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The Science of NOAA's Operational Hydrologic Ensemble Forecast Service
被引:210
作者:
Demargne, Julie
[1
,2
]
Wu, Limin
[1
,3
]
Regonda, Satish K.
[1
,4
]
Brown, James D.
[5
]
Lee, Haksu
[3
,6
]
He, Minxue
[1
,4
]
Seo, Dong-Jun
[7
]
Hartman, Robert
[8
]
Herr, Henry D.
[1
]
Fresch, Mark
[1
]
Schaake, John
Zhu, Yuejian
[9
]
机构:
[1] NOAA, Natl Weather Serv, Off Hydrol Dev, Silver Spring, MD 20910 USA
[2] HYDRIS Hydrol, St Mathieu De Treviers, France
[3] LEN Technol, Oak Hill, VA USA
[4] Riverside Technol Inc, Ft Collins, CO USA
[5] Hydrol Solut Ltd, Southampton, Hants, England
[6] NOAA, Natl Weather Serv, Off Climate Water & Weather Serv, Silver Spring, MD 20910 USA
[7] Univ Texas Arlington, Dept Civil Engn, Arlington, TX 76019 USA
[8] NOAA, Calif Nevada River Forecast Ctr, Natl Weather Serv, Sacramento, CA USA
[9] NOAA, Environm Modeling Ctr, Natl Ctr Environm Predict, Natl Weather Serv, College Pk, MD USA
基金:
美国海洋和大气管理局;
关键词:
MODEL CONDITIONAL PROCESSOR;
BIAS CORRECTION;
NONPARAMETRIC POSTPROCESSOR;
PRECIPITATION FORECASTS;
PREDICTIVE UNCERTAINTY;
OUTPUT STATISTICS;
DATA ASSIMILATION;
SOIL-MOISTURE;
STREAMFLOW;
SYSTEM;
D O I:
10.1175/BAMS-D-12-00081.1
中图分类号:
P4 [大气科学(气象学)];
学科分类号:
0706 ;
070601 ;
摘要:
NOAA's National Weather Service (NWS) is implementing a short- to long-range Hydrologic Ensemble Forecast Service (HEFS). The HEFS addresses the need to quantify uncertainty in hydrologic forecasts for flood risk management, water supply management, streamflow regulation, recreation planning, and ecosystem management, among other applications. The HEFS extends the existing hydrologic ensemble services to include short-range forecasts, incorporate additional weather and climate information, and better quantify the major uncertainties in hydrologic forecasting. It provides, at forecast horizons ranging from 6 h to about a year, ensemble forecasts and verification products that can be tailored to users' needs. Based on separate modeling of the input and hydrologic uncertainties, the HEFS includes 1) the Meteorological Ensemble Forecast Processor, which ingests weather and climate forecasts from multiple numerical weather prediction models to produce bias-corrected forcing ensembles at the hydrologic basin scales; 2) the Hydrologic Processor, which inputs the forcing ensembles into hydrologic, hydraulic, and reservoir models to generate streamflow ensembles; 3) the hydrologic Ensemble Postprocessor, which aims to account for the total hydrologic uncertainty and correct for systematic biases in streamflow; 4) the Ensemble Verification Service, which verifies the forcing and streamflow ensembles to help identify the main sources of skill and error in the forecasts; and 5) the Graphics Generator, which enables forecasters to create a large array of ensemble and related products. Examples of verification results from multiyear hind-casting illustrate the expected performance and limitations of HEFS. Finally, future scientific and operational challenges to fully embrace and practice the ensemble paradigm in hydrology and water resources services are discussed.
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页码:79 / 98
页数:20
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