Sea surface wind plays an essential role in the simulating and predicting ocean phenomena. However, it is difficult to obtain accurate data with uniform spatiotemporal scale. A high-resolution (10 km) sea surface wind hindcast around the Korean Peninsula (KP) is presented using the weather research and forecasting model focusing on wind speed. The hindcast data for 39 years (1979–2017) are obtained by performing a three-dimensional variational analysis data assimilation, using ERA-Interim as initial and boundary conditions. To evaluate the added value of the hindcasts, the ASCAT-L2 satellite-based gridded data (DASCAT) is employed and regarded as “True” during 2008–2017. Hindcast and DASCAT data are verified using buoy observations from 1997–2017. The added value of the hindcast compared to ERA-Interim is evaluated using a modified Brier skill score method and analyzed for seasonality and wind intensity. Hindcast data primarily adds value to the coastal areas of the KP, particularly over the Yellow Sea in the summer, the East Sea in the winter, and the Korean Strait in all seasons. In case of strong winds (10–25 m·s⁻¹), the hindcast performed better in the East Sea area. The estimation of extreme wind speeds is performed based on the added value and 50-year and 100-year return periods are estimated using a Weibull distribution. The results of this study can provide a reference dataset for climate perspective storm surge and wave simulation studies.

The parametric typhoon model is a powerful typhoon prediction and reproduction tool with advantages in accuracy, and computational speed. To simulate typhoons’ horizontal features, the longitude and latitude of the typhoon center, central pressure, radius of maximum wind speed (Rmax), and background states (such as surface air pressure and wind speed) are required. When a typhoon approaches or is predicted to affect Korea, the Korea Meteorological Agency (KMA) notifies the above-mentioned parameters, except for the Rmax and background state. The contribution of background wind and pressure is not very significant; however, Rmax is essential for calculating typhoon winds. Therefore, the optimized Rmax for the typhoons over the past five years was estimated at each time step compared with the in situ wind observation record. After that, a fifth-order polynomial fitting was performed between the estimated Rmax and the radius of strong wind (RSW; >15 m/s) provided by the KMA. Finally, the Rmax was calculated from the RSW via the empirical equation, and the horizontal fields of typhoon Hinnamnor (2211) were reproduced using a parametric model. Furthermore, the ocean storm surge height was adequately simulated in the surge model.

Kwon, Y.-Y.; Choi, J.-W.; Kim, H.; Kim, S., and Kwon, J.-I., 2019. Comparison of the storm surge prediction models using different atmospheric forcing for typhoon CHABA. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 31-35. Coconut Creek (Florida), ISSN 0749-0208.Typhoon-induced storm surges can cause severe damage on the Korean Peninsula, where typhoons typically occur two or three times per year. In the case of Typhoon MAEMI (0314), a storm surge of 2.1 m was observed at Masan Port, causing $4.8 billion US dollars in property damage. Therefore, the accurate prediction of the storm surge heights and peak times of typhoon surges occurring in the vicinity of the Korean Peninsula is important for minimizing this damage. Two weather models, Weather Research and Forecast (WRF) and Hurricane Weather Research and Forecasting (HWRF) were used to compare storm surges using different atmospheric forcings, and storm surges were calculated using the DElft3D-FM model. By comparing the height and peak time with those of the actual storm, we found that the WRF model results underestimate the actual height and peak time of the typhoon CHABA surge, but the HWRF model is better than the WRF model at predicting the surge height and peak time of the storm surge occurring during typhoon CHABA.

This study presents a novel methodology for analyzing the relationship between sea level pressure (SLP) distributions and sea fog occurrences, focusing on a spatiotemporal similarity-based approach. Using SLP data from 2001 to 2019 and visibility observations from Baengnyeong Island (BYI), Yellow Sea, the proposed framework quantifies the connection between atmospheric pressure patterns and sea fog formation. The methodology integrates three key components: defining temporal and spatial domains, calculating weighted similarities, and validating the results using sea fog occurrence data. The temporal domain was set to a 7-hour period, determined by analyzing visibility trends prior to sea fog events. This period captures the critical atmospheric changes leading to fog formation. Spatially, a 2D weighted map was constructed using Pearson correlation coefficients between SLP variations at BYI and other locations in the study area. This weighting emphasizes regions with strong correlations, ensuring the analysis focuses on areas most relevant to sea fog dynamics. The Spatiotemporal Similarity Measure (STSM) method was then applied to compare reference SLP maps from 2017–2019 with historical SLP data from 2001–2015. By identifying historical cases with high similarity to reference conditions, the study examined the likelihood of sea fog occurrences under similar atmospheric setups. These similarities were categorized into thresholds, and their connection to sea fog events was evaluated using Probability of Detection (POD) and False Alarm Ratio (FAR) metrics. The results demonstrate that higher SLP similarity corresponds to increased POD and decreased FAR, validating the effectiveness of the STSM method. This approach highlights the role of recurring SLP patterns in sea fog formation and underscores the utility of historical data in improving sea fog forecasting.