• Title/Summary/Keyword: 수온전선의 정량화

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Development of Line Density Index for the Quantification of Oceanic Thermal Fronts (해양의 수온전선 정량화를 위한 선밀도 지수 개발)

  • Cho, Hyun-Woo;Kim, Kye-Hyun
    • Journal of the Korean Association of Geographic Information Studies
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    • v.9 no.2
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    • pp.227-238
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    • 2006
  • Line density index(LDI) was developed to quantify a densely isothermal line rate as standard index in the ocean environment. Theoretical background on the LDI development process restricting index range 0 to 100 was described. And validation test was done for the LDI application condition that total line length is not greater than 1/10 of unit area. NOAA SST(Sea Surface Temperature) data were used for the experimental application of LDI in the South Sea of Korea. Using GIS, $0.1^{\circ}C$ isothermal lines were linearized as vector data form SST raster data, and unit area were built as polygon data. For the LDI calculation, spatial overlapping(line in polygon) was implemented. To analyze the effect of unit area size for the LDI distribution, two cases of unit area size were designed and descriptive statistics was calculated including performing normality test. The results showed no change of LDI's essential characteristics such as mean and normality except for the range of value, variance and standard deviation. Accordingly, it was found that complex structure of thermal front and even smaller scale of front width than unit area size could influence on the LDI distribution. Also, correlation analysis performed between LDI and difference of temperature(${\Delta}T^{\circ}C$), and horizontal thermal gradient(${\Delta}T^{\circ}C/km$) on the front was obtained from linear regression model. This obtained value was compared with the results from previous researches. Newly developed LDI can be used to compare the thermal front regions changing spatio-temporally in the ocean environment using absolute index value. It is considered to be significant to analyze the relationship between thermal front and marine environment or front and marine organisms in a quantitative approach described in this study.

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Temporal and spatial variations of SST and Ocean Fronts in the Korean Seas by Empirical Orthogonal Function (경험직교함수 분석에 의한 한반도 주변해역의 해수면온도 및 수온 전선의 시.공간 변화)

  • Yoon Hong-Joo;Byun Hye-Kyung
    • 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.

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Temporal and spatial variations of SST and Ocean Fronts in the Korean Seas by Empirical Orthogonal Function (경험 직교함수 분석에 의한 한반도 주변해역의 해수면온도 및 수온 전선의 시${\cdot}$공간 변화)

  • Yoon, Hong-Joo;Byun, Hye-Kyung
    • 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.

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