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.