• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.183-188
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• 2001

The NOAA AVHRR remotely sensed SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the sea near korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple SST images, all of images must be consistent exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which automatically detects cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$3$^{\circ}C$ as a criterion of SST anomalies). The remotely sensed SST data are tuned by comparing remotely sensed data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel and the SST anomalies are studied by statistical method. It was found that the SST anomalies are rather persistent for one or two months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST. In the Markov lprocess model of SST anomalies, autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. The developed algorithm with automatic cloud pixel detection and rediction of future SST is expected to be incorporated to the operational real time service of SST around Korea.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.236-241
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• 1998

The NOAA AVHRR remote sense SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the seas adjacent to Korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple 557 images, all of images must be aligned exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which yields automatic detections of cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$ 3$^{\circ}C$ as a criterion of SST anomalies). The remote sense SST data are tuned by comparing remote sense data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel. The SST anomalies are studied by statistical method. We found that the SST anomalies are rather persistent with time scales between 1 and 2 months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST Model fit of SST anomalies to the Markov process model yields that autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. We plan to improve our algorithms of automatic cloud pixel detection and prediction of future SST. Our algorithm is expected to be incorporated to the operational real time service of SST around Korea.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.21 no.6
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• pp.351-360
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• 1988

Based on historic data of monthly means of sea surface temperatures (SST) for 24 years $(1921\~1944) $ at 23 Korean and Japanese coastal stations in the East Sea (the Japan Sea), we analyzed spatio-temporal characteristics of coastal SST and SST anomalies. The means of SST at Korean coast are higher than those at Japanese coast of the same latitudes, and the annual range of SST at Korean coast are larger than those at Japanese coast. Empirical orthogonal function analysis shows that almost all $(96\%)$ of the SST fluctuations are described by simultaneous seasonal variations. The flurtuations of SST anomalies are small in the Korea Strait and large at the boundaries between the warm and told currents in the basin. The fluctuations of SST anomalies along Korean coast are correlated each other The same is true for SST anomalies along Japanese coast. However, there is only weak correlation between the SST anomalies at Korean coast and those at Japanese coast. Empirical orthogonal function analysis shows that $27\%$ of the coastal SST anomalies in the East Sea are described by simultaneous fluctuations, and $12\%$ of them are described by alternating fluctuations between Korean and Japanese coasts.

• 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.

• Ocean and Polar Research
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• v.23 no.2
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• pp.189-194
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• 2001

A practical algorithm for prediction of the sea surface temperatures (SST)from the satellite remote sensing data is presented in this paper. The fluctuations of SST consist of deterministic normals and stochastic anomalies. Due to large thermal inertia of sea water, the SST anomalies can be modelled by autoregressive or Markov process, and its near future values can be predicted provided the recent values of SST are available. The actual SST is predicted by superposing the pre-known SST normals and the predicted SST anomalies. We applied this prediction algorithm to the NOAA AVHRR weekly SST data for 18 years (1981-1998) in the seas adjacent to Korea (115-$145^{\circ}E$, 20-$55^{\circ}N$). The algorithm is applicable not only for prediction of SST in near future but also for nowcast of SST in the cloud covered regions.

• 한국해양학회지
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• v.22 no.2
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• pp.57-62
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• 1987

The Markov chain properties of the sea surface temperature (SST) anomalies, namely, the dependency of the monthly SST anomaly on that of the previous month, are studied based on the SST data for 28years(1957-1984) at 5 stations in the southeastern coast of Korea. Wi classified the monthly SST anomalies at each station into the low, the normal and the high state, and computed transition probabilities between SST anomalies of two successive months The standard deviation of SST anomalies at each station is used as a reference for the classification of SST anomalies into 3states. The transition probability of the normal state to remain in the same state is about 0.8. The transition probability of the high or the low states to remain in the same state is about one half. The SST anomalies have almost no probability to transit from the high (the low) state to the low (the high) state. Statistical tests show that the Markov chain properties of SST anomalies are stationary in tine and homogeneous in space. The multi-step Markov chain analysis shows that the 'memory' of the SST anomalies at the coastal stations remains about 3 months.

• Ocean and Polar Research
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• v.23 no.4
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• pp.361-376
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• 2001

The characteristics of SST variability in the East Sea are analyzed using NOAA/AVHRR weekly SST data with about $0.18^{\circ}{\times}0.18^{\circ}$ resolution ($1981{\sim}2000$) and reconstructed historical monthly SST data with $2^{\circ}{\times}2^{\circ}$ resolution $(1950{\sim}1998)$. The distinct feature of wintertime SST is high variability in the western and eastern parts of $38^{\circ}{\sim}40^{\circ}$ latitudinal band, which are the northern boundary of warm current in the East Sea during winter. However, summertime SST exhibits variability with similar magnitude in the entire region of the East Sea. The analysis of remote correlation also shows that SST in the East Sea is closely correlated with that in the region of Kuroshio in winter, but in summer is related with that in the western and eastern regions of the same latitudes. From these results it is postulated that the SST variability in the East Sea may be related with the variations of East Korean Warm Current and Tsushima Warm Current in winter, but in summer probably with the variations of atmospheric components. In the analysis of ENSO related SST anomaly, a significant negative correlation between SST anomalies in the East Sea and SST anomalies in the tropical Pacific is found in the months of August-October (ASO). The SST in the ASO period shows more significant cooling in E1 $Ni\~{n}o$ events than warming in La $Ni\~{n}a$ events. Also, the regional analysis shows by the Student's t-test that the negative SST anomalies in the E1 $Ni\~{n}o$ events are more significant in the southwestern part of the East Sea.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.18 no.6
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• pp.497-505
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• 1985

We studied the annual and interannual fluctuations of sea surface temperature (SST) for 30 years ($1941{\sim}1970$) at 9 coastal stations in the Tsushima Current and the Kuroshio regions by means of harmonic analysis, correlation analysis, and spectral analysis. The fluctuations of annual mean and amplitude are 0.3 to $0.7^{\circ}C$, and those of annual phase are 3 to 4 days. The SST anomalies are about $1^{\circ}C$, and they are relatively large in summer and winter than in spring and fall. The SST anomalies in the Tsushima Current and the Kuroshio regions are related with each other. The predominant periods of SST anomalies differ slightly from station to station. The quasi-biennial (26 months) and pole tide (14 months) oscillations are found in the spectra of SST anomalies.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.17 no.6
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• pp.557-565
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• 1984

The year-to-year fluctuations of seasonal variation of sea surface temperatures (SST) in the Korea Strait are studied based on monthly SST data for more than 50 years at Mitsushima ana Okinoshima in the strait. The frequency distribution of SST has two peaks at temperatures below and above the multi-year average, but that of SST anomalies has only one peak at the zero anomaly. More than $95\%$ of the anomalies are in the range of ${\pm}2^{\circ}C$. The harmonic constansts of seasonal SST variation vary from year to year. The standard deviations of annual means, annual amplitudes, ana semi-annual amplitudes are less than $1^{\circ}C$, and those of the annual ana semi-annual phases are about $5^{\circ}$ and less than $50^{\circ}$, respectively. The SST in the Korea Strait have a tendency to decrease their amplitudes as the annual means increase. Physical mechanisms responsible for the analyzed results are discussed in this paper.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.20 no.2
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• pp.89-94
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• 1987

The fluctuations of sea surface temperatures (SST) and their anomalies in the Tsushima Current region are studied by means of empirical orthogonal function (EOF) analysis of the monthly SST data for 30 years (1941-1970) at 8 coastal stations. The overall features of the seasonal variation of SST are described by the first EOF mode, which explains $97.2\%$ of the total variance. Annual ranges of seasonal variation of SST and root-mean-square amplitudes of SST anomalies in the downstream of the Tsushima Current are larger than those in the upstream. The SST anomalies in the Tsushima Current region consist of simultaneous fluctuations, which explain $40.9\%$ of the total variance, and 'see-saw' fluctuations of which rise and fall in the upstream are opposite to those in the downstream. The latter second EOF mode explains $19.3\%$ of the total variance. We generated the low-pass (periods longer than 24 months), band-pass (periods between 6 and 24 months) and high-pass (periods shorter than 6 months) SST anomaly series and analyzed them by EOF method. The spatial distributions of the first and second EOF modes of all filtered SST anomalies are similar to each other.