• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.2
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• pp.282-297
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• 2019

Detection of mesoscale oceanic eddies using satellite data can utilize various ocean parameters such as sea surface temperature (SST), chlorophyll-a pigment concentration in phytoplankton, and sea level altimetry measurements. Observation methods vary for each satellite dataset, as it is obtained using different temporal and spatial resolution, and optimized data processing. Different detection results can be derived for the same oceanic eddies; therefore, fundamental research on eddy detection using satellite data is required. In this study, we used ocean color satellite data, sea level altimetry data, and infrared SST data to detect mesoscale eddies in the East Sea and compared results from different detection methods. The sea surface current field derived from the consecutive ocean color chlorophyll-a concentration images using the maximum cross correlation coefficient and the geostrophic current field obtained from the sea level altimetry data were used to detect the mesoscale eddies in the East Sea. In order to compare the eddy detection from satellite data, the results were divided into three cases as follows: 1) the eddy was detected in both the ocean color and altimeter images simultaneously; 2) the eddy was detected from ocean color and SST images, but no eddy was detected in the altimeter data; 3) the eddy was not detected in ocean color image, while the altimeter data detected the eddy. Through these three cases, we described the difficulties with satellite altimetry data and the limitations of ocean color and infrared SST data for eddy detection. It was also emphasized that study on eddy detection and related research required an in-depth understanding of the mesoscale oceanic phenomenon and the principles of satellite observation.

• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.191-195
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• 2008

In Ansan (the headquarter of KORDI ; Korea Ocean Research & Development Institute), KOSC(Korea Ocean Satellite Center) is being prepared for acquisition, processing and distribution of sensor data via L-band from GOCI(Geostationary Ocean Color Imager) instrument which is loaded on COMS(Communication, Ocean and Meteorological Satellite); it will be launched in 2009. The basis equipment of KOSC(Electric power, Network, Security) has been constructed in 2007. KOSC is being constructed data processing and management system, GOCI L-band reception system, etc. The final object of KOSC is that maximize the application of GOCI.

• Proceedings of the KSRS Conference
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• v.1
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• pp.11-14
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• 2006

We present an overview on the observation and research for the China seas using both field experiments and multi-sensor satellite data at ORSI/OUC, covering two topics: (1) Spatial and temporal distribution of internal waves in the China Seas and retrieval of internal wave parameters; (2) Retrieval, validation, and cross-comparison of multi-sensor ocean color data as well as ocean optics in situ experiments in the East China Sea. We also present an incoherent Doppler wind lidar, developed by ORSI, and its observation for marine-atmospheric boundary layer.

• Journal of Satellite, Information and Communications
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• v.1 no.2
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• pp.25-31
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• 2006

The COMS (Communication, Ocean, and Meteorological Satellite) performing meteorological and ocean monitoring and providing communication service with meteorological, ocean and Ka-band payload in the geostationary orbit includes MODCS (Meteorological and Ocean Data Communication Subsystem) which provides transmitting the raw data collected by meteorological payload called MI (Meteorological Imager) and ocean payload named GOCI (Geostationary Ocean Color Imager) to the ground station and relaying the meteorological data processed on the ground to the end-user stations. MODCS comprises of two channels: SD channel which formats the raw data according to CCSDS recommendation, amplifies and transmits its signal to the ground station; MPDR channel which relays to the end-user stations the ground-processed meteorological data in the data format of LRIT/HRIT recommended by CGMS. This paper constructs the architecture of MODCS for transmitting and relating the observed data, and investigates that the key performance parameters have the required margin through the preliminary performance analyses.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.146-157
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• 2019

Recently, the importance of highly accurate bathymetric data is greatly emphasized by the increased use of the ocean numerical models and research results in major areas such as ocean forecasting and natural disaster. There are domestic bathymetric data mainly used in ocean numerical models of Choi et al.(2002) and Seo (2008), but the production year is old and the data was created on the basis of nautical charts. Nautical charts are made for the purpose of navigation and based on the minimum depth from bathymetric data, so there is a limitation to reproduce the actual submarine topography. Korea Hydrographic and Oceanographic Agency (KHOA) produces nautical charts every year through continuous bathymetric survey, but no bathymetric data for numerical models have been produced. In this study, using the raw bathymetric survey data, we built an exclusive bathymetric dataset (BADA Ver.1) for ocean numerical models and compared it with published bathymetric data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.333-344
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• 2021

Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model's RMS errors are 0.93 and 0.35~1.95, respectively.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.239-249
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• 2010

The Geostationary Ocean Color Imager (GOCI) data-processing system (GDPS), which is a software system for satellite data processing and analysis of the first geostationary ocean color observation satellite, has been developed concurrently with the development of th satellite. The GDPS has functions to generate level 2 and 3 oceanographic analytical data, from level 1B data that comprise the total radiance information, by programming a specialized atmospheric algorithm and oceanic analytical algorithms to the software module. The GDPS will be a multiversion system not only as a standard Korea Ocean Satellite Center(KOSC) operational system, but also as a basic GOCI data-processing system for researchers and other users. Additionally, the GDPS will be used to make the GOCI images available for distribution by satellite network, to calculate the lookup table for radiometric calibration coefficients, to divide/mosaic several region images, to analyze time-series satellite data. the developed GDPS system has satisfied the user requirement to complete data production within 30 minutes. This system is expected to be able to be an excellent tool for monitoring both long-term and short-term changes of ocean environmental characteristics.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2000.09a
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• pp.176-181
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• 2000

This paper briefly outline the approach we are now setting for improving the existing global ocean tidal charts in next few years. There has been notable progress in predicting global ocean tide in mid 90s to improve correction procedures for tidal signals in altimetry with more accurate tidal models (http://podaac.jpl.nasa.gov/) than existing ones. (omitted)

• Proceedings of the KSRS Conference
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• v.1
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• pp.90-93
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• 2006

The first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. The special feature with GOCI is that like MODIS, MERIS and GLI, it will include the band triplets 660-680-745 for the measurements of sun-induced chlorophyll-a fluorescence signal from the ocean. The GOCI will provide SeaWiFS quality observations with frequencies of image acquisition 8 times during daytime and 2 times during nighttime. With all the above features, GOCI is considered to be a remote sensing tool with great potential to contribute to better understanding of coastal oceanic ecosystem dynamics and processes by addressing environmental features in a multidisciplinary way. To achieve the objectives of the GOCI mission, we develop the GOCI Data Processing System (GDPS) which integrates all necessary basic and advanced techniques to process the GOCI data and deliver the desired biological and geophysical products to its user community. Several useful ocean parameters estimated by in-water and other optical algorithms included in the GDPS will be used for monitoring the ocean environment of Korea and neighbouring countries and input into the models for climate change prediction.

• Ocean and Polar Research
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• v.38 no.4
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• pp.271-286
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• 2016

We evaluate the temperature and salinity fields in the East Sea reproduced by the global ocean reanalysis data using HYbrid Coordinate Ocean Model (HYCOM for short). Temporal correlation of Sea Surface Temperature (SST) change between HYCOM and the Group for High Resolution Sea Surface Temperature (GHRSST) are higher in summer than winter. Though distributions of temperature and salinity in the HYCOM are similar to those from historical data (World Ocean Atlas 2013 V2), salinity in the HYCOM is lower (highter) in the region where the salinity is high (low). Temperature fields in the Ulleung basin of HYCOM are quite similar to those derived from Pressure-recording Inverted Echo Sounder (PIES), such as the correlation coefficient is higher than 0.7. This indicates that the HYCOM represents well the circulation and meso-scale phenomena in the Ulleung basin.