Evaluations of Global Wave Prediction at the Fleet Numerical Meteorology and Oceanography Center

W. Erick Rogers Oceanography Division, Naval Research Laboratory, Stennis Space Center, Mississippi

Search for other papers by W. Erick Rogers in
Current site
Google Scholar
PubMed
Close
,
Paul A. Wittmann Fleet Numerical Meteorology and Oceanography Center, Monterey, California

Search for other papers by Paul A. Wittmann in
Current site
Google Scholar
PubMed
Close
,
David W. C. Wang Oceanography Division, Naval Research Laboratory, Stennis Space Center, Mississippi

Search for other papers by David W. C. Wang in
Current site
Google Scholar
PubMed
Close
,
R. Michael Clancy Fleet Numerical Meteorology and Oceanography Center, Monterey, California

Search for other papers by R. Michael Clancy in
Current site
Google Scholar
PubMed
Close
, and
Y. Larry Hsu Oceanography Division, Naval Research Laboratory, Stennis Space Center, Mississippi

Search for other papers by Y. Larry Hsu in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

It is a major challenge to determine whether bias in operational global wave predictions is predominately due to the wave model itself (internal error) or due to errors in wind forcing (an external error). Another challenge is to characterize bias attributable to errors in wave model physics (e.g., input, dissipation, and nonlinear transfer). In this study, hindcasts and an evaluation methodology are constructed to address these challenges. The bias of the wave predictions is evaluated with consideration of the bias of four different wind forcing fields [two of which are supplemented with the NASA Quick Scatterometer (QuikSCAT) measurements]. It is found that the accuracy of the Fleet Numerical Meteorology and Oceanography Center’s operational global wind forcing has improved to the point where it is unlikely to be the primary source of error in the center’s global wave model (WAVEWATCH-III). The hindcast comparisons are specifically designed to minimize systematic errors from numerics and resolution. From these hindcasts, insight into the physics-related bias in the global wave model is possible: comparison to in situ wave data suggests an overall positive bias at northeast Pacific locations and an overall negative bias at northwest Atlantic locations. Comparison of frequency bands indicates a tendency by the model physics to overpredict energy at higher frequencies and underpredict energy at lower frequencies.

Corresponding author address: W. Erick Rogers, NRL Code 7322, Bldg. 1009, Stennis Space Center, MS 39529. Email: rogers@nrlssc.navy.mil

Abstract

It is a major challenge to determine whether bias in operational global wave predictions is predominately due to the wave model itself (internal error) or due to errors in wind forcing (an external error). Another challenge is to characterize bias attributable to errors in wave model physics (e.g., input, dissipation, and nonlinear transfer). In this study, hindcasts and an evaluation methodology are constructed to address these challenges. The bias of the wave predictions is evaluated with consideration of the bias of four different wind forcing fields [two of which are supplemented with the NASA Quick Scatterometer (QuikSCAT) measurements]. It is found that the accuracy of the Fleet Numerical Meteorology and Oceanography Center’s operational global wind forcing has improved to the point where it is unlikely to be the primary source of error in the center’s global wave model (WAVEWATCH-III). The hindcast comparisons are specifically designed to minimize systematic errors from numerics and resolution. From these hindcasts, insight into the physics-related bias in the global wave model is possible: comparison to in situ wave data suggests an overall positive bias at northeast Pacific locations and an overall negative bias at northwest Atlantic locations. Comparison of frequency bands indicates a tendency by the model physics to overpredict energy at higher frequencies and underpredict energy at lower frequencies.

Corresponding author address: W. Erick Rogers, NRL Code 7322, Bldg. 1009, Stennis Space Center, MS 39529. Email: rogers@nrlssc.navy.mil

Save
  • Abdalla, S., 2001: Impact of wind gustiness and air density on modelling of wave generation: Implementation at ECMWF. Proc. ECMWF Workshop on Ocean Wave Forecasting, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 147–153.

  • Abdalla, S., and Cavaleri L. , 2002: Effect of wind variability and variable air density on wave modeling. J. Geophys. Res., 107 .3080, doi:10.1029/2000JC000639.

    • Search Google Scholar
    • Export Citation
  • Abdalla, S., Janssen P. , and Bidlot J-R. , 2003: Use of satellite data and enhanced physics to improve wave prediction. Proc. 28th Int. Conf. on Coastal Engineering, Cardiff, Wales, United Kingdom, American Society of Civil Engineers, 87–96.

    • Crossref
    • Export Citation
  • Bender, L. C., 1996: Modification of the physics and numerics in a third-generation ocean wave model. J. Atmos. Oceanic Technol., 13 , 726750.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bidlot, J-R., Holmes D. J. , Wittmann P. A. , Lalbeharry R. , and Chen H. S. , 2002: Intercomparison of the performance of operational ocean wave forecasting systems with buoy data. Wea. Forecasting, 17 , 287310.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Booij, N., and Holthuijsen L. H. , 1987: Propagation of ocean waves in discrete spectral wave models. J. Comput. Phys., 68 , 307326.

  • Cardone, V. J., Graber H. C. , Jensen R. E. , Hasselmann S. , and Caruso M. J. , 1995: In search of the true surface wind field in SWADE IOP-1: Ocean wave modeling perspective. Global Atmos. Ocean Syst., 3 , 107150.

    • Search Google Scholar
    • Export Citation
  • Cardone, V. J., Jensen R. E. , Resio D. T. , Swail V. R. , and Cox A. T. , 1996: Evaluation of contemporary ocean wave models in rare extreme events: The “Halloween storm” of October 1991 and the “storm of the century” of March 1993. J. Atmos. Oceanic Technol., 13 , 198230.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chin, T. M., Milliff R. F. , and Large W. G. , 1998: Basin scale, high-wavenumber sea surface fields from a multiresolution analysis of scatterometer data. J. Atmos. Oceanic Technol., 15 , 741763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clancy, R. M., Kaitala J. E. , and Zambresky L. F. , 1986: The Fleet Numerical Oceanography Center Global Spectral Ocean Wave Model. Bull. Amer. Meteor. Soc., 67 , 498512.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebuchi, N., Graber H. C. , and Caruso M. J. , 2002: Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data. J. Atmos. Oceanic Technol., 19 , 20492062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., and Zivkovic-Rothman M. , 1999: Development and evaluation of a convection scheme for use in climate models. J. Atmos. Sci., 56 , 17661782.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, L-L., Christensen E. J. , and Yamarone C. A. , 1994: TOPEX/POSEIDON mission overview. J. Geophys. Res., 99 , 2436924381.

  • Günther, H., Hasselmann S. , and Janssen P. A. E. M. , 1992: The WAM model Cycle 4 (revised version). Dtsch. Klim. Rechenzentrum Tech. Rep. 4, Hamburg, Germany, 101 pp. [Available from Institute for Coastal Research, GKSS Research Center, Max Planck Str. 1, D-21502 Geesthacht, Germany.].

  • Hogan, T. F., and Rosmond T. E. , 1991: The description of the U.S. Navy Operational Global Atmospheric Prediction System’s spectral forecast models. Mon. Wea. Rev., 119 , 17861815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janssen, P. A. E. M., 1998: On the error growth in wave models. ECMWF Research Memo. 249, ECMWF, Reading, United Kingdom, 12 pp.

  • Janssen, P. A. E. M., Hansen B. , and Bidlot J-R. , 1997: Verification of the ECMWF wave forecasting system against buoy and altimeter data. Wea. Forecasting, 12 , 763784.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, R. E., Wittmann P. A. , and Dykes J. D. , 2002: Global and regional wave modeling activities. Oceanography, 15 , 5766.

  • Khandekar, M. L., and Lalbeharry R. , 1996: An evaluation of Environment Canada’s operational ocean wave model based on moored buoy data. Wea. Forecasting, 11 , 137152.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Komen, G. J., Hasselmann S. , and Hasselmann K. , 1984: On the existence of a fully developed wind-sea spectrum. J. Phys. Oceanogr., 14 , 12711285.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Komen, G. J., Cavaleri L. , Donelan M. , Hasselmann K. , Hasselmann S. , and Janssen P. A. E. M. , 1994: Dynamics and Modelling of Ocean Waves. Cambridge University Press, 532 pp.

    • Search Google Scholar
    • Export Citation
  • Moskowitz, L., 1964: Estimates of the power spectrums for fully developed seas for wind speeds of 20 to 40 knots. J. Geophys. Res., 69 , 51615179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pierson, W. J., and Moskowitz L. , 1964: A proposed spectral form for fully developed wind seas based on the similarity theory of S. A. Kitaigorodskii. J. Geophys. Res., 69 , 51815190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • PODAAC, 2001: QuikSCAT science data product user’s manual. Jet Propulsion Laboratory Tech. Doc., Physical Oceanography Distributed Active Archive Center, 86 pp.

  • Rogers, W. E., 2002: The U.S. Navy’s global wind-wave models: An investigation into sources of error in low frequency energy predictions. NRL Formal Rep. 7320-02-10035, 63 pp. [Available online at http://torpedo.nrl.Navy.mil/tu/ps.].

    • Crossref
    • Export Citation
  • Rogers, W. E., and Wittmann P. A. , 2002: Quantifying the role of wind field accuracy in the U.S. Navy’s global ocean wave nowcast/forecast system. NRL Memo. Rep. 7320-02-8290, 26 pp. [Available online at http://torpedo.nrl.Navy.mil/tu/ps.].

  • Rogers, W. E., Kaihatu J. M. , Petit H. A. H. , Booij N. , and Holthuijsen L. H. , 2002: Diffusion reduction in an arbitrary scale third generation wind wave model. Ocean Eng., 29 , 13571390.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, W. E., Wittmann P. A. , Wang D. W. , Clancy M. , and Hsu L. , 2004: Evaluations of global wind prediction at Fleet Numerical Meteorology and Oceanography Center (from the perspective of a wave modeler). NRL Memo. Rep. 7320-04-8823, 15 pp. [Available online at http://torpedo.nrl.navy.mil/tu/ps.].

    • Crossref
    • Export Citation
  • Rosmond, T. E., Teixeira J. , Peng M. , Hogan T. F. , and Pauley R. , 2002: Navy Operational Global Atmospheric Prediction System (NOGAPS): Forcing for ocean models. Oceanography, 15 , 99108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teixeira, J., and Hogan T. F. , 2001: A new boundary layer cloud scheme in NOGAPS. Tech. Rep. NRL/MR/7532-01-7243, Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, 38 pp.

  • Teixeira, J., and Hogan T. F. , 2002: Boundary layer clouds in a global atmospheric model: Simple cloud cover parameterizations. J. Climate, 15 , 12611276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., 1991: A third-generation model for wind waves on slowly varying, unsteady, and inhomogeneous depths and currents. J. Phys. Oceanogr., 21 , 782797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., 1999: Validation of NCEP’s ocean winds for the use in wind wave models. Global Atmos. Ocean Syst., 6 , 243268.

  • Tolman, H. L., 2002a: User manual and system documentation of WAVEWATCH-III version 2.22. NCEP Tech. Note, 133 pp. [Available online at http://polar.ncep.noaa.gov/waves/references.html.].

  • Tolman, H. L., 2002b: Alleviating the garden sprinkler effect in wind wave models. Ocean Modelling, 4 , 269289.

  • Tolman, H. L., 2002c: Validation of WAVEWATCH III version 1.15 for a global domain. NCEP Tech. Note, 33 pp. [Available online at http://polar.ncep.noaa.gov/waves/references.html.].

  • Tolman, H. L., 2002d: Testing of WAVEWATCH III version 2.22 in NCEP’s NWW3 ocean wave model suite. NCEP Tech. Note, 99 pp. [Available online at http://polar.ncep.noaa.gov/waves/references.html.].

  • Tolman, H. L., 2003: Treatment of unresolved islands and ice in wind wave models. Ocean Modelling, 5 , 219231.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., Balasubramaniyan B. , Burroughs L. D. , Chalikov D. V. , Chao Y. Y. , Chen H. S. , and Gerald V. M. , 2002: Development and implementation of wind-generated ocean surface wave models at NCEP. Wea. Forecasting, 17 , 311333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WAMDI Group, 1988: The WAM model—A third generation ocean wave prediction model. J. Phys. Oceanogr., 18 , 17751810.

  • Wittmann, P. A., 2001: Implementation of WAVEWATCH-III at Fleet Numerical Meteorological and Oceanography Center. Proc. MTS/IEEE Conf. and Exposition: An Ocean Odyssey, Honolulu, HI, Marine Technology Society and IEEE, 1474–1479.

  • Wittmann, P. A., and Clancy R. M. , 1993: Implementation and validation of a global third-generation wave model at Fleet Numerical Meteorological and Oceanography Center. Proc. Second Int. Symp. on Ocean Wave Measurement and Analysis, New Orleans, LA, Army Corps of Engineers, 406–419.

  • Wittmann, P. A., and O’Reilly W. C. , 1998: WAM validation of Pacific swell. Proc. Fifth Int. Workshop on Wave Hindcasting and Forecasting, Melbourne, FL, Atmospheric Environment Service/Environment Canada, 83–87.

  • Wittmann, P. A., Clancy R. M. , and Mettlach T. , 1995: Operational wave forecasting at Fleet Numerical Meteorology and Oceanography Center. Proc. Fourth Int. Workshop on Wave Hindcasting and Forecasting, Banff, AB, Canada, Atmospheric Environment Service/Environment Canada, 335–342.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 197 56 2
PDF Downloads 111 37 5