Abstract
The impacts of observation data sets on the high-resolution (1/24°) Northwest Pacific prediction system were investigated with the model sensitivity tests. We compared the model experiments assimilating the different combinations of the observation data sets, such as the sea surface height derived from satellite altimetry, sea surface temperature, and in-situ profiles, based on the Ensemble Optimal Interpolation. Pseudo-profiles constructed by the method of Cooper and Haines (1996, CH96) were assimilated into the model to assimilate sea surface height data. CH96 applied a conservation principle to derive pseudo-profiles by rearranging preexisting profiles. The comparison of the model experiments suggests that each observation data set enhances the model performance. Especially, the assimilation of the sea surface height reduces the model error by more than 9.81% and 6.44%, respectively, in terms of the root-mean-square error of the ocean temperature and salinity in the subsurface layer. It is interesting that the assimilation of the in-situ temperature profiles in the Korean marginal seas contributes to improving the reproducibility of the subsurface temperature and salinity in the East/Japan Sea (EJS) as well as Kuroshio-Kuroshio Extension (K-KE) regions. The improvement in the K-KE region seems to be related to the reproducibility of the Kuroshio axis. As the water mass in the EJS flows into the Pacific Ocean through the Tsugaru Strait, it affects the front of the Sanriku confluence, and it seems to eventually control the Oyashio Current and Kuroshio axis. In conclusion, this study evaluated the contribution of each observation component to ocean analysis in the KOOS-OPEM and confirmed the role of the existing observation networks. This study also suggests that greater attention should be paid to the role of regional ocean observation networks to improve the forecast skill of the ocean prediction system not only in the region but also in the open ocean, such as the Pacific Ocean.