
Recommended Citation:
Shukla S.,Lettenmaier D.P.. Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: Implications for seasonal hydrologic forecast skill[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(19)