• Ocean and Polar Research
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• v.40 no.3
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• pp.99-114
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• 2018

In the present study, we assess the GloSea5 (Global Seasonal Forecasting System version 5) near-surface ocean current forecasts using globally observed surface drifter dataset. Annual mean surface current fields at 0-day forecast lead time are quite consistent with drifter-derived velocity fields, and low values of root mean square (RMS) errors distributes in global oceans, except for regions of high variability, such as the Antarctic Circumpolar Current, Kuroshio, and Gulf Stream. Moreover a comparison with the global high-resolution forecasting system, HYCOM (Hybrid Coordinate Ocean Model), signifies that GloSea5 performs well in terms of short-range surface-current forecasts. Predictions from 0-day to 4-week lead time are also validated for the global ocean and regions covering the main ocean basins. In general, the Indian Ocean and tropical regions yield relatively high RMS errors against all forecast lead times, whilst the Pacific and Atlantic Oceans show low values. RMS errors against forecast lead time ranging from 0-day to 4-week reveal the largest increase rate between 0-day and 1-week lead time in all regions. Correlation against forecast lead time also reveals similar results. In addition, a strong westward bias of about $0.2m\;s^{-1}$ is found along the Equator in the western Pacific on the initial forecast day, and it extends toward the Equator of the eastern Pacific as the lead time increases.

• Ocean and Polar Research
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• v.43 no.2
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• pp.53-63
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• 2021

Changes in a marine environment have a broad socioeconomic implication on fisheries and their relevant industries so that there has been a growing demand for the medium-range (months to years) prediction of the marine environment Using a medium-range ocean prediction model (Ocean Mid-range prediction System, OMIDAS) for the northwest Pacific, this study attempted to assess seasonal difference in the mid-range predictability of the sea surface temperature (SST), focusing on the Korea seas characterized as a complex marine system. A three-month re-forecast experiment was conducted for each of the four seasons in 2016 starting from January, forced with Climate Forecast System version 2 (CFSv2) forecast data. The assessment using relative root-mean-square-error was taken for the last month SST of each experiment. Compared to the CFSv2, the OMIDAS revealed a better prediction skill for the Korea seas SST, particularly in the Yellow sea mainly due to a more realistic representation of the topography and current systems. Seasonally, the OMIDAS showed better predictability in the warm seasons (spring and summer) than in the cold seasons (fall and winter), suggesting seasonal dependency in predictability of the Korea seas. In addition, the mid-range predictability for the Korea seas significantly varies depending on regions: the predictability was higher in the East Sea than in the Yellow Sea. The improvement in the seasonal predictability for the Korea seas by OMIDAS highlights the importance of a regional ocean modeling system for a medium-range marine prediction.

• Atmosphere
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• v.26 no.4
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• pp.599-615
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• 2016

This study investigates the accuracy of short-term ocean predictions during the development of ocean stratification for the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 5 (GloSea5) as well as the effect of atmosphere-ocean coupling on the predictions through a series of sensitive numerical experiments. Model performance is evaluated using the marine meteorological buoys at seas around the Korean peninsular (KP), Tropical Atmosphere Ocean project (TAO) buoys over the tropical Pacific ocean, and ARGO floats data over the western North Pacific for boreal winter (February) and spring (May). Sensitive experiments are conducted using an ocean-atmosphere coupled model (i.e., GloSea5) and an uncoupled ocean model (Nucleus for European Modelling of the Ocean, NEMO) and their results are compared. The verification results revealed an overall good performance for the SST predictions over the tropical Pacific ocean and near the Korean marginal seas, in which the Root Mean Square Errors (RMSE) were $0.31{\sim}0.45^{\circ}C$ and $0.74{\sim}1.11^{\circ}C$ respectively, except oceanic front regions with large spatial and temporal SST variations (the maximum error reached up to $3^{\circ}C$). The sensitive numerical experiments showed that GloSea5 outperformed NEMO over the tropical Pacific in terms of bias and RMSE analysis, while NEMO outperformed GloSea5 near the KP regions. These results suggest that the atmosphere-ocean coupling substantially influences the short-term ocean forecast over the tropical Pacific, while other factors such as atmospheric forcing and the accuracy of simulated local current are more important than the coupling effect for the KP regions being far from tropics during the development of ocean stratification.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.2
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• pp.84-94
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• 2018

In order to overcome the limitation of deterministic forecast, an ensemble forecasting system for regional ocean wave is developed. This system predicts ocean wind waves based on the meteorological forcing from the Ensemble Prediction System for Global of the Korea Meteorological Administration, which is consisted of 24 ensemble members. The ensemble wave forecasting system is evaluated by using the moored buoy data around Korea. The root mean squared error (RMSE) of ensemble mean showed the better performance than the deterministic forecast system after 2 days, especially RMSE of ensemble mean is improved by 15% compared with the deterministic forecast for 3-day lead time. It means that the ensemble method could reduce the uncertainty of the deterministic prediction system. The Relative Operating Characteristic as an evaluation scheme of probability prediction was bigger than 0.9 showing high predictability, meaning that the ensemble wave forecast could be usefully applied.

• Atmosphere
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• v.27 no.3
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• pp.261-276
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• 2017

The main objectives of this study are to introduce Global Seasonal forecasting system version5 (GloSea5) of KMA and to evaluate the performance of ensemble prediction of system. KMA has performed an operational seasonal forecast system which is a joint system between KMA and UK Met office since 2014. GloSea5 is a fully coupled global climate model which consists of atmosphere (UM), ocean (NEMO), land surface (JULES) and sea ice (CICE) components through the coupler OASIS. The model resolution, used in GloSea5, is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, we evaluate the performance of this system using by RMSE, Correlation and MSSS for ensemble mean values. The forecast (FCST) and hindcast (HCST) are separately verified, and the operational data of GloSea5 are used from 2014 to 2015. The performance skills are similar to the past study. For example, the RMSE of h500 is increased from 22.30 gpm of 1 week forecast to 53.82 gpm of 7 week forecast but there is a similar error about 50~53 gpm after 3 week forecast. The Nino Index of SST shows a great correlation (higher than 0.9) up to 7 week forecast in Nino 3.4 area. It can be concluded that GloSea5 has a great performance for seasonal prediction.

• Atmosphere
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• v.29 no.2
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• pp.219-226
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• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.22 no.3
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• pp.103-117
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• 2017

This note provides technical guide on three issues associated with establishing and automatically running regional ocean forecasting systems: (1) a strategy for continuous production of hourly-interval three-day ocean forecast data, (2) the daily download of ocean and atmospheric forecasting data (i.e., HYCOM and NOAA/NCEP GFS data), which are provided by outside institutions and used as initial condition, surface forcing, and boundary data for regional ocean models, and (3) error notifications to numerical model managers through the Short Message Service (SMS). Guidance on dealing with these three issues is illustrated via solutions implemented by the Korea Hydrographic and Oceanographic Agency, since in embarking on this project we found that this procedural information was not readily available elsewhere. This technical guide is based on our experiences and lessons learned during the process of establishing and operating regional ocean forecasting systems for the East Sea and the Yellow and East China Seas over the 5 year period of 2012-2016. The fundamental approach and techniques outlined in this guide are of use to anyone wanting to establish an automatic regional and coastal ocean forecasting system.

• Ocean and Polar Research
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• v.36 no.4
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• pp.505-514
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• 2014

The development of deep seabed manganese nodules has been carried out with the aim of commercial development in 2023. It is important to forecast the price of the four metals (copper, nickel, cobalt, and manganese) extracted from manganese nodules because price change is a criterion for investment decision. The main purpose of the study is to forecast the price of four metals using the ARIMA model and VAR model, and calculate the MAPE to compare a goodness-of-fit between the two models. The estimated results of the two models reveal statistical significance and are in keeping with economic theory. The results of MAPE for goodness-of-fit show that the VAR model is between 0.1 and 0.2, and the ARIMA model is between 0.4 and 0.6. That is, the VAR model is better than the ARIMA model in forecasting changes in the price of metals.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2006.09a
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• pp.33-36
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• 2006

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.645-648
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• 2005

The COMS(Communication, Ocean and Meteorological Satellite), a multi-mission geo-stationary satellite, is being developed by KARl. The first mission of the COMS is the meteorological image and data gathering for weather forecast by using a five channel meteorological imager. The second mission is the oceanographic image and data gathering for marine environment monitoring around Korean Peninsula by using an eight channel Geostationary Ocean Color Imager(GOCI). The third mission is newly developed Ka-Band communication payload certification test in space by providing communication service in Korean Peninsula and Manjurian area. There were many low Earth orbit satellites for ocean monitoring. However, there has never been any geostationary satellite for ocean monitoring. The COMS is going to be the first satellite for ocean monitoring mission on the geo-stationary orbit. The meteorological image and data obtained by the COMS will be distributed to end users in Asia-Pacific area and it will contribute to the improved weather forecast.