The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

被引:200
|
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.
引用
收藏
页码:79 / 98
页数:20
相关论文
共 50 条
  • [21] Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System
    Chu, Philip Y.
    Kelley, John G. W.
    Mott, Gregory V.
    Zhang, Aijun
    Lang, Gregory A.
    OCEAN DYNAMICS, 2011, 61 (09) : 1305 - 1316
  • [22] Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System
    Philip Y. Chu
    John G. W. Kelley
    Gregory V. Mott
    Aijun Zhang
    Gregory A. Lang
    Ocean Dynamics, 2011, 61 : 1305 - 1316
  • [23] Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA
    Alves, Jose-henrique
    Padilla-hernandez, Roberto
    Spindler, Deanna
    Kolczynski, Walter
    Rajan, Bhavani
    Spindler, Todd
    Abdolali, Ali
    Campos, Ricardo
    Banihashemi, Saeideh
    Meixner, Jessica
    WEATHER AND FORECASTING, 2024, 39 (12) : 1761 - 1776
  • [24] Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas
    Kim, Sunghee
    Sadeghi, Hossein
    Limon, Reza Ahmad
    Saharia, Manabendra
    Seo, Dong-Jun
    Philpott, Andrew
    Bell, Frank
    Brown, James
    He, Minxue
    JOURNAL OF HYDROMETEOROLOGY, 2018, 19 (09) : 1467 - 1483
  • [25] Update on NOAA's Operational Air Quality Predictions
    Stajner, Ivanka
    Lee, Pius
    McQueen, Jeffery
    Draxler, Roland
    Dickerson, Phil
    Upadhayay, Sikchya
    AIR POLLUTION MODELING AND ITS APPLICATION XXIV, 2016, : 593 - 597
  • [26] An Ensemble Learning Based Approach For Building Airfare Forecast Service
    Chen, Yuwen
    Cao, Jian
    Feng, Shanshan
    Tan, Yudong
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, 2015, : 964 - 969
  • [27] Wave data assimilation using ensemble error covariances for operational wave forecast
    Sannasiraj, S. A.
    Babovic, Vladan
    Chan, Eng Soon
    OCEAN MODELLING, 2006, 14 (1-2) : 102 - 121
  • [28] Research on and application of the operational storm surge ensemble forecast model in the Bay of Bengal
    Liu, Qiuxing
    Li, Mingjie
    Liang, Sendong
    Liu, Shichao
    Fu, Xiang
    APPLIED OCEAN RESEARCH, 2023, 130
  • [29] An operational mesoscale ensemble-based forecast system using HPC resources
    Bowers, James
    Astling, Elford
    Liu, Yubao
    Hacker, Joshua
    Swerdlin, Scott
    Betancourt, Terri
    Warner, Thomas
    PROCEEDINGS OF THE HPCMP USERS GROUP CONFERENCE 2007, 2007, : 255 - +
  • [30] USING NOAA's NEW CLIMATE OUTLOOKS IN OPERATIONAL HYDROLOGY
    Croley, Thomas E., II
    JOURNAL OF HYDROLOGIC ENGINEERING, 1996, 1 (03) : 93 - 102