• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.589-593
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• 2013

Sea Surface Temperature (SST) is the temperature close to the ocean's surface and affects the Earth's atmosphere as an important parameter for the climate circulation and change. The SST from satellite still has biases from the error in specifying retrieval coefficients from either forward modeling or instrumental biases. So in this paper, we performed sensitivity analysis using input parameter of the SST to notice that the SST is most affected among the input parameter. We used Infrared (IR) data from the Communication, Ocean, and Meteorological Satellite (COMS)/Meteorological Imager (MI) from April 2011 to March 2012. We also used the Global Space-based Inter-Calibration System (GSICS) correction to quality of the IR data from COMS. SST was calculated by substituting the input parameters; IR data with or without the GSICS correction. The results of this sensitivity analysis, the SST was sensitive from -0.0403 to 0.2743 K when the IR data were changed by the GSICS corrections.

• 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.

• 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.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.953-968
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• 2018

In order to understand the change of surface water temperature in the East China Sea (ECS), this study analyzed the relationship between sea surface temperature (SST), air temperature (AT) and heat flux using satellite and model reanalysis data from 2003 to 2017. SST in the ECS showed the lowest (average : $13.72^{\circ}C$) in March and the highest (average : $28.12^{\circ}C$) in August. AT is highly correlated with SST and shows a similar seasonal change. In August, SST is higher than AT and then continuously higher than AT until winter. To analyze the change of the summer SST in the ECS, we used the SST anomaly value in August to classify the periods with positive (04', 06', 07', 13', 16', 17') and negative (03', 05', 08', 09', 10', 11', 12', 14', 15') values. Spatial similarity between the two periods indicates that SSTs are relatively larger variations in the northern part than in the southern part, and in the western part than in the eastern part in the study area. AT and net heat flux values also show similar changes with SST. However, the periods of the positive SST anomaly have the relatively increasing SST, AT and heat flux values compared to the periods of the negative SST anomaly in the summer season of the ECS. Although the change of SST in the summer season generally well correlates with AT, there were the periods when it was different from general trends between SST and AT (10', 12', 15', 16'). SST in August 2010 and 2012 decreased by $0.5^{\circ}C$ from AT. It suggests that the decreasing SST was considered to be caused by the effects of the typhoon passing through the study area. In August 2015, AT was relatively lower than SST (> $0.5^{\circ}C$), which is might be weakening of the East Asian Summer Monsoon. In August 2016, SST and AT show the highest values during the whole study periods, but SST is higher than AT (> $1^{\circ}C$). From satellite and heat flux data, the variations of SST have been shown to be relatively higher in the area of the expansion Changjiang Diluted Water (CDW) originated from the China coast. More research is needed to analyze this phenomenon, it is believed as not only the effect of rising AT but also the expansion of the low-salinity water.

• 한국해양학회지
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• v.19 no.1
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• pp.31-35
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• 1984

The seasonal variation of sea surface temperatures (SST) in the neighbouring seas of Korea was studied performing the harmonic analysis of the monthly mean SST data of 15 years (1961-1975) at 182 stations routinely collected by the Fisheries Research and Development Agency. The mean SST in the West Sea (Yellow Sea) is lower than that in the East Sea (Sea of Japan) whereas the annual range of SST in the West Sea is much larger than that in the East Sea. The maximum SST occurs between mid August and early September. The seasonal variation of SST in the seas of Korea is influenced by incoming radiation and heat advections by ocean currents and winds.

• Journal of Environmental Science International
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• v.10 no.2
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• pp.167-171
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• 2001

The magnitudes of sea surface temperature (SST) anomalies at 13 coastal stations along the Korean peninsula in the summer and winter for the past 29years (1969-1997) are more larger than those in the spring and autumn. The periods of positive SST anomalies (negative SST anomalies) longer than 1$^{\circ}C$ were 75(74.5) months in the eastern coast of Korea, 47.8(51.6) months in the southern coast of Korea and 69.5(69.8) months in the western coast of Korea during the past 348 months (1969-1997). The predominant periods of the low-pass filtered monthly SST anomalies are 3 years or 13 months, even another predominant period is 24 months. The spatial variation of SST anomalies were confined by regional seas of the Korean peninsula, such as the East Sea, the South Sea and the West Sea itself.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.555-567
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• 2018

In order to identify the characteristics of sea surface temperature (SST) differences between microwave SST from GCOM-W1/AMSR2 and in-situ measurements in the western coast of Korea, a total of 6,457 collocated matchup data were produced using the in-situ temperature measurements from marine buoy stations (Deokjeokdo, Chilbaldo, and Oeyeondo) from July 2012 to December 2017. The accuracy of satellite microwave SSTs was presented by comparing the ocean buoy data of Deokjeokdo, Chilbaldo, and Oeyeondo stations with the AMSR2 SST data more than five years. The SST differences between the microwave SST and the in-situ temperature measurements showed some dependence on environmental factors, such as wind speed and water temperature. The AMSR2 SSTs were tended to be higher than the in-situ temperature measurements during the daytime when the wind speed was low ($<6ms^{-1}$). On the other hand, they showed positive deviation increasingly as the wind speed increased for nighttime. In addition, increasing tendency of SST differences was related to decreasing sensitivity of microwave sensors at low temperatures and data contamination by land. A monthly analysis of the SST difference showed that unlike the previous trend, which was known to be the largest in winter when strong winds were blowing, the SST difference was largest in summer in Deokjeokdo and Chilbaldo buoy stations. This seemed to be induced by differential tidal mixing at the collocated matchup points. This study presented problems and limitations of the use of microwave SSTs with high contribution to the SST composites in the western coastal region off the Korean peninsula.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.8
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• pp.1-7
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• 2003

The numerical simulation of flow-filed around a square cylinder near a wall with $\varepsilon$-SST turbulence model is carried out in this study. The newly suggested $\varepsilon$-SST turbulence model that modifies the original SST turbulence model is proved to yield more accurate results than the other 2-equation turbulence models in large separation region around a bluff body. Therefore, $\varepsilon$-SST turbulence model can be effectively applied for predicting the flow-fields with large separation. And it is found that vortex shedding is suppressed below the critical gap height, the Strouhal number is affected by the gap height and the wall boundary layer thickness.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.1
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• pp.18-25
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• 2013

Assessment and validation of RANS turbulence models are conducted for the optimal analysis of supersonic converging-diverging nozzle through the comparison between computational results and experimental data. One/two equation turbulence closures such as Spalart-Allmaras, RNG k-${\varepsilon}$, and k-${\omega}$ SST are employed to simulate the two-dimensional nozzle flow. Computational results with the turbulence models mentioned fairly well predict shock structure of the nozzle-inside and pressure distribution along the wall. Especially, SST model among the employed ones shows the best agreement to experimental results.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.39-44
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• 2014

The results of the synoptic meteorological analysis showed that when the cold and dry continental high pressure was extended, heavy snow occurred at dawn when the upper atmosphere cooled. In particular, when the continental high pressure was extended and the upper pressure trough passed through, heavy snow occurred due to the convergence region formed in the west coast area, sometimes in the inland of the Honam area. In addition, it was verified that the changes in the humidity coefficients in the upper and lower layers are important data for the determination of the probability, start/end and intensity of heavy snow. However, when the area was influenced by the middle-latitude low pressure, the heavy snow was influenced by the wind in the lower layer (925 hPa and 850 hPa), the equivalent potential temperature, the convergence field, the moisture convergence and the topography. In Case 2010 (30 December 2010), OSTIA had the best numerical simulation with diverse atmospheric conditions, and the maximum difference in the numerically simulated snowfall between NCEP/NCAR SST and OSTIA was 20 cm. Although there was a regional difference in the snowfall according to the difference in the SST, OSTIA and RTG SST numerical tests, it was not as significant as in the previous results. A higher SST led to the numerical simulation of larger snowfall, and the difference was greatest near Buan in the west coast area.