Consensus of Numerical Model Forecasts of Significant Wave Heights

Frank Woodcock Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia

Search for other papers by Frank Woodcock in
Current site
Google Scholar
PubMed
Close
and
Diana J. M. Greenslade Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia

Search for other papers by Diana J. M. Greenslade in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

The operational consensus forecast (OCF) scheme uses past performance to bias correct and combine numerical forecasts to produce an improved forecast at locations where recent observations are available. Here, OCF uses past observations and forecasts of significant wave height from five numerical wave models available in real time at the Australian Bureau of Meteorology. In addition to OCF, different adaptive weighting and forecast combination strategies are investigated. At deep-water sites (ocean depth > 25 m), all of the interpolated raw model forecasts outperformed 24-h persistence and, after bias correction, one model was clearly best. Significant improvements over raw model significant wave height forecasts were achieved by bias correction, linear-regression methods, and combination strategies. The best forecasts were obtained from a “composite of composites” in which models with highly correlated errors were combined before being included in the performance-weighted bias-corrected forecast. This technique slightly outperformed the linear-regression-corrected best model. At shallow-water sites (ocean depth < 25 m), all raw models perform poorly relative to the 24-h persistence. The composited, corrected forecasts significantly improved on raw model significant wave height forecasts but only slightly outperformed the 24-h persistence. The raw models generated unrealistically large biases that tended to be amplified with larger observed values of significant wave height.

Corresponding author address: Frank Woodcock, Bureau of Meteorology Research Centre, P.O. Box 1289 K, Melbourne, VIC 3001, Australia. Email: f.woodcock@bom.gov.au

Abstract

The operational consensus forecast (OCF) scheme uses past performance to bias correct and combine numerical forecasts to produce an improved forecast at locations where recent observations are available. Here, OCF uses past observations and forecasts of significant wave height from five numerical wave models available in real time at the Australian Bureau of Meteorology. In addition to OCF, different adaptive weighting and forecast combination strategies are investigated. At deep-water sites (ocean depth > 25 m), all of the interpolated raw model forecasts outperformed 24-h persistence and, after bias correction, one model was clearly best. Significant improvements over raw model significant wave height forecasts were achieved by bias correction, linear-regression methods, and combination strategies. The best forecasts were obtained from a “composite of composites” in which models with highly correlated errors were combined before being included in the performance-weighted bias-corrected forecast. This technique slightly outperformed the linear-regression-corrected best model. At shallow-water sites (ocean depth < 25 m), all raw models perform poorly relative to the 24-h persistence. The composited, corrected forecasts significantly improved on raw model significant wave height forecasts but only slightly outperformed the 24-h persistence. The raw models generated unrealistically large biases that tended to be amplified with larger observed values of significant wave height.

Corresponding author address: Frank Woodcock, Bureau of Meteorology Research Centre, P.O. Box 1289 K, Melbourne, VIC 3001, Australia. Email: f.woodcock@bom.gov.au

Save
  • Bidlot, J-R., Holmes D. J. , Wittman 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., Ris R. C. , and Holthuijsen L. H. , 1999: A third-generation wave model for coastal regions. Part I. Model description and validation. J. Geophys. Res., 104 , 76497666.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • Donelan, M., and Pierson W. J. , 1983: The sampling variability of estimates of spectra of wind-generated waves. J. Geophys. Res., 88 , 43814392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efron, B., and Tibshirani R. , 1991: Statistical data analysis in the computer age. Science, 253 , 390395.

  • Glahn, H. R., and Lowry D. A. , 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11 , 12031211.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greenslade, D. J. M., 2001: The assimilation of ERS-2 significant wave height data in the Australian region. J. Mar. Syst., 28 , 141160.

  • Greenslade, D. J. M., and Young I. R. , 2004: Background errors in a global wave model determined form altimeter data. J. Geophys. Res., 109 .C09007, doi:10.1029/2004JC002324.

    • Search Google Scholar
    • Export Citation
  • Gorman, R. M., Bryan K. R. , and Liang A. K. , 2003: Wave hindcast for the New Zealand region: Nearshore validation and coastal wave climate. N. Z. J. Mar. Freshwater Res., 37 , 567588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hemer, M. A., and Bye J. A. T. , 1999: The swell climate of the south Australian sea. Trans. Royal Soc. S. Aust., 123 , 3. 107113.

  • Hibon, M., and Evgeniou T. , 2005: To combine or not to combine: Selecting among forecasts and their combinations. Int. J. Forecasting, 21 , 1524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holt, M. W., 1997: Assimilation of ERS-2 altimeter observations into a global wave model. Research Activities in Atmospheric and Oceanic Modelling—1997, WGNE Rep. 25, WMO/TD-792, 8.31–8.32.

  • 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
  • Mao, Q., McNider R. T. , Mueller S. F. , and Juang H. H. , 1999: An optimal model output calibration algorithm suitable for objective temperature forecasting. Wea. Forecasting, 14 , 190202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monaldo, F., 1988: Expected differences between buoy and radar altimeter estimates of wind speed and significant wave height and their implications on buoy–altimeter comparisons. J. Geophys. Res., 93 , 22852302.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Meteorological and Oceanographic Centre, 1999: Changes to the operational sea state forecast system. Bureau of Meteorology Operations Bull. 47, Melbourne, Australia, 6 pp.

  • Stensrud, D. J., and Skindlov J. A. , 1996: Gridpoint predictions of high temperature from a mesoscale model. Wea. Forecasting, 11 , 103110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Yussouf N. , 2003: Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev., 131 , 25102524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Yussouf N. , 2005: Bias-corrected short-range ensemble forecasts of near surface variables. Meteor. Appl., 12 , 217230.

  • 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
  • WAMDI Group, 1988: The WAM model—A third generation ocean wave prediction model. J. Phys. Oceanogr., 18 , 17751810.

  • Wonnacott, T. H., and Wonnacott R. J. , 1972: Introductory Statistics. Wiley, 510 pp.

  • Woodcock, F., and Engel C. , 2005: Operational consensus forecasts. Wea. Forecasting, 20 , 101111.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 835 583 198
PDF Downloads 174 51 3