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Julie Demargne
,
Mary Mullusky
,
Kevin Werner
,
Thomas Adams
,
Scott Lindsey
,
Noreen Schwein
,
William Marosi
, and
Edwin Welles

Forecast verification in operational hydrology has been very limited to date, mainly due to the complexity of verifying both forcing input forecasts and hydrologic forecasts on multiple space-time scales. However, forecast verification needs to be the driver in both hydrologic research and operations to help advance the understanding of predictability and help the diverse users better utilize the river forecasts. Therefore, in NOAA's National Weather Service, the Hydrologic Services Program is developing a comprehensive river forecast verification service to routinely and systematically verify all hydrometeorological and hydrologic forecasts. This verification service will include capabilities for archiving forecast and observed data, evaluating logistical properties of the forecast services, computing a variety of verification metrics to evaluate the different aspects of forecast quality, displaying and disseminating verification data and metrics, and analyzing the sources of forecast skill and uncertainty through the use of multiple forecast and hindcast scenarios. This paper describes ongoing and planned verification activities for enhancing the collaboration between the meteorological and hydrologic research and operational communities to quantify forecast improvements based on rigorous forecast verification.

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Julie Demargne
,
Limin Wu
,
Satish K. Regonda
,
James D. Brown
,
Haksu Lee
,
Minxue He
,
Dong-Jun Seo
,
Robert Hartman
,
Henry D. Herr
,
Mark Fresch
,
John Schaake
, and
Yuejian Zhu

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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