Articles | Volume 14, issue 6
https://doi.org/10.5194/os-14-1435-2018
https://doi.org/10.5194/os-14-1435-2018
Research article
 | 
20 Nov 2018
Research article |  | 20 Nov 2018

Better Baltic Sea wave forecasts: improving resolution or introducing ensembles?

Torben Schmith, Jacob Woge Nielsen, Till Andreas Soya Rasmussen, and Henrik Feddersen

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Cited articles

Alari, V., Staneva, J., Breivik, Ø., Bidlot, J.-R., Mogensen, K., and Janssen, P.: Surface wave effects on water temperature in the Baltic Sea: simulations with the coupled NEMO-WAM model, Ocean Dyn., 66, 917–930, 2016. 
Alves, J.-H. G., Wittmann, P., Sestak, M., Schauer, J., Stripling, S., Bernier, N. B., McLean, J., Chao, Y., Chawla, A., Tolman, H., Nelson, G., and Klotz, S.: The NCEP–FNMOC combined wave ensemble product: Expanding benefits of interagency probabilistic forecasts to the oceanic environment, B. Am. Meteorol. Soc., 94, 1893–1905, 2013. 
Amante, C. and Eakins, B. W.: ETOPO1 1 arc-minute global relief model: procedures, data, sources and analysis, National Geophysical Data Center, Marine Geology and Geophysics Division, Boulder, Colorado, 2009. 
Battjes, J. A. and Janssen, J. P. F. M.: Energy Loss and Set-Up Due to Breaking of Random Waves, Proceedings of 16th Conference on Coastal Engineering, Hamburg, Germany, 1978. 
Behrens, A.: Development of an ensemble prediction system for ocean surface waves in a coastal area, Ocean Dyn., 65, 469–486, https://doi.org/10.1007/s10236-015-0825-y, 2015. 
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Using the Baltic Sea as an example, the benefit of increased wave model resolution as opposed to ensemble forecasting is examined, on the premise that the extra computational effort tends to be of the same order of magnitude in both cases. For offshore waters, an ensemble mean is shown to outperform high-resolution modeling. However, for nearshore or shallow waters, where fine-scale depth or coastal features gain importance, this is not necessarily found to be the case.