• Journal of Korean Society of Coastal and Ocean Engineers
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• v.23 no.3
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• pp.236-247
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• 2011

This study investigates sea level (SL) rise due to global warming in the Northwestern Pacific (NWP) and Seas around the Korean peninsula (KP) using outputs of IPCC AR4 climate models. Particularly, components of the SL rise induced by a local steric effect, which was not considered in most climate models, were computed using model-projected 3-dimensional temperature and salinity data. Analysis of the SL data shows that the ratio of the SL rise in the NWP and KP was about two times higher than that in global mean and particularly the ratio in the Kuroshio extension region was the highest. The SL rises over 100 years estimated from MPI_ECHAM5 and GFDL_CM2.1 model by A1B scenario considering the thermosteric effect were 24 cm and 28 cm for the NWP and 27 cm and 31 cm for the Seas around the KP, respectively. Statistical analysis reveals that these SL rises are caused by the weakening of the Siberian High in winter as well as variations of pressure system in the NWP and by the resultant change of water temperature. It also found that the highest SL rise in the Kuroshio extension region of the NWP was connected with the large increase of water temperature in this area.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.40-53
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• 2021

Research vessels(R/Vs) used for ocean research move to the planned research area and perform ocean observations suitable for the research purpose. The five research vessels of the Korea Institute of Ocean Science & Technology(KIOST) are equipped with global positioning system(GPS), water depth, weather, sea surface layer temperature and salinity measurement equipment that can be observed at all times during cruise. An information platform is required to systematically manage and utilize the data produced through such continuous observation equipment. Therefore, the data flow was defined through a series of business analysis ranging from the research vessel operation plan to observation during the operation of the research vessel, data collection, data processing, data storage, display and service. After creating a functional design for each stage of the business process, KIOST Underway Meteorological & Oceanographic Information System(KUMOS), a Web-Geographic information system (Web-GIS) based information platform, was built. Since the data produced during the cruise of the R/Vs have characteristics of temporal and spatial variability, a quality management system was developed that considered these variabilities. For the systematic management and service of data, the KUMOS integrated Database(DB) was established, and functions such as R/V tracking, data display, search and provision were implemented. The dataset provided by KUMOS consists of cruise report, raw data, Quality Control(QC) flagged data, filtered data, cruise track line data, and data report for each cruise of the R/V. The business processing procedure and system of KUMOS for each function developed through this study are expected to serve as a benchmark for domestic ocean-related institutions and universities that have research vessels capable of continuous observations during cruise.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.159-161
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• 2007

Geostationary Ocean Color Imager (GOCI), the world-first ocean remote sensing instrument on geostationary Communication, Ocean, Meteorological Satellite (COMS), will be able to take a picture of a large region several times a day (almost with every one hour interval). We, KORDI, are in charge for developing the GOCI data processing system (GDPS) which is the basic software for processing the data from GOCI. The GDPS will be based on windows operating system to produce the GOCI level 2 data products (useful for oceanographic environmental analysis) automatically in real-time mode. Also, the GDPS will be a user-interactive program by well-organized graphical user interfaces for data processing and visualization. Its products will be the chlorophyll concentration, amount of total suspended sediments (TSS), colored dissolved organic matters (CDOM) and red tide from water leaving radiance or remote sensing reflectance. In addition, the GDPS will be able to produce daily products such as water current vector, primary productivity, water quality categorization, vegetation index, using individual observation data composed from several subscenes provided by GOCI for each slit within the target area. The resulting GOCI level 2 data will be disseminated through LRIT using satellite dissemination system and through online request and download systems. This software is carefully designed and implemented, and will be tested by sub-contractual company until the end of this year. It will need to be updated in effect with respect to new/improved algorithms and the calibration/validation activities.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.3
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• pp.149-159
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• 2014

In this study, we develop an algorithm to predict swell-like high waves on the east coast of Korea using the directional wave gauge which was installed near Sokcho. Using the numerical wave model SWAN, we estimate wave data in open sea from the wave data collected by using the directional wave gauge. Then, using the wave ray method and SWAN model with the open-sea wave data as offshore boundary conditions, we predict the swell-like high waves at several major points on the east coast of Korea. We verify the prediction methods with the SWAN and wave ray methods by comparing predicted data against measured one at Wangdolcho. We can improve the prediction of the swell-like high waves in the east sea of Korea using both the real-time wave measurement system and the present prediction algorithm.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.693-703
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• 2019

Long-term coastal topographical data is critical for analyzing temporal and spatial changes in shorelines. Especially understanding the change trends is essential for future coastal management. For this research, in the data preparation, we obtained digital aerial images, terrestrial laser scanning data and UAV images in the year of 2009. 2018 and 2019 respectively. Also tidal observation data obtained by the Korea Hydrographic and Oceanographic Agency were used for Bangamoeri beach located in Ansan, Gyeonggi-do. In the process of it, we applied the photogrammetric technique to extract the coastline of 4.40 m from the stereo images of 2009 by stereoscopic viewing. In 2018, digital elevation model was generated by using the raw data obtained from the laser scanner and the corresponding shoreline was semi-automatically extracted. In 2019, a digital elevation model was generated from the drone images to extract the coastline. Finally the change rate of shorelines was calculated using Digital Shoreline Analysis System. Also qualitative analysis was presented.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.326-336
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• 2018

Continuous measurement of nearshore waves around Korea over long period is very demanding to setup plans for prevention of disasters of port and coastal structures. In this respect, a new web-based system, termed as WINK, was established, which collects nearshore wave data from Korea Meteorological Agency (KMA), Korea Hydrographic and Oceanographic Agency (KHOA), and Ministry of Oceans and Fisheries (MOF) and provide them after quality control of the data. This paper describes technical aspects regarding collection and selection of the wave observation data, construction of wave hindcasting data, the methodology of quality control for the selected wave data, and overall process of building the web-based data providing system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2329-2341
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• 2013

In this study, we develop an algorithm to predict swell-like significant waves in the east coast of Korea using the directional wave gauge which is installed near Sokcho. Using the numerical wave model SWAN, we estimate wave data in open sea from the wave data observed through the directional wave gauge. Then, using the wave ray method with the open-sea wave data as offshore boundary conditions, we predict the swell-like significant waves at several points in the east coast of Korea. We verify the prediction methods with the SWAN and wave ray methods by comparing numerically predicted data against either target or measured data at the observation site. We can improve the prediction of the swell-like significant waves in the east sea of Korea using both the real-time wave measurement system and the present prediction algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.896-898
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• 2012

Recently, many countries are making efforts for the development of ocean resources because the necessity and importance of the ocean resources are increased. Underwater sensor networks have emerged as a very powerful technique for many applications, including monitoring, measurement, surveillance and control and envisioned to enable applications for oceanographic data collection, ocean sampling, environmental and pollution monitoring, offshore exploration, disaster prevention, tsunami and seaquake warning, assisted navigation, distributed tactical surveillance, and mine reconnaissance. The idea of applying sensor networks into underwater environments (i.e., forming underwater sensor networks) has received increasing interests in monitoring aquatic environments for scientific, environmental, commercial, safety, and military reasons. The data obtained by observing around the environment are wireless-transmitted by a radio set with various waves. According to the technical development of the medium set, some parameters restricted in observing the ocean have been gradually developed with the solution of power, distance, and corrosion and watertight by the seawater. The actual matters such as variety of required data, real-time observation, and data transmission, however, have not enough been improved just as various telecommunication systems on the land. In this paper, a wireless management system will be studied through a setup of wireless network available at fishery around the coast, real-time environmental observation with RF sensor, and data collection by a sensing device at the coastal areas.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.