• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.251-256
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• 2005

담수와 염수가 적절히 혼합되어있는 만에서는 굴과 가리비 미역 등의 양식업이 활발히 이루어지고 있다. 이러한 바다생물 특히, 미역의 경우는 염분농도가 13psu 이하의 상태가 장시간 유지될 경우 심한 피해를 입는다는 보고가 있다. 그러므로 만에서의 염분분포 파악은 자연환경의 적절한 활용 및 자연재해의 예방적 측면에서 매우 중요하다고 사료되어진다. 본 연구에서는 만에서의 염분분포를 파악하기 위하여 위성영상을 활용하였다. 위성영상은 현지관측이 어려운 홍수기에도 데이터 취득이 가능하며, 대상 지역 전체를 한눈에 볼 수 있는 면분포를 제공한다는 장점이 있다. 본 연구에서 수행한 구체적인 방법은 다음과 같다. 첫째, 위성영상과 탁도와의 높은 상관을 이용하여 위성영상을 탁도 분포로 변환하였다. 둘째, 현지 관측으로부터 얻은 염분과 탁도와의 상관관계를 이용하여 탁도 분포를 염분 분포로 변환하였다. 끝으로, 본 추정수법은 현지관측결과와의 검증을 통하여 타당성을 입증하였다. 결론적으로, 본 수법은 현지관측에 드는 비용절감과, 관측 데이터가 부족한 홍수기의 자연현상 파악등에 그 활용 가치가 있다고 확인되었다.

• Journal of the Korean earth science society
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• v.41 no.5
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• pp.469-477
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• 2020

Salinity is not only an important variable that determines the density of the ocean but also one of the main parameters representing the global water cycle. Ocean salinity observations have been mainly conducted using ships, Argo floats, and buoys. Since the first satellite salinity was launched in 2009, it is also possible to observe sea surface salinity in the global ocean using satellite salinity data. However, the satellite salinity data contain various errors, it is necessary to validate its accuracy before applying it as research data. In this study, the salinity accuracy between the Soil Moisture Active Passive (SMAP) satellite salinity data and the in-situ salinity data provided by the Ieodo ocean research station was evaluated, and the error characteristics were analyzed from April 2015 to August 2020. As a result, a total of 314 match-up points were produced, and the root mean square error (RMSE) and mean bias of salinity were 1.79 and 0.91 psu, respectively. Overall, the satellite salinity was overestimated compare to the in-situ salinity. Satellite salinity is dependent on various marine environmental factors such as season, sea surface temperature (SST), and wind speed. In summer, the difference between the satellite salinity and the in-situ salinity was less than 0.18 psu. This means that the accuracy of satellite salinity increases at high SST rather than at low SST. This accuracy was affected by the sensitivity of the sensor. Likewise, the error was reduced at wind speeds greater than 5 m s^-1. This study suggests that satellite-derived salinity data should be used in coastal areas for limited use by checking if they are suitable for specific research purposes.

• Journal of Korea Water Resources Association
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• v.44 no.1
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• pp.23-30
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• 2011

Any significant inflow of fresh water due to flood or snow melting can rapidly reduce salinity at the bay. In such a case, the habitat environment in the sea near river mouth can be partly destroyed. Therefore, research to understand the salinity distribution quantitatively at the bay for the utilization of natural environment and for the inhabitant conservation must be very important. In this study, the investigation on the relationship between satellite image and turbidity is carried out first, and then the salinity distribution at the bay using the relationship between turbidity and salinity is derived. The main results are as follows. First the reappearance ability of RGB bands respectively of the satellite image is investigated, and then it was confirmed that the combination of band2 and band3 expressed best the movement characteristics of turbid water at the bay is chopped up into 4 small areas. Second the turbidity of river mouth is estimated using the travel time from the upward monitoring station to the river mouth. Finally the satellite image is converted into the salinity distribution by the correlation of salinity and turbidity. It is confirmed that the salinity distributions obtained from above three investigation methods are quite reasonable and clear.

• Atmosphere
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• v.31 no.2
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• pp.229-240
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• 2021

The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1307-1315
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• 2021

The Changjiang Diluted Water (CDW) spreads over the East China Sea every summer and significantly affects the sea surface salinity changes in the seas around Jeju Island and the southern coast of Korea peninsula. Sometimes its effect extends to the eastern coast of Korea peninsula through the Korea Strait. Specifically, the CDW has a significant impact on marine physics and ecology and causes damage to fisheries and aquaculture. However, due to the limited field surveys, continuous observation of the CDW in the East China Sea is practically difficult. Many studies have been conducted using satellite measurements to monitor CDW distribution in near-real time. In this study, an algorithm for estimating Sea Surface Salinity (SSS) in the East China Sea was developed using the Geostationary Ocean Color Imager (GOCI). The Multilayer Perceptron Neural Network (MPNN) method was employed for developing an algorithm, and Soil Moisture Active Passive (SMAP) SSS data was selected for the output. In the previous study, an algorithm for estimating SSS using GOCI was trained by 2016 observation data. By comparison, the train data period was extended from 2015 to 2020 to improve the algorithm performance. The validation results with the National Institute of Fisheries Science (NIFS) serial oceanographic observation data from 2011 to 2019 show 0.61 of coefficient of determination (R²) and 1.08 psu of Root Mean Square Errors (RMSE). This study was carried out to develop an algorithm for monitoring the surface salinity of the East China Sea using GOCI and is expected to contribute to the development of the algorithm for estimating SSS by using GOCI-II.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.621-629
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• 2019

A logical measure is suggested to estimate an accurate Sound Speed Profile (SSP) for the unusual variation of salinity in the Yellow Sea. Based on National Aeronautics and Space Administration (NASA)'s Aqua and Soil Moisture Active Passive (SMAP) satellite data, this measure identifies the area of temperature inversion effect and expansion of low salinity (<30.5 psu) water. Subsequently, on the area, the Conductivity, Temperature, and Depth (CTD) mounted unmanned maritime system estimates accurate SSP. In order to carry out this measure conveniently, a flow chart is demonstrated in this research. By using this measure which finds the high variational salinity area, the inaccuracy issue for calculating SSP from Expandable Bathy Thermograph (XBT) is expected to be solved.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Journal of the Korean earth science society
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• v.33 no.6
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• pp.554-566
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• 2012

High-density temperature and salinity profiles from the successful international Argo project made it possible to reproduce the three-dimensional global ocean state in near-real time, which also increased much attention on the data analysis studies of global ocean. This paper reviewed several important issues on the recent data analysis studies such as systematic biases of XBT (eXpendable BathyThermograph) and Argo data, sea level budget discrepancy between steric height and satellite observed data, heat content change, and the current status of the development of objective analysis fields. This study also emphasized that it is required to carry out very cautious ocean data quality control and understand global-scale ocean variability prior to analyzing the regional-scale ocean climate change, particularly, in the East Asian marginal Seas.

• Proceedings of KOSOMES biannual meeting
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• 2006.05a
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• pp.137-140
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• 2006

황해와 동중국해의 해황 변동에 관한 연구는 현장관측을 중심으로 체계적으로 많이 수행되어 왔지만, 인공위성자료를 이용한 황해와 동중국해의 해황 변동 연구는 미비한 실정이다. 이것은 인공위성자료를 통해 얻을 수 있는 관측항목이 표층수온자료에 국한되어 있었기 때문이다. 그러나 SeaWiFS 해색위성과 같은 인공위성자료들을 이용하여 부유물 농도, 엽록소 농도 등이 원활하게 생산되고 있으며 최근 연구결과에 의해 염분과 유향성분 등도 추정 및 추출이 가능케 되었으므로 이들 인공위성자료를 이용한 황해와 동 중국해의 해황 변동에 관한 연구를 수행하게 되었다. 특히 2004 년도는 계절변동에 있어서 이상기후의 해라고 점철되고 있다. 2004년 봄철의 폭설과 일시적인 고온현상, 여름철에는 10 년만의 무더위, 겨울철에는 36년만에 가장 포근한 날씨가 지속되었다. 이러한 이상기후의 발생은 해양과 대기의 상호작용에 의해서 기인했을 것이라고 생각되어 한반도 주변 해역에서 황해와 동중국해의 해황변동이 연안 해역의 해황변동과 어떠한 연관성이 존재하고, 이러한 요인들은 2004년도에 발생한 이상기후와 어떤 상관관계를 갖는지 연구하기 위한 기초연구를 진행하였다. 2003년 12월 - 2004년 2월과 2004년 12월 - 2005년 2월에 동일한 시기에 관측된 NOAA 표층수온 분포 영상 자료들을 황해와 동중국해 해역을 중심으로 월별로 비교해보면 2003년 12월 2004년 1월에 관측된 표층수온 분포값보다 2004녀 12월 - 2005년 1월에 관측된 표층수온 분포값이 상대적으로 높은 분포 특성을 나타내고 있었다. 이와 같은 현상은 국립수산과학원의 2004년 10월과 12월의 정선관측자료에서도 나타나고 있었다. 그러나 이와는 반대로 2004년 2월에 관측된 표층수온 분포값보다 2005년 2월에 관측된 표층수온 분포값이 상대적으로 낮은 분포 특성을 나타내고 있었다. 따라서 인공위성자료를 이용한 황해의 2004년 해황 분석 결과는 이상수온 상승의 원인이 쿠로시오 해류의 변동과 관련성이 높다고 판단되며 이에 대한 지속적인 연구가 현재 진행중에 있다.

• 한국해양학회지
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• v.19 no.1
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• pp.56-67
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• 1984

Mesoscale thermohaline structures of the meandering front in the Korea Strait during November 1976, 1980 and 1982 are studied by means of closely spaced oceanographic observations and the satellite infrared images. Strong thermal and salinity fronts coincide each other and show a wavelike meander motion with wavelengths of 40-60km and amplitudes of, 15-20km. Salinity minimum band of less than 33.0 is found along the onshore edge of the front. Width of the frontal zone corresponds approximately to the internal radius of deformation (R=5-10km) and the slope of density interface is confined to about 2R. A series of satellite infrared images with the interval of 4-5 days show a noticeable growth of frontal meander over the flat shelf west of the Korea Strait. Possible mechanisms of frontal meander and its growth are discussed.