• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1620-1624
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• 2010

The NLCCA has been applied to analyze the East Asia sea surface temperature (SST) and Korea monthly precipitation, where the eight leading PCs of the SST and the eight PCs of the precipitation during 1973-2007 were inputs to an NLCCA model. The first NLCCA mode is plotted in the PC spaces of the Korea precipitation and the world SST present a curve linking the nonlinear relationship between the first three leading PCs of Korea precipitation and world SST forthright. The correlation coefficient between canonical variate time series u and v is 0.8538 for the first NLCCA mode. And there are some areas' climate variability have higher relationship with Korea precipitation, especially focus on the north of East Sea' climate variability have represented the higher canonical correlation with Korea precipitation, with the correlation coefficient is 0.871 and 0.838. Likewise in Korea, most stations display similarly uniform distributing characteristic and less difference, in particular the inshore stations have display identical distributing characteristic. In correlation variables' scores, the fluctuation and variation trend are also seasonal oscillation with high frequency.

• 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.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.2
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• pp.333-345
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• 2012

NOAA/AVHRR, Topex/Poseidon, and Jason-1 data were used to analyze sea surface temperatures and thermal fronts in the North East Asia Seas. Temporal and spatial analyses were based on data from 1993 to 2008. The amplitude and phase for the annual mode on SL and SST were investigated with harmonic analysis. The geographical distribution of amplitudes for comparison of SL and SST are slightly reverse in southwest-northeast tilted direction. The time series analysis conducted on the entire researched area presented consistent pattern. Peak of Sea Level was presented 1~2 months after the peak of the surface sea temperature was shown. This explains that Sea Level change occurs after the generation of surface sea temperature change in sea. The Sobel edge detection method delineated four fronts. Thermal fronts generally occurred over steep bathymetric slopes. Annual amplitudes and phases were bounded within these frontal areas.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.579-581
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• 2003

Recently, the environment of the Ariake Sea, Japan has changed drastically. In this study, the result of sea survey, synchronizing the passage time of the Landsat in August 2002 was collated with the satellite data to develop the evaluating equation for transparency and sea surface temperature (SST). By Applying these equations to 5 satellite images of the same season, the transparency and SST in summer of 1985, 1991, 1996 and 2000 is estimated. Consequently, the transparency had increased until 2000 and then decreased in 2002. The SST, on the other hand, shows no remarkable trend.

• Ocean and Polar Research
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• v.41 no.4
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• pp.213-232
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• 2019

This study evaluates the accuracy of four satellite-composite (OSTIA, AVHRR, G1SST, FNMONC-S) and three model-reanalysis (HYCOM, JCOPE2, FNMOC-M) daily sea surface temperature (SST) data around the Korean Peninsula (KP) using ocean buoy data from 2011-2016. The results reveal that OSTIA has the lowest root mean square error (RMSE; 0.68℃) and FNMOC-S/M has the highest correction coefficients (r = 0.993) compared with observations, while G1SST, JCOPE2, and AVHRR have relatively larger RMSEs and smaller correlations. The large RMSEs were found in the western coastal regions of the KP where water depth is shallow and tides are strong, such as Chilbaldo and Deokjeokdo, while low RMSEs were found in the East Sea and open oceans where water depth is relatively deep such as Donghae, Ulleungdo, and Marado. We found that the main sources of the large RMSEs, sometimes reaching up to 5℃, in SST data around the KP, can be attributed to rapid SST changes during events of strong tidal mixing, upwelling, and typhoon-induced mixing. The errors in the background SST fields which are used in data assimilations and satellite composites and the missing in-situ observations are also potential sources of large SST errors. These results suggest that both satellite and reanalysis SST data, which are believed to be true observation-based data, sometimes, can have significant inherent errors in specific regions around the KP and thus the use of such SST products should proceed with caution particularly when the aforementioned events occur.

• Proceedings of the KSRS Conference
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• v.2
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• pp.880-883
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• 2006

Altimeter(Topex/Poseidon) and AVHRR(NOAA) data were used to study the variations and correlations of Sea Level(SL) and Sea Surface Temperature (SST) in the North East Asian Seas from November 1993 to May 1998. This region is influenced simultaneously to continental and oceanic climate as the border of the East Sea(Japan Sea). SL and SST have increased gradually every year because the global warming, and presented usually a strong annual variations in Kuroshio extension region with the influence of bottom topography.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.59-63
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• 2007

Altimeter (Topex/Poseidon) and AVHRR (NOAA) data were used to study the variations and correlations of Sea Level (SL) and Sea Surface Temperature (SST) in the North East Asian Seas from November 1993 to May 1998. This region is influenced simultaneously to continental and oceanic climate as the border of the East Sea (Japan Sea). SL and SST have increased gradually every year because the global warming, and presented usually a strong annual variations in Kuroshio extension region with the influence of bottom topography.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.107-112
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• 2011

Recently, climate change around the world due to global warming has became an important issue and damages by climate change have a bad effect on human life. Changes of Sea Surface Temperature(SST) is associated with natural disaster such as Typhoon and El Nino. So we predicted daily future SST using Statistical Downscaling Method and CGCM 3.1 A1B scenario. 9 points of around Korea peninsular were selected to predict future SST and built up a regression model using Multiple Linear Regression. CGCM 3.1 was simulated with regression model, and that comparing Probability Density Function, Box-Plot, and statistical data to evaluate suitability of regression models, it was validated that regression models were built up properly.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.273-287
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• 2008

Characteristics of skin-bulk sea surface temperature (SST) differences in the Northeast Asia seas were analyzed by utilizing 845 collocated matchup data between NOAA/AVHRR data and oceanic in-situ temperature measurements for selected months from 1994 to 2003. In order to understand diurnal variation of SST within a few meters of the upper ocean, the matchup database were classified into four categories according to day-night and drifter-shipboard measurements. Temperature measurements from daytime drifters showed a good agreement with satellite MCSST (Multi-Channel Sea Surface Temperature) with an RMS error of about $0.56^{\circ}C$. Poor accuracy of SST with an rrns error of $1.12^{\circ}C$ was found in the case of daytime shipboard CTD (Conductivity, Temperature, Depth) measurements. SST differences between MCSST and in-situ measurements are caused by various errors coming from atmospheric moist effect, coastal effect, and others. Most of the remarkable errors were resulted from the diurnal variation of vertical temperature structure within a few meters as well as in-situ oceanic temperatures at different depth, about 20 cm for a satellite-tracked drifting buoy and a few meters for shipboard CTD or moored buoy. This study suggests that satellite-derived SST shows significant errors of about ${\pm}3^{\circ}C$ in some cases and therefore it should be carefully used for one's purpose on the base of in-depth understanding of skin-bulk SST difference and vertical temperature structure in regional sea.

• Ocean and Polar Research
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• v.34 no.2
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• pp.201-218
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• 2012

The purpose of this study is to investigate climatological variations from the sea surface temperature (SST), chlorophyll-a concentration (Chl-a), and phytoplankton size class (PSC), using NOAA AVHRR, SeaWiFS, and MODIS data in the South Sea of Korea (SSK) and East China Sea (ECS). 26-year monthly SST and 13-year monthly Chl-a and PSC data, separated by whole and nine-different areas, were used to understand seasonal and inter-annual variations. SST and Chl-a clearly showed seasonal variations: higher SST and Chl-a were observed during the summer and spring, and lower values occurred during the winter and summer. The annual and monthly SST over 26 years increased by $0.2{\sim}1.0^{\circ}C$. The annual and monthly Chl-a concentration over 13 years decreased by $0.2{\sim}1.1mg/m^3$. To determine more detailed spatial and temporal variations, we used the combined data with monthly SST, Chl-a, and PSC. Between 1998 and 2010, the inter-annual trend of Chl-a decreased, with decreasing micro- and nano-size plankton, and increasing pico-size plankton. In regional analysis, the west region of the study area was spatially and temporally correlated with the area dominated by decreasing micro-size plankton; while the east region was less sensitive to coastal and land effects, and was dominated by increasing pico-size plankton. This phenomenon is better related to one or more forcing factors: the increased stratification of ocean driven by changes occurring in spatial variations of the SST caused limited contributions of nutrients and changed marine ecosystems in the study area.