DOI: 10.5194/hess-22-1831-2018
Scopus记录号: 2-s2.0-85043768998
论文题名: Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system
作者: Sharma S ; , Siddique R ; , Reed S ; , Ahnert P ; , Mendoza P ; , Mejia A
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期: 3 起始页码: 1831
结束页码: 1849
语种: 英语
Scopus关键词: Atmospheric temperature
; Forecasting
; Stream flow
; Surface properties
; Auto regressive models
; Distributed hydrologic model
; Ensemble forecast systems
; Ensemble prediction systems
; Multi-sensor precipitations
; National centers for environmental predictions
; Near surface temperature
; Precipitation forecast
; Weather forecasting
; data processing
; ensemble forecasting
; hydrological modeling
; hydrometeorology
; model
; NOAA satellite
; prediction
; statistical analysis
; streamflow
; vector autoregression
; weather
英文摘要: The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short-to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (>3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative. © 2018 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79357
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, United States; Northeast Climate Science Center, University of Massachusetts, Amherst, MA, United States; National Weather Service, Middle Atlantic River Forecast Center, State College, PA, United States; Advanced Mining Technology Center (AMTC), Universidad de Chile, Santiago, Chile
Recommended Citation:
Sharma S,, Siddique R,, Reed S,et al. Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system[J]. Hydrology and Earth System Sciences,2018-01-01,22(3)