We aimed to evaluate the effectiveness of ensemble optimal interpolation (EnOI) in improving the analysis of significant wave height (SWH) within wave models using satellite-derived SWH data. Satellite observations revealed higher SWH in mid-latitude regions (30o to 60o in both hemispheres) due to stronger winds, whereas equatorial and coastal areas exhibited lower wave heights, attributed to calmer winds and land interactions. Root mean square error (RMSE) analysis of the control experiment without data assimilation revealed significant discrepancies in high-latitude areas, underscoring the need for enhanced analysis techniques. Data assimilation experiments demonstrated substantial RMSE reductions, particularly in high-latitude regions, underscoring the effectiveness of the technique in enhancing the quality of analysis fields. Sensitivity experiments with varying ensemble sizes showed modest global improvements in analysis fields with larger ensembles. Sensitivity experiments based on different decorrelation length scales demonstrated significant RMSE improvements at larger scales, particularly in the Southern Ocean and Northwest Pacific. However, some areas exhibited slight RMSE increases, suggesting the need for region-specific tuning of assimilation parameters. Reducing the observation error covariance improved analysis quality in certain regions, including the equator, but generally degraded it in others. Rescaling background error covariance (BEC) resulted in overall improvements in analysis fields, though sensitivity to regional variability persisted. These findings underscore the importance of data assimilation, parameter tuning, and BEC rescaling in enhancing the quality and reliability of wave analysis fields, emphasizing the necessity of region-specific adjustments to optimize assimilation performance. These insights are valuable for understanding ocean dynamics, improving navigation, and supporting coastal management practices.

The interannual variability of summer temperature during June-August (JJA) in South Korea was associated with geopotential height averaged in the East Sea (Korea-Japan Index, KJI) and in the subtropical western North Pacific (Western North Pacific Subtropical High Index, WNPSHI). The KJI was coupled with a decaying El Niño one month in advance, while the WNPSHI was influenced by Sea Surface Temperature (SST) anomaly in the western North Pacific and a developing El Niño one to three months ahead. Additionally, the JJA temperature over South Korea was affected by SST anomaly in the western North Pacific in May. Based on these teleconnections, a multivariate regression model using the SST surrogates for the KJI and WNPSHI and an univariate model using an area-averaged May SST were developed to reconstruct the JJA temperature over South Korea. Both of the empirical models reproduced the JJA and monthly temperatures reasonably well. However, when the simulated SSTs from global climate models were used, the multivariate model outperformed the univariate model. Further, for JJA temperature prediction, the multivariate model with 6-month lead SST outstripped one-month lead prediction of global climate models. Therefore, the empirical-dynamical approach can pave a promising way for summer temperature prediction in South Korea.