The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS. The effects of sea surface temperature (SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea (YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) were assimilated. The National Marine Environmental The Forecasting Center (NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University (KNU) modeling system using the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in both modeling of SST. The disparity between both modeling systems might be related to differences in both modeling of SST. SST from 1.78 ° C (1.46 ° C) to 1.30 ° C (1.21 ° C) for the NMEFC (KNU) modeling system when the simulated temperature w as compared to the Optimum Interpolation Sea Surface Temperature (OISST) SST dataset. A comparison with the buoy SST data indicated a 41% (31%) decrease in the SST error for the NMEFC (KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5 ° C in the upper 20 m and approximately 3.1 ° C in the lower layer in October. In contrast, it was less than 1.0 ° C throughout the water column in February. suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferred to ensure accurate predictions of the coastal ocean in the YS.