Sea surface winds and coastal winds, which have a significant influence on the ocean environment, are very difficult to predict. Although most planetary boundary layer (PBL) parameterizations have demonstrated the capability to represent many meteorological phenomena, little attention has been paid to the precise prediction of winds at the lowest PBL level. In this study, the ability to simulate sea winds of two widely used mesoscale models, fifth-generation mesoscale model (MM5) and weather research and forecasting model (WRF), were compared. In addition, PBL sensitivity experiments were performed using Medium-Range Forecasts (MRF), Eta, Blackadar, Yonsei University (YSU), and Mellor-Yamada-Janjic (MYJ) during Typhoon Ewiniar in 2006 to investigate the optimal PBL parameterizations for predicting sea winds accurately. The horizontal distributions of winds were analyzed to discover the spatial features. The time-series analysis of wind speed from five sensitivity experimental cases was compared by correlation analysis with surface observations. For the verification of sea surface winds, QuikSCAT satellite 10-m daily mean wind data were used in root-mean-square error (RMSE) and bias error (BE) analysis. The MRF PBL using MM5 produced relatively smaller wind speeds, whereas YSU and MYJ using WRF produced relatively greater wind speeds. The hourly surface observations revealed increasingly strong winds after 0300 UTC, July 10, with most of the experiments reproducing observations reliably. YSU and MYJ using WRF showed the best agreements with observations. However, MRF using MM5 demonstrated underestimated winds. The conclusions from the correlation analysis and the RMSE and BE analysis were compatible with the above-mentioned results. However, some shortcomings were identified in the improvements of wind prediction. The data assimilation of topographical data and asynoptic observations along coast lines and satellite data in sparsely observed ocean areas should make it possible to improve the accuracy of sea surface wind predictions.

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In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system.

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In this study, a two-dimensional storm surges/tide prediction model called the Storm surges/Tide Operational Model (STORM) was developed and applied as the operational forecast model of the Korea Meteorological Administration (KMA). STORM has good horizontal resolution (8 km) and accounts for the interaction between tides and storm surges. This model has been implemented for the northwestern Pacific Ocean includingthe area around the Korean Peninsula. To examine the model performance, a hindcasting experiment was carried out for Typhoon Maemi. The results showed good agreement between the simulation and observation. The operational model results were also verified for two years (June 2005-June 2007) using observed sea level data from tidal stations around the Korean Peninsula. Comparisons of modeled and observed sea level revealed larger differences at the western coast of Korea than at the southern and eastern coasts. The seasonal variations of bias and root mean square error (RMSE) between the modeled and observed sea levels generally showed small differences in summer and large differences in winter. The average bias, RMSE, and correlation coefficients for 12 total stations between modeled and observed values were −0.13 m (−0.14 m), 0.47 m (0.47 m), and 0.79 (0.78) for 24-hour (48-hour) forecasts.