The Science of NOAA's Operational Hydrologic Ensemble Forecast ServiceSource: Bulletin of the American Meteorological Society:;2013:;volume( 095 ):;issue: 001::page 79Author:Demargne, Julie
,
Wu, Limin
,
Regonda, Satish K.
,
Brown, James D.
,
Lee, Haksu
,
He, Minxue
,
Seo, Dong-Jun
,
Hartman, Robert
,
Herr, Henry D.
,
Fresch, Mark
,
Schaake, John
,
Zhu, Yuejian
DOI: 10.1175/BAMS-D-12-00081.1Publisher: American Meteorological Society
Abstract: tional 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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contributor author | Demargne, Julie | |
contributor author | Wu, Limin | |
contributor author | Regonda, Satish K. | |
contributor author | Brown, James D. | |
contributor author | Lee, Haksu | |
contributor author | He, Minxue | |
contributor author | Seo, Dong-Jun | |
contributor author | Hartman, Robert | |
contributor author | Herr, Henry D. | |
contributor author | Fresch, Mark | |
contributor author | Schaake, John | |
contributor author | Zhu, Yuejian | |
date accessioned | 2017-06-09T16:44:30Z | |
date available | 2017-06-09T16:44:30Z | |
date copyright | 2014/01/01 | |
date issued | 2013 | |
identifier issn | 0003-0007 | |
identifier other | ams-73291.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4215388 | |
description abstract | tional 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. | |
publisher | American Meteorological Society | |
title | The Science of NOAA's Operational Hydrologic Ensemble Forecast Service | |
type | Journal Paper | |
journal volume | 95 | |
journal issue | 1 | |
journal title | Bulletin of the American Meteorological Society | |
identifier doi | 10.1175/BAMS-D-12-00081.1 | |
journal fristpage | 79 | |
journal lastpage | 98 | |
tree | Bulletin of the American Meteorological Society:;2013:;volume( 095 ):;issue: 001 | |
contenttype | Fulltext |