• Journal of the Korean earth science society
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• v.41 no.4
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• pp.356-366
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• 2020

The accuracy of satellite-observed sea surface salinity (SSS) was evaluated in comparison with in-situ salinity measurements from ARGO floats and buoys in the seas around the Korean Peninsula, the northwest Pacific, and the global ocean. Differences in satellite SSS and in-situ measurements (SSS errors) indicated characteristic dependences on geolocation, sea surface temperature (SST), and other oceanic and atmospheric conditions. Overall, the root-mean-square (rms) errors of non-averaged SMOS SSSs ranged from approximately 0.8-1.08 psu for each in-situ salinity dataset consisting of ARGO measurements and non-ARGO data from CTD and buoy measurements in both local seas and the ocean. All SMOS SSSs exhibited characteristic negative bias errors at a range of -0.50- -0.10 psu in the global ocean and the northwest Pacific, respectively. Both rms and bias errors increased to 1.07 psu and -0.17 psu, respectively, in the East Sea. An analysis of the SSS errors indicated dependence on the latitude, SST, and wind speed. The differences of SMOS-derived SSSs from in-situ salinity data tended to be amplified at high latitudes (40-60°N) and high sea water salinity. Wind speeds contributed to the underestimation of SMOS salinity with negative bias compared with in-situ salinity measurements. Continuous and extensive validation of satellite-observed salinity in the local seas around Korea should be further investigated for proper use.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• 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.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.327-337
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• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

• Fisheries and Aquatic Sciences
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• v.10 no.3
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• pp.133-142
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• 2007

To investigate conditions for the survival and growth of Dungeness crab zoeae in situ, spatial and monthly changes of sea surface temperature (SST), sea surface salinity (SSS), Chlorophyll ${\alpha}$ (Chl ${\alpha}$), and zooplankton biomass were measured in four transects: upper Chatham, Icy Strait, Cross Sound, and Icy Point in southeastern Alaska from May to September, 1997-2004. Monthly mean SST was coldest in May, increased throughout the summer months, and decreased in September. SST was coldest in the Cross Sound transect, intermediate in the upper Chatham and Icy Strait transects, and warmest in the Icy Point transect. SSS of northern stations in the upper Chatham and Icy Strait transects decreased throughout the summer months and increased in September, while that of other transects did not vary. Monthly mean Chl ${\alpha}$ was highest in May and decreased thereafter. Chl ${\alpha}$ in the upper Chatham and Icy Strait transects were relatively higher from May through September than those in the Cross Sound and Icy Point transects. Mean zooplankton biomass was highest in the Icy Strait transect in May and lowest in the Icy Point transect in September. This research suggests that oceanographic conditions during the season of Dungeness crab zoeae in southeastern Alaska may not constrain the survival and growth of Dungeness crab zoeae.

• Journal of the Korean earth science society
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• v.26 no.7
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• pp.714-724
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• 2005

The East Sea, a semi-enclosed marginal sea with shallow straits in the northwest Pacific, is marked by the nearly geographic isolation and the low sea surface salinity during the last glacial maximum (LGM). The East Sea might have the only connection to the open ocean through the Korea Strait with a sill depth of 130 m, allowing the paleo-Tsushima Water to enter the sea during the LGM. The low paleosalinity associated with abnormally light $\delta^{18}O$ values of planktonic foraminifera is interpreted to have resulted from river discharge and precipitation. Nevertheless, two LGM features in the East Sea are disputable. This study attempts to estimate volume transport of the paleo-Tsushima Water via the Korea Strait and further examines its effect on the low sea surface salinity (SSS) during the lowest sea level of the LGM. The East Sea was not completely isolated, but partially linked to the northern East China Sea through the Korea Strait during the LGM. The volume transport of the paleo-Tsushima Water during the LGM is calculated approximately$(0.5\~2.1)\times10^{12}m^3/yr$ on the basis of the selected seismic reflection profiles along with bathymetry and current data. The annual influx of the paleo-Tsushima Water is low, compared to the 100 m-thick surface water volume $(about\;79.75\times10^{12}m^3)$ in the East Sea. The paleo-Tsushima Water influx might have changed the surface water properties within a geologically short time, potentially decreasing sea surface salinity. However, the effect of volume transport on the low sea surface salinity essentially depends on freshwater amounts within the paleo-Tsushima Water and excessive evaporation during the glacial lowstands of sea level. Even though the paleo-Tsushima Water is assumed to have been entirely freshwater at that time period, it would annually reduce only about 1‰ of salinity in the surface water of the East Sea. Thus, the paleo-Tsushima Water influx itself might not be large enough to significantly reduce the paleosalinity of about 100 m-thick surface layer during the LGM. This further suggests contribution of additional river discharges from nearby fluvial systems (e.g. the Amur River) to freshen the surface water.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• Ocean and Polar Research
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• v.38 no.4
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• pp.259-270
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• 2016

Sea Surface Salinity (SSS) observations from the Aquarius satellite in the East Sea show large systematic biases mainly caused by the surrounding lands and Radio Frequency Interferences (RFI) along the descending orbits on which the satellite travels from the Asian continent to the East Sea. To develop a technique for correcting the systematic biases unique to the East Sea, the least square regression between in situ observations of salinity and the reanalyzed salinities by HYCOM is first performed. Then monthly mean reanalyzed salinities fitted to the in situ salinities are compared with monthly mean Aquarius salinities to calculate mean biases in $1^{\circ}{\times}1^{\circ}$ boxes. Mean biases in winter (December-March) are found to be considerably larger than those in other seasons possibly caused by the inadequate correction of surface roughness in the sea surrounded by the land, and thus the mean bias corrections are performed using two bias tables. Large negative biases are found in the area near the coast of Japan and in the areas with islands. In the northern East Sea, data sets using the ascending orbit only (SCIA) are chosen for correction because of large RFI errors on the descending orbit (SCID). Resulting mean biases between the reanalysis salinities fitted to in situ observations and the bias corrected Aquarius salinities are less than 0.2 psu in all areas. The corrected mean salinity distributions in March and September demonstrate marked improvements when compared with mean salinities from the World Ocean Atlas (WOA [2005-2012]). In September, salinity distributions based on the corrected Aquarius and on the WOA (2005-2012) show similar distributions of Changjiang Diluted Water (CDW) in the East Sea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.12 no.1 s.24
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• pp.23-32
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• 2006

This study was conducted to find out the effects of meterological factors on oceanic conditions when cold and dry continental air mass passes through the western sea of Korea The change of ocean conditions during the winter season were more obvious in coastal area than open sea And sea surface temperature (SST) during February is lower by $3^{\circ}C$ than December but in coastal area SST dropped by $3^{\circ}C$. As for the salinity, there was not much difference between areas except southern area of Mokpo. In the coastal regions, air temperature(AT) and SST showed a positive correlation; as the air temperature goes up with the increase of SST and when the former goes down the latter decrease. SST of open sea seems to be changed by latent (Qe) and sensible heat (Qs), when the open sea lose its heat by Qe and Qs then SST goes down And when they get the heat then the SST goes up. 1here was a positive correlation between the AT of the coastal region and sea surface salinity (SSS), when the AT goes up then SSS increase and when the former goes down the latter decrease. Precipitation during the summer seasons (June$\sim$September) appeared to the more closely related with salinity of February of the following year than those of October and December.

• Ocean and Polar Research
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• v.24 no.2
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• pp.135-146
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• 2002

The upper ocean in the western equatorial Pacific warm pool during TOGA-COARE IMET IOP was simulated using a one-dimensional turbulence closure ocean mixed-layer model, which considered recent observations, such as the remarkable enhancement of turbulent kinetic energy near the ocean surface. The shoaling/deepening of the mixed layer and warming/cooling subsurface water in the model were in reasonable agreement with the observations. There was a significant improvement in simulating the cooling trend of the sea surface temperature under a westerly wind burst with heavy rainfall over previous simulations using bulk mixed-layer models. By contrast the simulated sea surface salinity (SSS) departed significantly from the observed SSS, especially during a westerly burst and the subsequent restratification period, which might be due to 3-D control processes, such as downwelling/upwelling or advection.