Ha, T.; Heo, K.-Y.; Jeon, J.S., and Kang, S., 2017. Numerical modelling of large swell waves using different atmospheric reanalysis data in East Sea. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 164–168. Coconut Creek (Florida), ISSN 0749-0208.
Recently, large swell waves have been attracted by many engineers and scientists in South Korea since the eastern coast of the Korean Peninsula has been frequently damaged by large swell waves for several years during winter season. These waves were occasionally higher than 3 m and could be very dangerous for people in beach areas. It has been identified that these waves originated from a certain cyclone passing over East Sea in recent researches. However, an apparent developing mechanism of large swell waves in East Sea is still remained unidentified. Numerical modelling of water waves using the third generation wave model is a convenient source for analysis of wave behaviors in the ocean. In this study, two well-known global atmospheric reanalysis data (ERA-Interim and NCEP FNL) were employed to simulate large swell waves in East Sea. Since sea surface wind is generally regarded as the most important source to wave models, different global reanalysis data should be carefully applied to wave models for better performance. Numerical results of wave models using two different reanalysis data were compared with available observational data and their performance reproducing large swell waves was evaluated both qualitatively and quantitatively. Both simulated wave products using ERA-Interim and NCEP FNL data were reasonably agreeable with corresponding observational data while wave products employing ERA-Interim data qualitatively represented development of observed wave profiles slightly better at certain locations than those employing NCEP FNL data. On the other hand, wave products using NCEP FNL data quantitatively represented observational wave heights slightly better than those using ERA-Interim data during the extreme events.