Three-dimensional (3-D) optimally-interpolated sea surface temperature (SST) field was produced by using AQUA/AMSR-E satellite data, and its limitations were described by comparing the temporal average of sea surface temperatures. The 3-D OI (Optimum Interpolation) SST showed a small error of less than $0.05^{\circ}C$ in the central North Pacific, but yielded large errors of greater than $0.4^{\circ}C$ at the coastal area where the satellite microwave data were not available. OI SST composite around pixels with no observation due to heavy rainfall or cloudy pixels had estimation errors of $0.1-0.15^{\circ}C$. Comparison with temporal means showed a tendency that overall OI SSTs were underestimated around heavy cloudy pixels and smoothed out by reducing the magnitude of SST fronts. In the low-latitude areas near the equator, OI SST field produced discontinuity, originated from the window size for the OI procedure. This was mainly caused by differences in the spatial scale of oceanic features. Infernal Rossby deformation radius, as a measure of spatial stale, showed dominant latitudinal variations with O(1) difference in the North Pacific. This study suggests that OI SST methodology should consider latitudinally-varying size of window and the characteristics of spatial scales of oceanic phenomena with substantial dependency on latitude and vertical structure of density.
The Journal of the Korea institute of electronic communication sciences
/
v.15
no.6
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pp.1201-1208
/
2020
A mass of seawater with similar properties in the ocean is called a water mass, and the front is a sea area where two masses of different properties meet. The gradient algorithm is a method of extracting where the sea water temperature pixel changes rapidly assuming that the slope is large, and the place with the large slope is assumed to be a front. This method is able to process large amounts of satellite data at once. Therefore, in this study, we tried to find the front lines in the sea area around the Korean Peninsula by using a gradient algorithm. The study data used gridded sea surface temperature satellite data. The resolution was 1/4°, and the monthly average data from January 1993 to December 2018 were used. There were major five fronts representatively, China Coastal Front, South Sea Coastal Front, Kuroshio Front/ Kuroshio Extension Front, Subpolar Front and the Subarctic Front. As a result of comparing the distribution of front by season, more types of front were distributed in winter and spring than in summer and autumn, and the distribution range was wider.
The seasonal distribution and migration of flying squid, Ommastrephes bartrami (LeSueur), in the North Pacific were studied by means of mantle length, surface temperature, and catch and effort data of the Korean drift gillnet fishery from 1980 to 1983. The water temperature for the best fishing ranged from $15^{\circ}\;to\;16^{\circ}C$ in May through July and from $13^{\circ}\;to\;18^{\circ}C$ in August through January. High densities of flying squid were found in the thermal fronts with $18^{\circ}C$ isotherm in August and with $15^{\circ}C$ isotherm in September. The densities of flying squid were higher in the western region than in the eastern region in the North Pacific. The high densities of flying squid in the northwestern Pacific were attributed to the high gradients of oceanographic properties in the region. Migration models for flying squid were hypothesized based on the monthly distributions of catch per unit net, mantle length compositions by statistical blocks, and the hydrographic features of the North Pacific. The large flying squid moved to the northern region and to the central Pacific region earlier than the small sized group in the northward migration period (from June to August). Flying squid begin the reverse southward migration from the Subarctic Frontal Zone in autumn with onset of cooling and the development of Oyashio Current. The large sized group starts their southward return migration from more northern waters than the small sized group but the former moves past the later ana reaches the spawing ground first.
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.
A series of sheps-of-opportunity sea sryface temperature (SST) measurement beween Jeju and Wando during a period from December 1979 through June 1981 produced following results. 1. A sihnificantly warm water appeared south of Chuja Island and Cheongsan Island during Island. It is suggested that this water represents a current entering the Jeju Strait from the west. Direction of this currint in other seasons is not certain. 2. Coastal waters were found north of the Cheongsan Island and Bogil Islhnd throughout the measurement period. In February these waters sometimes reached as far as Chuja Island to south. 3. Frequently thermal fronts were observed near the Chuja Island and the Cheongsan Island. 4. In summer cold waters appeared north of the Chuja Island and Changsu Island. Intrusion of cold bottom water from offshore and its subsequent vertical mixing due to strong tidal current are probably reponsible for this appearance. 5. Cold waters also appeared locally around islands and in ghe Jeju Harbor in spring and summer. 6. North-south SST difference reached 8-9$^{\circ}C$ in winter which is the annual maximum. 7. Annual range of SST varies from 12-14$^{\circ}C$ in the central part of the Jeju Strait to 16-20$^{\circ}C$ in coastal waters to north. The highest SST appeared everywhere in September but the lowest one did not appesr in the same month of year.
An extensive set of both in-situ and satellite data regarding oceanic sea surface temperatures in Northeast Asian seas, collected over a 10-year period, was collocated and surveyed to assess the accuracy of satellite-observed sea surface temperatures (SST) and investigate the characteristics of satellite measured SST errors. This was done by subtracting insitu SST measurements from multi-channel SST (MCSST) measurements. 845 pieces of collocated data revealed that MCSST measurements had a root-mean-square error of about 0.89$^{\circ}C$ and a bias error of about 0.18$^{\circ}C$. The SST errors revealed a large latitudinal dependency with a range of $\pm3^{\circ}C$ around 40$^{\circ}N$, which was related to high spatial and temporal variability from smaller eddies, oceanic currents, and thermal fronts at higher latitudes. The MCSST measurements tended to be underestimated in winter and overestimated in summer when compared to in-situ measurements. This seasonal dependency was discovered from shipboard and moored buoy measurements, not satellite-tracked surface drifters, and revealed the existence of a strong vertical temperature gradient within a few meters of the upper ocean. This study emphasizes the need for an effort to consider and correct the significant skin-bulk SST difference which arises when calculating SST from satellite data.
To investigate the effect of seasonal marine environment conditions on the cyanobacteria abundance and diversity in the South Sea, four-seasonal surveys were conducted along the 127.5°E survey transect line in the central South Sea using flow cytometry and 16S-23S ITS on the Miseq platform from August 2016 to May 2017. The average abundance of Synechococcus varied from 3.3 × 103 to 7.4 × 104 cells ml-1. The abundance was the highest in the summer and the lowest in the winter, and the abundance fluctuated according to water temperature. The abundance was high in the outer sea affected by TWC. However, in summer, the Coastal areas affected by the Yangtze River were more populated than the outer sea. Prochlorococcus was rare and could not penetrate into coastal areas due to the fronts, but showed its dominance in the waters influenced by the TWC. Synechococcus clades II, VII, IX, CRD1, and CRD2 were predominant in the outer sea area affected by the TWC. In the coastal area, clades I and IV showed higher dominance whereas clades V, VI, WPC1, and 5.3-MS3 with euryhaline characteristics, showed a high dominance rate in the water masses affected by the low-salinity water of the Yangtze River in the summer. Clade XVI, XVII, CB1, CB5, and 5.3-I/II showed high dominance in nutrient-rich waters in the summer with increased water temperature. The abundance and community composition of cyanobacteria changed in the South Sea due to the influence of the TWC and stratification. In the summer, the abundance and the community composition differed, and were mainly affected by the general influence of the TWC in addition to the influence of the Yangtze River low-salinity water.
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
/
v.27
no.2
/
pp.49-70
/
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.
In order to investigate the relation between the marine environmental characteristics and the change of the catch in set net, the marine environment properties were analyzed by temperature and salinity observed in the western coastal area of Cheju Island from 1995 to 1996 and the results are as follows 1) Main axis of Tsushima Current appeared in the western coastal area of Cheju Island was off 2$\~$3 miles from November to May. Therefore the waters of high temperature over $14^{\circ}C$ and high salinity from $34.40\%_{\circ}$ to $34.60\%_{\circ}$ were distributed homogeneously from surface to bottom in this time. But China Coastal Waters of low salinity appeared in the Cheju Strait from June to October, surface waters became of high temperature and low salinity, and middle and bottom waters became of the temperature from 11 to $14^{\circ}C$ and the salinity over $33.50\%_{\circ}$ and then vertically sharp thermocline and halocline are formed in the western coastal area of Cheju Island. In summer, the water temperature and salinity of the surface waters in wstern coastal area of Cheju Island were lower and higher respectively than that in middle area of the Cheju Strait and the temperature and salinity of the bottom waters in this area were higher and lower, respectively than that in middle area of the Cheju Strait. Such a distribution shows a tidal front in this coastal area. On the whole year, surface temperature and salinity were from 14 to $23^{\circ}C$ and from 30.60 to $34.60\%_{\circ}$, respectively, and annual fluctuation range of temperature and salinity was within $9^{\circ}C$ and $4.00\%_{\circ}$, respectively, Thus, annual fluctuation range in this area is much narrower than that in the Cheju Strait. In bottom water, temperature ranges from 14 to $20^{\circ}C$ through the year. Thus, the fluctuation range of temperature is narrow. The low temperature of from $11^{\circ}C$ to $13^{\circ}C$ appeared in the west enterance of Cheju Strait was not shown in this coastal area. 2) The salinity of bottom water was from $33.60\%_{\circ}$ to $34.40\%_{\circ}$ in 1995, while low salinity wale. below $32.00\%_{\circ}$ appeared all depth from June in 1996. Thus, the variation of hydrographic conditions in this area is narrow in winter, and wide in summer due to the influence of China Coastal Waters. 3) In summer, surface cold water, local eddy and fronts of temperature and salinity were showed within 2 mile from the west coast of the Cheju Island due to vertical mixing by tidal current. Especially, temperature and salinity of bottom water are changed with the change of depth around Biyang-Do. Thus, the front of temperature and salinity appeared clearly between shallow area with the depth of under 10 m and deep area with of the depth of more than 50m. Surface water in outside area where high temperature and low salinity water appear intrudes between Worlreong-Ri and Geumreung-Ri. Thus, the front of temperature and salinity was made along the line that connects from this coast to Biyang-Do, The temperature of the bottom water is $2^{\circ}C$ to $4^{\circ}C$ lower than that of the surface water and its salinity is $0.02\%_{\circ}$ to $0.08\%_{\circ}$ higher than that of the surface water even in shallow area.
The optimum mold design and the optimum process condition were constructed upon executing process simulation of rubber injection molding with the commercial CAE program of MOLDFLOW (Ver. 5.2) in order to solve the process-problems of K company relating to cracks, which occurs at the inner cavity wall of C. V. joint boots. As a result it was confirmed that the real cracks occurs at the exactly same position of the cavity as exhibits the defects of weld and meld line and unsatisfactory curing according to the result of simulation. In order to prevent the occurrence of weld and meld line at the defect-position, the location of gate was altered to the optimum position of the cavity. Consequently the filling pattern was established to minimize the degree of the melt-fronts confronting or the melt-flows melding to prevent the occurrence of weld and meld line at the defect-position. It was observed that both gate-positions to maximize the degree of the formation of weld and meld line and air traps are located, respectively, in opposite direction each other with reference to the optimum gate position. In addition, the temperature of mold was raised by $10^{\circ}C$ and maintained at $170^{\circ}C$ for satisfactory curing.
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