The Science of Noaa's Operational Hydrologic Ensemble Forecast Service
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THE SCIENCE OF NOAA’S OPERATIONAL HYDROLOGIC ENSEMBLE FORECAST SERVICE BY 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 HEFS extends hydrologic ensemble services from 6-hour to year-ahead forecasts and includes additional weather and climate information as well as improved quantification of major uncertainties. s no forecast is complete without a description needs for risk-based management of water resources of its uncertainty (National Research Council and hazards across weather and climate scales have A of the National Academies 2006), it is necessary, increased, the research and operational communi- for both atmospheric and hydrologic predictions, to ties have been actively working on integration of the quantify and propagate uncertainty from various NWP ensembles into hydrologic ensemble predic- sources in the forecasting system. For informed risk- tion systems and quantification of all major sources based decision making, such integrated uncertainty of uncertainty in such systems. In particular, the information needs to be communicated to forecast- Hydrological Ensemble Prediction Experiment ers and users effectively. In an operational environ- (HEPEX; www.hepex.org/), launched in 2004, ment, ensembles are an effective means of producing has facilitated communications and collaborations uncertainty-quantified forecasts. Ensemble forecasts among the atmospheric community, the hydrologic can be ingested in a user’s downstream application community, and the forecast users toward improving (e.g., reservoir management decision support system) ensemble forecasts and demonstrating their utility in and used to derive probability statements about the decision making in water management (Schaake et al. likelihood of specific future events (e.g., probabil- 2007b; Thielen et al. 2008; Schaake et al. 2010). ity of exceeding a flood threshold). Atmospheric Ensemble approaches hold great potential for ensemble forecasts have been routinely produced by operational hydrologic forecasting. As demonstrated operational Numerical Weather Prediction (NWP) with atmospheric ensemble forecasts, the estimates of centers for two decades. Hydrologic ensemble fore- predictive uncertainty provide forecasters and users casts for long ranges have been initially based on with objective guidance on the level of confidence historical observations of precipitation and tem- that they may place in the forecasts. The end users perature as plausible future inputs (e.g., Day 1985) can decide to take action based on their risk tolerance. in an attempt to account for the uncertainty at the Furthermore, by modeling uncertainty, hydrologic climate time scales. Ensemble forecasts generated in forecasters can maximize the utility of weather and this fashion were considered viable beyond 30 days climate forecasts, which are generally highly uncer- where the climatic uncertainty would dominate tain and noisy (Buizza et al. 2005). With the major other uncertainty sources. More recently, as the uncertainties quantified and their relative importance AMERICAN METEOROLOGICAL SOCIETY JANUARY 2014 | 79 Unauthenticated | Downloaded 10/10/21 01:09 PM UTC analyzed, ensemble forecasting helps identify areas of hydrologic ensemble forecast products in various where investments in forecast systems and processes downstream applications where decision support will have the greatest benefit. systems could be “trained” (van Andel et al. 2008). Development and implementation of hydrologic In the National Oceanic and Atmospheric ensemble prediction systems is still ongoing and Administration (NOAA)’s National Weather Service hence only limited operational experience exists. (NWS), an end-to-end Hydrologic Ensemble Forecast A number of case studies using experimental and Service (HEFS) is currently being implemented as (pre) operational systems, however, have demon- part of the Advanced Hydrologic Prediction Service strated their potential benefits (see, e.g., Cloke and (AHPS; McEnery et al. 2005) to address a variety of Pappenberger 2009 and Zappa et al. 2010 for refer- water information and service needs for flood risk ences). Recent verification studies of hydrologic management, water supply management, streamflow ensemble forecasts or hindcasts (i.e., forecasts that are regulations, recreation planning, ecosystem manage- retroactively generated using a fixed forecasting sys- ment, and others (Raff et al. 2013). Such a wide range tem) over long time periods include Bartholmes et al. of applications requires forcing inputs and hydrologic (2009), Jaun and Ahrens (2009), Renner et al. (2009), forecasts at multiple space–time scales and for mul- Hopson and Webster (2010), Demargne et al. (2010), tiple forecast horizons: from minutes for flash flood Thirel et al. (2010), Van den Bergh and Roulin (2010), predictions in fast-responding basins to years for water Addor et al. (2011), and Zappa et al. (2012) for short- to supply forecasts over larger areas (see examples in medium-range hydrologic forecasts and Kang et al. McEnery et al. 2005). To account for the forcing input (2010), Wood et al. (2011), Fundel et al. (2012), Singla uncertainty, the NWS River Forecast Centers (RFCs) et al. (2012), and Yuan et al. (2013) for monthly to have been using the Ensemble Streamflow Prediction seasonal hydrologic ensembles. Objective verifica- (ESP) component of the National Weather Service tion analysis of ensemble forecasts or hindcasts over River Forecast System (NWSRFS; National Weather multiple years should improve not only the science of Service 2012). ESP produces seasonal probabilistic hydrologic ensemble forecasting but also the utility forecasts of water supply based on the historical obser- vations of precipitation and temperature (the climate being considered stationary and repeating itself) and the current hydrologic conditions (Day 1985). AFFILIATIONS: DEMARGNE—Office of Hydrologic Development, National Weather Service, NOAA, Silver Spring, Maryland, HEFS enhances ESP to include short-, medium-, and and HYDRIS Hydrologie, Saint Mathieu de Tréviers, France; long-range forcing forecasts, incorporate additional WU—Office of Hydrologic Development, National Weather weather and climate information, and better quantify Service, NOAA, Silver Spring, Maryland, and LEN Technologies, the major uncertainties in hydrologic forecasting. Oak Hill, Virginia; REGONDA AND HE—Office of Hydrologic The HEFS provides ensemble forecast and verifica- Development, National Weather Service, NOAA, Silver Spring, tion products and adds a major new capability to Maryland, and Riverside Technology, Inc., Fort Collins, Colorado; the NWS’s baseline river forecasting system: the BROWN—Hydrologic Solutions Limited, Southampton, United Kingdom; LEE—Office of Climate, Water, and Weather Services, Community Hydrologic Prediction System (CHPS). National Weather Service, NOAA, Silver Spring, Maryland, and The next section presents an overview of the HEFS LEN Technologies, Oak Hill, Virginia; SEO—Department of Civil and its various components. In the subsequent four Engineering, The University of Texas at Arlington, Arlington, sections, the individual components are described Texas; HARTMAN—California–Nevada River Forecast Center, in more detail and selected illustrative verification National Weather Service, NOAA, Sacramento, California; results are presented to demonstrate HEFS potential HERR AND FRESCH—Office of Hydrologic Development, National benefits. Finally, future scientific and operational Weather Service, NOAA, Silver Spring, Maryland; SCHAAKE— challenges for improving hydrologic ensemble fore- Consultant, Annapolis, Maryland; ZHU—Environmental Modeling Center, National Centers for Environmental Prediction, National casting services are discussed. Weather Service, NOAA, College Park, Maryland CORRESPONDING AUTHOR: Dr. Julie Demargne, 5 avenue du OVERVIEW OF THE HEFS. Uncertainty in Grand Chêne, 34270 Saint Mathieu de Tréviers, France hydrologic predictions comes from many differ- E-mail: [email protected] ent sources: atmospheric forcing observations and The abstract for this article can be found in this issue, following the predictions; initial conditions of the hydrologic table of contents. model, its parameters, and structure; and streamflow DOI:10.1175/BAMS-D-12-00081.1 regulations among other anthropogenic uncertain- In final form 6 April 2013 ties (Gupta et al. 2005). The uncertainties in the atmospheric forcing inputs are typically referred to 80 | JANUARY 2014 Unauthenticated | Downloaded 10/10/21 01:09 PM UTC as input uncertainty and those in all other sources as hydrologic uncertainty (Krzysztofowicz 1999). A hydrologic ensemble pre- diction system could either model the total uncertainty in the hydrologic output forecasts (e.g., Montanari and Grossi 2008; Coccia and Todini 2011; Weerts et al. 2011; Smith et al. 2012; Regonda et al. 2013) or explicitly account for the major sources of uncer- tainty, which is the primary FIG. 1. Schematic view of HEFS in the NWS’s CHPS, where dark gray boxes approach of HEFS (Seo correspond to ensemble-specific components and light gray boxes are also et al. 2006). As noted by used in operational single-valued forecasting. The current manual data assimilation procedures are not included in HEFS. The Hydrologic Processor Velázquez et al. (2011), hy- includes a suite of hydrologic, hydraulic, and reservoir models—for example, drologic ensemble predic-