Impact of Data Assimilation on KMA's Global and Regional Ocean Wave Predictions.

Oh, S.M.; Roh, M.; Chang, P.-H.; Kim, K.O.; Oh, Y.; Kang, H.-S., and Moon, I.-J., 2023. Impact of data assimilation on KMA's global and regional ocean wave predictions. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 86-90. Charlotte (North Carolina), ISSN 0749-0208. The most efficient and effective way to improve the ocean wave prediction is to assimilate observational data collected in real time. Recently, most institutes are trying to improve the accuracy of ocean wave predictions by assimilating various observation data. In this study, significant wave heights observed from satellites and buoys were assimilated into global and regional ocean wave models of the Korea Meteorological Administration (KMA), and their performance was verified. The KMA global and regional wave data assimilation system uses 2-dimensional optimal interpolation based on WaveWatch-Ⅲ version 6.07 with spatial resolution of 1/4° and 1/30°, respectively. Numerical experiments for boreal summer and winter from June 2020 to February 2021 reveal that the use of data assimilation reduced the Root Mean Square Error (RMSE) by 15% and 44%, respectively, for the initial field of global and regional wave models. In particular, in the case of typhoon Bavi in 2020, when data assimilation was not used, there was a tendency to overestimate the significant wave height at the three ocean research stations, but the use of data assimilation reduced the error by up to105 cm. The assimilated initial fields improved ocean wave predictions by 48 and 12 hours in KMA's global and regional ocean wave models, respectively. [ABSTRACT FROM AUTHOR]