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Abstract

The third-generation wave model WW3 (Wave Watch III), provided by NOAA, is widely used for wave research. Data assimilation to modify initial wind and wave conditions has contributed to improving the results of the wave model (Xu et al. 2021). According to Kim et al. (2021), applying data assimilation results in more excellent outcomes than without it. In this study, we used Ensemble Optimal Interpolation (EnOI), which require less computation than Ensemble Kalman Filter (EnKF) and have excellent performance. However, in most cases of wave models, the influence of improvement after data assimilation is limited in time because it depends on the predicted wind field (Kim et al. 2021). To address this limitation, we applied a wind-stress parameterization that takes into account air stability based on the air-sea temperature difference. To extend the effect of data assimilation, the observed significant wave height was assimilated into the model to update the initial field and air-sea temperature difference. The updated air-sea temperature difference is expected to extend the effect of the data assimilation through improving the wind-stress parameterization. In this study, we have compared the performances of the model with and without updating air-sea temperature difference to evaluate the effect of the data assimilation.