A wave forecasting system using FUNWAVE-TVD which is based on the fully nonlinear Boussinesq equations by Chen (2006) was developed to provide an accurate wave prediction in the Port of Busan, South Korea. This system is linked to the Korea Operational Oceanographic System (KOOS) developed by Park et al. (2015). The computational domain covers a region of 9.6 km×7.0 km with a grid size of 2 m in both directions, which is sufficient to resolve short waves and dominant sea states. The total number of grid points exceeds 16 millions, making the model computational expensive. To provide real-time forecasting, an interpolation method, which is based on pre-calculated results of FUNWAVE-TVD and SWAN forecasting results at the FUNWAVE-TVD offshore boundary, was used. A total of 45 cases were pre-calculated, which took 71 days on 924 computational cores of a Linux cluster system. Wind wave generation and propagation from the deep water were computed using the SWAN in KOOS. SWAN results provided a boundary condition for the FUNWAVE-TVD forecasting system. To verify the model, wave observations were conducted at three locations inside the port in a time period of more than 7 months. A model/model comparison between FUNWAVE-TVD and SWAN was also carried out. It is found that, FUNWAVE-TVD improves the forecasting results significantly compared to SWAN which underestimates wave heights in sheltered areas due to incorrect physical mechanism of wave diffraction, as well as large wave heights caused by wave reflections inside the port.

Sea surface temperature SST obtained from the initial version of the Korea Operational Oceanographic System (KOOS) SST satellite have low accuracy during summer and daytime. This is attributed to the diurnal warming effect. Error estimation of SST data must be carried out to use the real-time forecasting numerical model of the KOOS. This study suggests two quality control methods for the KOOS SST system. To minimize the diurnal warming effect, SSTs of areas where wind speed is higher than 5 m/s were used. Depending on the wind threshold value, KOOS SST data for August 2014 were reduced by 0.15℃. Errors in SST data are considered to be a combination of random, sampling, and bias errors. To estimate bias error, the standard deviation of bias between KOOS SSTs and climatology SSTs were used. KOOS SST data yielded an analysis error standard deviation value similar to OSTIA and NOAA NCDC (OISST) data. The KOOS SST shows lower random and sampling errors with increasing number of observations using six satellite datasets. In further studies, the proposed quality control methods for the KOOS SST system will be applied through more long-term case studies and comparisons with other SST systems.

A down-scaled operational oceanographic system is developed for the coastal waters of Korea using a regional ocean modeling system (ROMS). The operational oceanographic modeling system consists of atmospheric and hydrodynamic models. The hydrodynamic model, ROMS, is coupled with wave, sediment transport, and water qualitymodules. The system forecasts the predicted results twice a day on a 72 h basis, including sea surface elevation, currents, temperature, salinity, storm surge height, and wave information for the coastal waters of Korea. The predicted results are exported to the web-GIS-based coastal information system for real-time dissemination to the public and validation with real-time monitoring data using visualization technologies. The ROMS is two-way coupledwith a simulatingwaves nearshoremodel, SWAN, for the hydrodynamics and waves, nested with themeteorologicalmodel,WRF, for the atmospheric surface forcing, and externally nested with the eutrophicationmodel, CE-QUAL-ICM, for the water quality. The operational model, ROMS, was calibrated with the tidal surface observed with a tide-gage and verified with current data observed by bottom-mounted ADCP or AWAC near the coastal waters of Korea. To validate the predicted results, we used real-time monitoring data derived from remote buoy system, HF-radar, and geostationary ocean color imager (GOCI). This down-scaled operational coastal forecasting system will be used as a part of the Korea operational oceanographic system(KOOS) with other operational oceanographic systems.

In the present article, we introduce a high resolution sea surface temperature (SST) product generated daily by Korea Institute of Ocean Science and Technology (KIOST). The SST product is comprised of four sets of data including eight-hour and daily average SST data of 1 km resolution, and is based on the four infrared (IR) satellite SST data acquired by advanced very high resolution radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), Multifunctional Transport Satellites-2 (MTSAT-2) Imager and Meteorological Imager (MI), two microwave radiometer SSTs acquired by Advanced Microwave Scanning Radiometer 2 (AMSR2), and WindSAT with in-situ temperature data. These input satellite and in-situ SST data are merged by using the optimal interpolation (OI) algorithm. The root-mean-square-errors (RMSEs) of satellite and in-situ data are used as a weighting value in the OI algorithm. As a pilot product, four SST data sets were generated daily from January to December 2013. In the comparison between the SSTs measured by moored buoys and the daily mean KIOST SSTs, the estimated RMSE was 0.71℃ and the bias value was -0.08℃. The largest RMSE and bias were 0.86 and -0.26℃ respectively, observed at a buoy site in the boundary region of warm and cold waters with increased physical variability in the Sea of Japan/East Sea. Other site near the coasts shows a lower RMSE value of 0.60℃ than those at the open waters. To investigate the spatial distributions of SST, the Group for High Resolution Sea Surface Temperature (GHRSST) product was used in the comparison of temperature gradients, and it was shown that the KIOST SST product represents well the water mass structures around the Korean Peninsula. The KIOST SST product generated from both satellite and buoy data is expected to make substantial contribution to the Korea Operational Oceanographic System (KOOS) as an input parameter for data assimilation.

The Maenggol Channel and Uldolmok Strait, located on the south-west coast of Korea, have notably strong and complex currents due to tidal effects and to local geological factors. In these areas, electric power has been generated using strong tidal currents, the speed of which is more than 3 m/s during spring tides. The region also provides a shortcut for navigation. These tidal conditions are therefore sometimes useful, but may also cause terrible accidents or severe economic damage, in the absence of accurate information regarding ocean conditions. In April 2014, the passenger ferry MV Sewol capsized in the Maenggol Channel, with 295 passengers killed and 9 still missing. While this was unquestionably a man-made disaster, strong currents were one of the contributing causes. It was also difficult to conduct scuba diving rescue operations given strong current speeds, and accurate prediction of the time when the tide would turn was thus critically needed. In this research, we used the high-resolution coastal circulation forecasting system of KOOS (Korea Operational Oceanographic System) for analysis and simulation of strong tidal currents in such areas with many small islands, using measurements and modeling from this research area. For accurate prediction of tidal currents, small grid size-modeling was needed, and in this study, we identified a suitable grid size that offers efficiency as well as accuracy.