• Battjes, J. A., , and J. P. F. M. Janssen, 1978: Energy loss and set-up due to breaking of random waves. Proc. 16th Conf. on Coastal Engineering, Hamburg, Germany, ASCE, 569587.

    • Search Google Scholar
    • Export Citation
  • Berg, R., 2012: Tropical cyclone report—Hurricane Irwin 6–16 October 2011. NOAA, 11 pp. [Available online at www.nhc.noaa.gov/data/tcr/EP112011_Irwin.pdf.]

    • Search Google Scholar
    • Export Citation
  • Brennan, M., 2012: Tropical cyclone report—Hurricane Jova 6–12 October 2011. NOAA, 17 pp. [Available online at www.nhc.noaa.gov/data/tcr/EP102011_Jova.pdf.]

    • Search Google Scholar
    • Export Citation
  • Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137, 16551665.

  • Cao, D., , H. S. Chen, , and H. L. Tolman, 2007: Verification of ocean wave ensemble forecasts at NCEP. Proc. 10th Int. Workshop on Wave Hindcasting and Forecasting and First Coastal Hazards Symp., Oahu, Hawaii, Environment Canada, G1.

    • Search Google Scholar
    • Export Citation
  • Carrasco, A., , and Ø. Saetra, 2008: A limited-area wave ensemble prediction system for the Nordic Seas and the North Sea. Norwegian Meteorological Institute, Rep. 22/2008, 29 pp.

    • Search Google Scholar
    • Export Citation
  • Carrasco, A., , Ø. Saetra, , and J.-R. Bidlot, 2011: Wave ensemble predictions for safe offshore operations. Proc. 12th Int. Workshop on Wave Hindcasting and Forecasting and Third Coastal Hazard Symp., Kohala Coast, Hawaii, Environment Canada, K3.

    • Search Google Scholar
    • Export Citation
  • Charron, M., , G. Pellerin, , L. Spacek, , P. L. Hourtekamer, , N. Gagnon, , H. L. Mitchell, , and L. Michelin, 2010: Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev., 138, 18771901.

    • Search Google Scholar
    • Export Citation
  • Chawla, A., , and H. L. Tolman, 2008: Obstruction grids for spectral wave models. Ocean Modell., 22, 1225.

  • Chelton, D. B., , M. H. Freilich, , and S. K. Esbensen, 2000: Satellite observations of the wind jets off the Pacific coast of Central America. Part I: Case studies and statistical characteristics. Mon. Wea. Rev., 128, 19932018.

    • Search Google Scholar
    • Export Citation
  • Chen, H. S., 2006: Ensemble prediction of ocean waves at NCEP. Proc. 28th Ocean Engineering Conf., Taipei, Taiwan, NSYSU, 2537.

  • Cui, B., , Z. Toth, , Y. Zhu, , D. Hou, , D. Unger, , and S. Beauregard, 2006: The trade-off in bias correction between using the latest analysis/modeling system with a short, versus an older system with a long archive. Proc. First THORPEX International Science Symp., Montreal, QC, Canada, WMO, 281284.

    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., , R. Hagedorn, , and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting. II: Calibration and combination. Tellus, 57A, 234252.

    • Search Google Scholar
    • Export Citation
  • Durrant, T. H., , F. Woodcock, , and D. J. M. Greenslade, 2009: Consensus forecasts of modeled wave parameters. Wea. Forecasting, 24, 492503.

    • Search Google Scholar
    • Export Citation
  • Farina, L., , A. M. Mendonca, , and J. P. Bonatti, 2005: Approximation of ensemble members in ocean wave prediction. Tellus, 57A, 204216.

    • Search Google Scholar
    • Export Citation
  • Grabemann, I., , and R. Weisse, 2008: Climate change impact on extreme wave conditions in the North Sea: An ensemble study. Ocean Dyn., 58, 199212.

    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., , F. J. Doblas-Reyes, , and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting. I: Basic concept. Tellus, 57A, 219233.

    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., , R. Buizza, , T. M. Hamill, , M. Leutbecher, , and T. N. Palmer, 2012: Comparing TIGGE multimodel forecasts with reforecast-calibrated ECMWF ensemble forecasts. Quart. J. Roy. Meteor. Soc., 138, 18141827.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., and Coauthors, 1973: Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Dtsch. Hydrogr. Z., 12, 195.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, S., , and K. Hasselmann, 1985: Computations and parameterizations of the nonlinear energy transfer in a gravity-wave spectrum, Part I: A new method for efficient computations of the exact nonlinear transfer integral. J. Phys. Oceanogr., 15, 13691377.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559570.

    • Search Google Scholar
    • Export Citation
  • Hoffschildt, M., , J. Bidlot, , B. Hansen, , and P. A. E. Janssen, 1999: Potential benefits of ensemble forecasting for ship routing. ECMWF Tech. Memo. 287, 25 pp.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., , H. L. Mitchell, , and D. Deng, 2009: Model error representation in an operational ensemble Kalman filter. Mon. Wea. Rev., 137, 21262143.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., , C. M. Kishtawal, , Z. Zhang, , T. LaRow, , D. Bachiochi, , E. Williford, , S. Gadgil, , and S. Surendran, 2000: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13, 41964216.

    • Search Google Scholar
    • Export Citation
  • Leonard, B. P., 1991: The ULTIMATE conservative difference scheme applied to unsteady one-dimensional advection. Comput. Methods Appl. Mech. Eng., 88, 1774.

    • Search Google Scholar
    • Export Citation
  • McLay, J., , C. H. Bishop, , and C. A. Reynolds, 2010: A local formulation of the ensemble transform (ET) analysis perturbation scheme. Wea. Forecasting, 25, 985993.

    • Search Google Scholar
    • Export Citation
  • Mullen, S. L., , and R. Buizza, 2002: The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF ensemble prediction system. Wea. Forecasting, 17, 173191.

    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., , J. Bartholmes, , and J. Thielen, , and E. Anghel, 2008: TIGGE: Medium-range multi model weather forecast ensembles in flood forecasting (a case study). ECMWF Tech. Memo. 557, 28 pp.

    • Search Google Scholar
    • Export Citation
  • Park, Y.-Y., , R. Buizza, , and M. Leutbecher, 2008: TIGGE: Preliminary results on comparing and combining ensembles. Quart. J. Roy. Meteor. Soc., 134, 20292050.

    • Search Google Scholar
    • Export Citation
  • Pinson, P., , G. Reikard, , and J.-R. Bidlot, 2012: Probabilistic forecasting of the wave energy flux. Appl. Energy, 93, 364370.

  • Richardson, D. S., 2000: Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649667.

    • Search Google Scholar
    • Export Citation
  • Richardson, D. S., 2001: Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size. Quart. J. Roy. Meteor. Soc., 127, 24732489.

    • Search Google Scholar
    • Export Citation
  • Risien, C. M., , and D. B. Chelton, 2006: A satellite-derived climatology of global ocean winds. Remote Sens. Environ., 105, 221236.

  • Saetra, Ø., , and J.-R. Bidlot, 2004: Potential benefits of using probabilistic forecasts for waves and marine winds based on the ECMWF Ensemble Prediction System. Wea. Forecasting, 19, 673689.

    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., 2008: User manual and system documentation of WAVEWATCH III version 3.14. NOAA/NWS/NCEP MMAB Tech. Note 268, 192 pp.

  • Tolman, H. L., , and D. V. Chalikov, 1996: Source terms in a third-generation wind-wave model. J. Phys. Oceanogr., 26, 24972518.

  • Tolman, H. L., , M. L. Banner, , and J. M. Kaihatu, 2011: The NOPP operational wave model improvement project. Proc. 12th Int. Workshop on Wave Hindcasting and Forecasting and Third Coastal Hazards Symp., Kohala Coast, Hawaii, Environment Canada, I12.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., , and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 23172330.

  • Wei, M., , Z. Toth, , R. Wobus, , and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP Global Operational Forecast System. Tellus, 59A, 6279.

    • Search Google Scholar
    • Export Citation
  • Weigel, A. P., , M. A. Liniger, , and C. Appenzeller, 2008: Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Quart. J. Roy. Meteor. Soc., 134, 241260.

    • Search Google Scholar
    • Export Citation
  • Xu, L., , T. Rosmond, , and R. Daley, 2005: Development of NAVDAS-AR: Formulation and initial tests of the linear problem. Tellus, 57A, 546599.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y., 2005: Ensemble forecast: A new approach to uncertainty and predictability. Adv. Atmos. Sci., 22, 781788.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 58 58 24
PDF Downloads 34 34 12

The NCEP–FNMOC Combined Wave Ensemble Product: Expanding Benefits of Interagency Probabilistic Forecasts to the Oceanic Environment

View More View Less
  • 1 Systems Research Group Inc., Colorado Springs, Colorado, and Environmental Modeling Center, NOAA/NCEP, College Park, Maryland
  • 2 Fleet Numerical Meteorology and Oceanography Center, U.S. Navy, Monterey, California
  • 3 National Hurricane Center, NOAA/NCEP, Miami, Florida
  • 4 Environment Canada, Montreal, Quebec, Canada
  • 5 Environmental Modeling Center, NOAA/NCEP, College Park, Maryland
  • 6 Forward Slope, Inc., San Diego, California
  • 7 Dynamics Research Corporation, Reston, Virginia
© Get Permissions
Restricted access

The U.S. National Centers for Environmental Prediction (NCEP) and the Fleet Numerical Meteorology and Oceanography Center (FNMOC) have joined forces to establish a first global multicenter ensemble system dedicated to probabilistic forecasts of windwave heights. Both centers run independent wave ensemble systems (WES), which are combined onto a multicenter system with 41 members. A performance assessment of the multicenter wave-height product is made relative to altimeter data. Computed estimates of mean errors, ability to represent uncertainty, and reliability of probabilistic forecasts indicate that the multicenter ensemble product outperforms individual WES and deterministic wave models alike. The investigation includes an evaluation made at NCEP's National Hurricane Center (NHC) of the multicenter WES product, including severe sea-state events. The interagency collaboration has provided an opportunity to investigate in more depth the properties of wave ensembles, which has led to planned improvements that are expected to increase the accuracy of probabilistic forecasts within the oceanic environment. These outcomes are expected to be of great benefit to the society, the economy, and the environment. The successful operational implementation of the multicenter product has brought new opportunities for further collaboration with operational centers in North America, and a planned upgrade to the current interagency system is the inclusion of 20 additional members from a WES under development at Environment Canada.

Marine Modeling and Analysis Branch contribution 312

CORRESPONDING AUTHOR: Jose-Henrique Alves, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740. E-mail: henrique.alves@noaa.gov

The U.S. National Centers for Environmental Prediction (NCEP) and the Fleet Numerical Meteorology and Oceanography Center (FNMOC) have joined forces to establish a first global multicenter ensemble system dedicated to probabilistic forecasts of windwave heights. Both centers run independent wave ensemble systems (WES), which are combined onto a multicenter system with 41 members. A performance assessment of the multicenter wave-height product is made relative to altimeter data. Computed estimates of mean errors, ability to represent uncertainty, and reliability of probabilistic forecasts indicate that the multicenter ensemble product outperforms individual WES and deterministic wave models alike. The investigation includes an evaluation made at NCEP's National Hurricane Center (NHC) of the multicenter WES product, including severe sea-state events. The interagency collaboration has provided an opportunity to investigate in more depth the properties of wave ensembles, which has led to planned improvements that are expected to increase the accuracy of probabilistic forecasts within the oceanic environment. These outcomes are expected to be of great benefit to the society, the economy, and the environment. The successful operational implementation of the multicenter product has brought new opportunities for further collaboration with operational centers in North America, and a planned upgrade to the current interagency system is the inclusion of 20 additional members from a WES under development at Environment Canada.

Marine Modeling and Analysis Branch contribution 312

CORRESPONDING AUTHOR: Jose-Henrique Alves, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740. E-mail: henrique.alves@noaa.gov
Save