• Proceedings of the KSRS Conference
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• v.2
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• pp.831-835
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• 2006

NOAA/AVHRR data were used to analyze sea surface temperatures (SSTs) and thermal fronts (TFs) in the Korean seas. Temporal and spatial analyses were based on data from 1993 to 2000. Harmonic analysis revealed mean SST distributions of $10{\sim}25^{\circ}C$. Annual amplitudes and phases were $4{\sim}11^{\circ}C$ and $210{\sim}240^{\circ}$, respectively. Inverse distributions of annual amplitudes and phases were found for the study seas, with the exception of the East China Sea, which is affected by the Kuroshio Current. Areas with high amplitudes (large variations in SSTs) showed 'low phases' (early maximum SST); areas with low amplitudes (small variations in SSTs) had 'high phases' (late maximum SST). Empirical orthogonal function (EOF) analyses of SSTs revealed a first-mode variance of 97.6%. Annually, greater SST variations occurred closer to the continent. Temporal components of the second mode showed higher values in 1993, 1994, and 1995. These phenomena seemed to the effect of El $Ni{\tilde{n}}o$. The Sobel edge detection method (SEDM) delineated four fronts: the Subpolar Front (SPF) separating the northern and southern parts of the East Sea; the Kuroshio Front (KF) in the East China Sea, the South Sea Coastal Front (SSCF) in the South Sea, and a tidal front (TDF) in the West Sea. Thermal fronts generally occurred over steep bathymetric slopes. Annual amplitudes and phases were bounded within these frontal areas. EOF analysis of SST gradient values revealed the temporal and spatial variations in the TFs. The SPF and SSCF were most intense in March and October; the KF was most significant in March and May.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.213-219
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• 2005

In the Korean seas, Sea Surface Temperature (SST) and Thermal ronts (TF) were analyzed temporally and spatially during 8 years from 1993 to 2000 using NOAA/AVHRR MCSST. In the application of EOF analysis for SST, the variance of the 1st mode was 97.6%. Temporal components showed annual variations, and spatial components showed that where it is closer to continents, the SST variations are higher. Temporal components of the 2nd mode presented higher values of 1993, 94 and 95 than those of other years. Although these phenomena were not remarkable, they could be considered ELNI . NO effects to the Korean seas as the time was when ELNI . NO occurred. The Sobel Edge Detection Method (SEDM) delineated four fronts: the Subpolar Front (SPF) separating the northern and southern parts of the East Sea; the Kuroshio Front (KF) in the East China Sea, the South Sea Coastal Front (SSCF) in the South Sea, and the Tidal Front (TDF) in the West Sea. TF generally occurred over steep bathymetry slopes, and spatial components of the 1st mode in SST were bounded within these frontal areas. EOF analysis of SST gradient values revealed the temporal and spatial variations of the TF. The SPF and SSCF were most intense in March and October; the KF was most significant in March and May.

• 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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• 2004.10a
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• pp.696-700
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• 2004

In the Korean seas, Sea Surface Temperature (SST) and Thermal Fronts (TF) were analyzed temporally and spatially during 8 years from 1993 to 2000 using NOAA/AVHRR MCSST. As the result of harmonic analysis, distributions of the mean SST were $10~25^{\circ}C,$ and generally SST decreased as latitude increased. SST increased in the order as following; the South Sea $(20\~23^{\circ}C),$ the East Sea $(17\~19^{\circ}C)$, and the West $Sea(13\~16^{\circ}C).$ Annual amplitudes and phases were $4\~11^{\circ}C,\;210\~240^{\circ}$ and high values were shown as following; the West Sea $(A1,\;9\~11^{\circ}C),$ the Northern East Sea $(A5,\;8\~9^{\circ}C),$ the Southern East Sea $(A4,\;6\~8^{\circ}C),$ the South Sea $(A3,\;6\~7^{\circ}C),$ the East China Sea $(A2,\;4\~7^{\circ}C)$ and phases; $A3\;(238\~242^{\circ}),\;A4\;(235\~240^{\circ}),\;A5\;(225\~235^{\circ}),\;Al\;(220\~230^{\circ}),\;A2\;(210\~235^{\circ}),$ respectively, Both of them were related inversely except the area A2, therefore the rest areas were affected by seasonal variations. TF were detected by Soble Edge Detection Method using gradient of SST. Consequently, TF were divided into 4 fronts; the Subpolar Front (SPF) based on the Cold Water Mass (low SST and salinity Subartic Water), resulting from the North Korea Cold Current (NKCC) and the East Sea Proper Cold Water in the middle and low layer, and the Warm Water Mass (high SST and salinity Subtropical Water), resulting from the Tsushima Warm Current (TWC) in area A4 and 5, the Kuroshio Front (KF) based on the Kuroshio Current (KC) and shelf waters in the East China Sea (ESC) in A2, and the South Sea Coastal Front (SSCF) based on the South Sea Coastal Water (SSCW) and TWC in A3. Also, the Tidal Front was weakly appeared in AI. TF located in steep slope of submarine topography. Annual amplitudes and phases were bounded in the same place, and these results should be considered to influence of seasonal variations.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.32 no.5
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• pp.618-623
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• 1999

A relationship between SST (Sea Surface Temperature) fronts and formation of fishing grounds was examined using the data on fishing conditions obtained from 41 Korean purse-seiners during the period of 1991 to 1996. Good fishing grounds observed in the southern sea of Korea and the nothern area of the East China Sea were yearly found around the frontal zone and around the marginal area of Tsushima Current which was the periphery of fronts, Also, there were several fishing grounds, which are not related to the fronts. They can be classified into the following four types : The first type was found in the warm water pocket located in the western area of Cheju Island in winter. The second type was made in a intensive bending of isobathytherm with a higher temperature in the main stream of Tsushima Current between Cheju Island and the Goto Islands in winter. The third type was formed by the topographical vortex motion near the Tsushima Island in winter and spring. The fourth type was found at the area of the reflow Sea Warm Current in southwest sea of Korea between the costal front zone and the Yellow Bottom Cold Waters in summer and autumn.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.101-104
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• 2006

In the Korean seas, Sea Surface Temperature (SST) and Thermal Fronts (TF) were analyzed temporally and spatially during 8 years from 1993 to 2000 using NOAA/AVHRR MCSST. As the result of EOF method applying SST, the variance of the 1st mode was 97.6%. It is suitable to explain SST conditions in the whole Korean seas. Time coefficients were shown annual variations and spatial distributions were shown the closer to the continent the higher SST variations like as annual amplitudes. The 2nd mode presented higher time coefficients of 1993, 94, and 95 than those of other years. Although the influence is a little, that can explain ElNINO effect to the Korean seas. TF were detected by Sobel Edge Detection Method using gradient of SST. Consequently, TF were divided into 4 fronts; the Subpola. Front (SPF) dividing into the north and south part of the East sea, the Kuroshio Front (KF) in the East China Sea (ESC), the South Sea Coastal Front (SSCF) in the South sea, and the Tidal Front in the West sea. TF located in steep slope of submarine topography. The distributions of 1st mode in SST were bounded in the same place, and these results should be considered to influence of seasonal variations. To discover temporal and spatial variations of TF,SST gradient values were analyzed by EOF. The time coefficients fo the 1st mode (variance : 64.55%) showed distinctive annual variations and SPF, KF, and SSCF was significantly appeared in March. the spatial distributions of the 2nd mode showed contrast distribution, as SPF and SSCF had strong '-' value, where KF had strong '+' value. The time of '+' and '-' value was May and October, respectively. Time coefficients of the 3rd mode had 2 peaks per year and showed definite seasonal variations. SPF represented striking '+' value which time was March and October That was result reflected time of the 1st and 2nd mode. We can suggest specific temporal and spatial variations of TF using EOF.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.397-402
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• 2005

In the Korean seas, Sea Surface Temperature (SST) and Thermal Fronts (TF) were analyzed temporally and spatially during 8 years from 1993 to 2000 using NOAA/AVHRR MCSST As the result of EOF method applying SST, the variance of the 1st mode was 97.6%. It is suitable to explain SST conditions in the whole Korean seas. Time coefficients were shown annual variations and spatial distributions were shown the closer to the continent the higher SST variations like as annual amplitudes. The 2nd mode presented higher time coefficients of 1993, 94, and 95 than those of other years. Although the influence is a little, that tan explain EININO effort to the Korean seas. TF were detected by Sobel Edge Detection Method using gradient of SST. Consequently, TF were divided into 4 fronts; the Subpolar Front (SPF) dividing into the north and south part of the East sea , the Kuroshio Front (KF) in the East China Sea (ESC), the South Sea Coastal Front (SSCF) in the South sea, and the Tidal Front in the West sea. TF located in steep slope of submarine topography. The distributions of 1st mode in SST were bounded in the same place, and these results should be considered to influence of seasonal variations. To discover temporal and spatial variations of TF, SST gradient values were analyzed by EOF. The time coefficients fo the 1st mode (variance : 64.55%) showed distinctive annual variations and SPF, KF, and SSCF was significantly appeared in March. the spatial distributions of the 2nd mode showed contrast distribution, as SPF and SSCF had strong'-'value, where KF had strong'+'value. The time of'+'and'-'value was May and October, respectively. Time coefficients of the 3rd mode had 2 peaks per year and showed definite seasonal variations. SPF represented striking'+'value which time was March and October. That was result reflected time of the 1st and 2nd mode. We can suggest specific temporal and spatial variations of TF using EOF.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.31 no.5
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• pp.695-706
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• 1998

SST (Sea Surface. Temperature) fronts which were found in the South-West Sea of Korea and the northern area of the East China Sea were examined in order to clarify their positions, shapes, seasonal changes and the formation mechanism, For this study used SST data rearranged from the SST IR image during 1991 to 1996 and oceanographical data obtained by National Fisheries Research and Development Institute. Temperature front in the Cheju Strait was analyzed by the data obtained from a fisheries guidance ship of Cheju Provincial Government, The coastal frontal zone in the South-West Sea of Korea and the offshore frontal zone in the northern area of the East China Sea can be divided into several types (Type of Winter, Summer, Spring, Autumn and late Autumn), Short term variations of SST fronts have a tendency not to move to any Bleat extent for several days. The location of the frontal zone in the southwestern sea of Cheju Island changes on a much large scale than that of the one in the southern coast of Korea, The frontal Tone, formed every year in the southern sea of Korea approaches closer to the coastal area in winter, and moves closer to the south in spring and autumn. The frontal zone of the southwestern sea of Cheju Island moves in a westerly direction from the east, and reaches its most westerly point in the winter and its most easterly point in the summer related to the seasonal change of the Tsushima Current. Additionally, the frontal zone of the southwestern sea of Korea becomes extremely weak in March, April and November. SST fronts are formed every year around the line connecting Cheju Island to Yeoseo Island or to Chungsan Island in the Cheju Strait. A Ring-shaped tidal mixing front appears along the coastal area of Cheju Island throughout the year except during the months from November to January. Especially, in May and October fronts are formed between the coastal waters of Cheju Island and the Tsushima currents connecting the frontal zone of the coastal region in the southern sea of Korea with that of the southwestern sea of Cheju Island.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.