• Ocean and Polar Research
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• 제37권4호
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• pp.249-263
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• 2015

Standardization work for the ocean data produced by a variety of national oceanographic research projects was conducted in order to establish a national ocean data sharing system. For this work, we first prepared standard proposals for the national research ocean data by reviewing and analyzing of existing international and domestic ocean-data standards. The proposed standards were reviewed and revised by experts in the field of oceanography and academic societies for documentation. The 125-page technical report on the standards of 25 data items was prepared as an output of this research work, which is available free of charge for the public and interested parties. This paper explains the proposed standards of metadata and codes regarding the common properties of all the oceanographic data items. Especially, the standards for the metadata, codes and data formats of 4 physical data items were described in detail. In order to be adopted as the national standards for ocean data, however, the standards suggested here require further development and/or modification based on additional reviews of and ample feedbacks from the relevant academic and technical communities.

• 대한원격탐사학회지
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• 제26권2호
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• pp.263-275
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• 2010

정지궤도 해색탑재체(GOCI, Geostationary Ocean Color Imager)의 주관 운영기관인 해양위성센터 (KOSC, Korea Ocean Satellite Center)는 한국해양연구원에 기반시설을 구축하였다. 또한, 해양위성센터는 수신시스템(GDAS), 전처리시스템(IMPS), 처리시스템(GDPS), 배포시스템(GDDS), 자료교환시스템(DMS), 기관간 자료교환시스템(EDES), 통합감시제어시스템(TMC) 등 GOCI 데이터의 서비스를 위한 준비를 완료하였다. 해양위성센 터에서는 매일 8번 관측되는 GOCI 데이터를 수신하고, 처리하여 배포정책에 따라 Level 1B 이후의 데이터를 사용자에게 배포하게 된다. 여기서는 해양위성센터의 시스템과 배포정책에 대한 개요를 설명하고, 사용자가 해양위성센터의 홈페이지에서 GOCI 데이터를 검색 요청하고 다운로드할 수 있는 방법을 소개한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.231-236
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• 1999

Accurate ocean surface fluxes with high resolution are critical for understanding a mechanism of global climate. However, it is difficult to derive those fluxes by using ocean observation data because the number of ocean observation data is extremely small and the distribution is inhomogeneous. On the other hand. satellite data are characterized by the high density, the high resolution and the homogeneity. Therefore, it can be considered that we obtain accurate ocean surface by using satellite data. Recently we constructed ocean surface data sets mainly using satellite data. The data set is named by Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO). Here, we introduce J-OFURO. The data set includes shortwave radiation, longwave radiation, latent heat flux, sensible heat flux, and momentum flux etc. Moreover, sea surface dynamic topography data are included in the data set. Radiation data sets covers western Pacific and eastern Indian Ocean because we use a Japanese geostationally satellite (GMS) to estimate radiation fluxes. On the other hand, turbulent heat fluxes are globally estimated. The constructed data sets are used and shows the effectiveness for many scientific studies.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.75-78
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• 2006

The Korea Ocean Satellite Center (KOSC) is under development to establish in line with the launch of the first Korean multi-function geostationary satellite COMS (Communication, Ocean and Meteorological Satellite) scheduled in 2008. KOSC aims to receive, process and distribute Geostationary Ocean Color Sensor (GOCI) data on board COMS in near-real time. In this report, current status of KOSC development is presented in the following categories; site selection for KOSC, antenna design, GOCI data receiving and processing system, data distribution, future works.

• 한국지리정보학회지
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• 제22권4호
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• pp.215-228
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• 2019

해양관측 정지궤도 위성인 GOCI(Geostationary Ocean Color Imager) 데이터는 대용량 산출물을 효과적으로 저장, 배포하기 위해 HDF5 자료 형식을 사용하고 있다. 해양위성센터에서는 HDF5(Hierarchical Data Format version5) 포맷에 익숙지 않은 일반 사용자를 위해 GDPS(GOCI Data Processing System)를 개발하여 관측자료와 함께 제공하고 있다. 그럼에도 불구하고 위성데이터 특성에 대한 이해와 GDPS의 사용법을 익혀야 하는 점, 그리고 위치정보와 속성정보가 분리되어 있는 HDF5 형식의 자료를 병합하고 가공하는 일은 쉽지 않은 일이다. 따라서 본 연구에서는 오픈소스 R과 rhdf5, data.table, matrixStats 패키지를 이용하여 GDPS를 이용하는 과정 없이도 HDF5 형식의 위성데이터를 손쉽게 활용할 수 있는 알고리즘을 개발하였다.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2003년도 추계학술대회 논문집
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• pp.336-339
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• 2003

A digital data logging system has been developed for the purpose of a compact offline Ocean Bottom Seismometer(OBS). The Digital Data Logger(DDL) consists of A/D system, Micom with storage memory and firmware managing data files. The A/D system acquires data of 16bit/4ch with sampling rate of 250Hz per channel. The Micom, a micro controller board with T33521 processor of 8051 class, was equipped with 8 flash memories of 128MB for data storage capacity of 1GB. The firmware stores the acquiring data in form of binary files. The DDL was designated to be compact and light and to consume low energy as possible. The DDL is to interface with PC through USB(Universal Serial Bus). The performance of the DDL has been validated through tests with respect to a 3-axis seismometer.

• 대한원격탐사학회지
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• 제29권6호
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• pp.601-610
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• 2013

As a standard water clarity variable, the vertical underwater visibility, called Secchi depth, is estimated with ocean color satellite data. In the present study, Moderate Resolvtion Imaging Spectradiometer (MODIS) data are used to measure the Secchi depth which is a useful indicator of ocean transparency for estimating the water quality and productivity. To estimate the Secchi depth $Z_v$, the empirical regression model is developed based on the satellite optical data and in-situ data. In the previous study, a semi-analytical algorithm for estimating $Z_v$ was developed and validated for Case 1 and 2 waters in both coastal and oceanic waters using extensive sets of satellite and in-situ data. The algorithm uses the vertical diffuse attenuation coefficient, $K_d$($m^{-1}$) and the beam attenuation coefficient, c($m^{-1}$) obtained from satellite ocean color data to estimate $Z_v$. In this study, the semi-analytical algorithm is validated using temporal MODIS data and in-situ data over the Yellow, Southern and East Seas including Case 1 and 2 waters. Using total 156 matching data, MODIS $Z_v$ data showed about 3.6m RMSE value and 1.7m bias value. The $Z_v$ values of the East Sea and Southern Sea showed higher RMSE than the Yellow Sea. Although the semi-analytical algorithm used the fixed coupling constant (= 6.0) transformed from Inherent Optical Properties (IOP) and Apparent Optical Properties (AOP) to Secchi depth, various coupling constants are needed for different sea types and water depth for the optimum estimation of $Z_v$.

• Ocean and Polar Research
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• 제37권3호
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• pp.225-233
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• 2015

To understand the fluctuation of global carbon levels caused by the biogeochemical cycle within the ocean interior, it is essential to achieve comparability of global oceanic nutrient data to a fairly high degree. The Scientific Committee on Ocean Research (SCOR) commissioned a working group (WG147) to establish a system for achieving comparability of oceanic nutrient data within 1% among laboratories around the world. The introduction of international activities for improving nutrient comparability will facilitate the use of nutrient reference material of seawater by researchers within Korea, which will help in meeting international standards of nutrient comparability and promote international cooperation.

• Ocean Systems Engineering
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• 제14권1호
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• pp.85-99
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• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.224-227
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• 2006

For estimation of three inherent optical properties (IOPs), the absorption coefficients for phytoplankton ($a_{ph}$) and suspended solid particle ($a_{ss}$) and dissolved organic matter ($a_{dom}$), from ocean reflectance, we used inversion of remote sensing reflectance model (Ahn et al., 2001) at this study. The IOP inversion model assumes that (1) the relationship between remote sensing reflectance ($R_{rs}$) and absorption (a) and backscattering ($b_{b}$) is well known, (2) the optical coefficients for pure water ($a_{w}$, $b_{bw}$) are known, (3) the spectral shapes of the specific absorption coefficients for phytoplankton ($a^*_{ph}$) and suspended solid particle ($a^*_{ss}$) and the specific backscattering coefficients for phytoplankton ($b_b^*_{ph}$) and suspended solid particle ($b_b^*_{ss}$) are known. The input data of IOP inversion model is used in-situ ocean optical data at the seawater around the Korea Peninsula for 5 years (2001-2005). We compared the output data of the IOP inversion model and the in-situ observation for seawater around the Korea Peninsula.