Ocean Dynamics

, Volume 65, Issue 4, pp 469–486 | Cite as

Development of an ensemble prediction system for ocean surface waves in a coastal area

  • Arno BehrensEmail author


An ensemble prediction system for ocean surface waves has been developed and applied on a local scale to the German Bight and the western Baltic Sea. U10-wind fields generated by the COSMO-DE-EPS upstream forecast chain of the German Met Service (DWD: Deutscher Wetterdienst) have been used as the driving force for the third-generation spectral wave model WAM. The atmospheric chain includes four different global models that provide boundary values for four regional COSMO-EU realisations. Each of those drive five COSMO-DE members, respectively, with different sets of physical parameterisations, so that finally 20 members are available to run 20 corresponding wave ensemble members of the coastal wave model CWAM (Coastal WAve Model) for the German Bight and the western Baltic Sea. It is the first time that in an ensemble prediction system for ocean waves, an atmospheric model of such a fine spatial resolution of 2.8 km has been combined with a wave model running on a model grid with a mesh size of 900 m only. Test runs with the wave ensemble prediction system have been executed for two entire months (April 2013 and June 2014) and for an 8-day storm case (Xaver) in December 2013 in order to check whether such a system could be a reasonable step to improve the future operational wave forecasts of the DWD. The results computed by the different wave model members agree fairly well with available buoy data. The differences between the results for the integrated wave parameters of the individual members are small only, but more pronounced in extreme storm situations. Finally, the statistical analysis of the comparisons with measurements show without exception slightly improved values for the ensemble mean of the wave ensemble members compared with the usual deterministic routine control run.


Ensemble prediction Ocean surface waves COSMO-DE Spectral wave model WAM Wave forecasting German Bight 



The computed wind fields of all the atmospheric models that are used to drive the wave models of the ensemble prediction system and the wind measurements as well are kindly made available by the German Met Service (DWD: Deutscher Wetterdienst). I thank Thomas Bruns, Thomas Hanisch and Susanne Theis from the DWD for support and valuable discussions. I would also like to thank the two anonymous reviewers. Their constructive comments helped to improve the manuscript.


  1. Alves J-EGM, Wittman P, Sestak M, Schauer J, Stripling S, Bernier NB, McLean J, Chao Y, Chawla A, Tolman H, Nelson G, Klotz S (2013) The NCEP-FNMOC combined wave ensemble product: expanding benefits of interagency probabilistic forecasts to the ocean environment. Bull Am Meteorol Soc 94:1893–1905. doi: 10.1175/BAMS-D-12-00032.1 CrossRefGoogle Scholar
  2. Baldauf M, Seifert A, Förstner J, Majewski D, Raschendörfer M, Reinhardt T (2011) Operational convective-scale numerical weather prediction with the COSMO model. Mon Weather Rev 139:3887–3905CrossRefGoogle Scholar
  3. Bertotti L, Bidlot J-R, Buizza R, Cavaleri L, Janousek M (2011) Deterministic and ensemble-based predictions of Adriatic Sea sirocco storms leading to ‘acqua alta’ in Venice. Q J R Meteorol Soc 137:1446–1466. doi: 10.1002/qj:861 CrossRefGoogle Scholar
  4. Bidlot J-R, Janssen PAEM, Abdalla S (2007) A revised formulation for ocean wave dissipation and its model impact. ECMWF, Technical Memorandum Nr. 509Google Scholar
  5. Breivik O, Aarnes OJ, Bidlot JR, Carrasco A, Saetra O (2013) Wave extremes in the northeast Atlantic from ensemble forecasts. J Clim 26:7525–7540. doi: 10.1175/JCLI-D-12-00738.1 CrossRefGoogle Scholar
  6. Cao D, Chen HS, Tolman H (2007) Verification of ocean wave ensemble forecast at NCEP. Technical Note Nr. 261Google Scholar
  7. Cao D, Tolman H, Chen, HS, Chawla A, Wittmann P (2009) Performance of the Ocean wave ensemble forecast at NCEP. Technical Note Nr. 279Google Scholar
  8. Carrasco A, Saetra O (2008) A limited-area wave ensemble prediction system for the Nordic Seas and the North Sea. Norwegian Meteorological Institute, Report 22/2008, 29 pagesGoogle Scholar
  9. Carrasco A, Saetra O, Bidlot J-R (2011) wave ensemble predictions for safe offshore operations. Proc. 12th International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal Hazard Symposium. Kohala Coast Hawaii, October 30 – November 4. Available at
  10. Chen HS (2006) Ensemble prediction of ocean waves at NCEP. Proc 28th Ocean Engineering Conf. NSYSU, Taipei, pp 25–37Google Scholar
  11. Durrant TH, Woodcock F, Greenslade DJM (2009) Consensus forecasts of modelled wave parameters. Weather Forecast 24:492–503CrossRefGoogle Scholar
  12. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall/CRC, Boca RatonCrossRefGoogle Scholar
  13. Farina L (2002) On ensemble prediction of ocean waves. Tellus 54A:148–158CrossRefGoogle Scholar
  14. Gebhardt C, Theis SE, Paulat M, Ben Bouallègue Z (2011) Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos Res 100:168–177CrossRefGoogle Scholar
  15. Grabemann I, Weisse R (2008) Climate change impact on extreme wave conditions in the North Sea: an ensemble study. Ocean Dyn 58:199–212CrossRefGoogle Scholar
  16. Günther H, Hasselmann S, Janssen PAEM (1992) The WAM model cycle 4.0. User manual. Technical Report No. 4, Deutsches Klimarechenzentrum, Hamburg, Germany, 102 pagesGoogle Scholar
  17. Hersbach H, Janssen PAEM (1999) Improvements of the short fetch behaviour in the WAM model. J Atmos Ocean Technol 16:884–892CrossRefGoogle Scholar
  18. Hoffschildt M, Bidlot J-R, Hansen B, Janssen (1999) Potential benefits of ensemble forecasting for ship routing. ECMWF, Technical Memorandum 287Google Scholar
  19. Janssen PAEM (1999) Wave modelling and altimeter wave height data. ECMWF, Technical Memorandum Nr. 269Google Scholar
  20. Janssen PAEM (2008) Progress in ocean wave forecasting. J Comput Phys 227:3572–3594CrossRefGoogle Scholar
  21. Komen GJ, Cavaleri L, Donelan M, Hasselmann K, Hasselmann S, Janssen PAEM (1994) Dynamics and modelling of ocean waves. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  22. Lenartz F, Beckers J-M, Chiggiato J, Mourre B, Troupin C, Vandenbuicke L, Rixen M (2010) Super-ensemble techniques applied to wave forecast: performance and limitations. Ocean Sci 6:595–604CrossRefGoogle Scholar
  23. Peralta C, Ben Bouallègue Z, Theis SE, Gebhardt C, Buchhold M (2012) Accounting for initial condition uncertainties in COSMO-DE-EPS. J Geophys Res 117 (D7). doi: 10.1029/2011JD016581
  24. Saetra O, Bidlot J-R (2004) On the potential benefit of using probabilistic forecast for waves and marine winds based on the ECMWF ensemble prediction system. Weather Forecast 19:673–689CrossRefGoogle Scholar
  25. Saetra O, Hersbach H, Bidlot J-R, Richardson D (2004) Effects of observation errors on the statistics for ensemble spread and reliability. Mon Weather Rev 132:1487–1501CrossRefGoogle Scholar
  26. Schättler U, Doms G, Schraff C (2009) A description of the nonhydrostatic COSMO-Model. Part VII: user’s guide, report: 147 pp., Consort for Small-Scale Modell., Dtsch Wetterdienst, Offenbach, Germany. Available at
  27. WAMDI group, Hasselmann S, Hasselmann K, Bauer E, Janssen PAEM, Komen GJ, Bertotti L, Guillaume A, Cardone VC, Greenwood JA, Reistad M, Zambreski L, Ewing J (1988) The WAM model—a third generation ocean wave prediction model. J Phys Oceanogr 18:1775–1810CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Helmholtz-Zentrum Geesthacht, Institute of Coastal ResearchGeesthachtGermany

Personalised recommendations