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 Changjiang Diluted Water (CDW) spreads over the East China Sea every summer and significantly affects the sea surface salinity changes in the seas around Jeju Island and the southern coast of Korea peninsula. Sometimes its effect extends to the eastern coast of Korea peninsula through the Korea Strait. Specifically, the CDW has a significant impact on marine physics and ecology and causes damage to fisheries and aquaculture. However, due to the limited field surveys, continuous observation of the CDW in the East China Sea is practically difficult. Many studies have been conducted using satellite measurements to monitor CDW distribution in near-real time. In this study, an algorithm for estimating Sea Surface Salinity (SSS) in the East China Sea was developed using the Geostationary Ocean Color Imager (GOCI). The Multilayer Perceptron Neural Network (MPNN) method was employed for developing an algorithm, and Soil Moisture Active Passive (SMAP) SSS data was selected for the output. In the previous study, an algorithm for estimating SSS using GOCI was trained by 2016 observation data. By comparison, the train data period was extended from 2015 to 2020 to improve the algorithm performance. The validation results with the National Institute of Fisheries Science (NIFS) serial oceanographic observation data from 2011 to 2019 show 0.61 of coefficient of determination (R2) and 1.08 psu of Root Mean Square Errors (RMSE). This study was carried out to develop an algorithm for monitoring the surface salinity of the East China Sea using GOCI and is expected to contribute to the development of the algorithm for estimating SSS by using GOCI-II.
Laver is sea weeds that might have been eaten by Korean people since ancient times. The begining of laver culture is not known exactly, but it appears to be prehistoric age. Some laver culture complexes have been built in southern coastal sea of Korea around 1910. This paper was considered about the origin and development process of Korean laver culture industry by investigating Korean and Asian old books concerned. The results are as follows. 1. According to the Korean old books ralated, the name of laver is classified into 10kinds. Gim and Hae-I were called by Korean. Gim means weeds and Hae-I means the manufactured laver by cutting and drying like paper sheet. Ja-Chae and Hae-Tae are come from Chinese, however they are commonly called by Korean, Japanese and Chinese. Rest six names are come from Chinese botany. 2. As Chinese used laver as medicine for wen, scrofula, fever, vomiting, diarrhoea and. so on, they didn't regard it as foods and took into account an warning by Chinese botany that they could take ill when overeating it. On the other as Korean people have eaten it with pleasure nevertheless the Chinese warning, various foods using laver have been developed. The typical food is rice covering laver sheet. It is also popular to Japanese. 3. Laver culture can be carried out in all coastal seas around Korean peninsula, the best sea area for it is the middle west of south sea. 4. Seopkkoji type is a laver culture method that when branches of tree are put in tidal flat laver sporules are attached and gronm on them. It was begun by Hae-Jak Kun(a group of fishery slaves) on Kwang-Yang bay the most suitable for. laver growth at the beginning of King $Sung-long(1469{\~}1481)$. It is assumed that when Hae-Jak Kun set Oe-Jeon(a sort of fixing fishing gear) to catch tributary fish for king, they could find grown laver attached on Oe-Jeon and invent Seopkkoji type for exclusive laver culture. That was carried out 200 fears earlier than in Japan. Dde-Bal type is more advanced and productive laver culture method with thinly spilt bamboo tied like screen(one end fixed on bottom and other end set free in water), It is assumed that Dde-Bal type was begun in Wan-Do county in King Chull-Jong(1830). All laver culture methods developed were transfered to Japan.
Kim, Jeong-Dae;Jeong, Shin-Taek;Cho, Hong-Yeon;Oh, Nam-Sun
Journal of Korean Society of Coastal and Ocean Engineers
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v.19
no.4
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pp.313-319
/
2007
Long term wave climate of both extreme and operational wave height is essential for planning and designing coastal structures. Since the availability of the field wave data for the waters around Korean peninsula is limited to provide a reliable wave statistics, the wave climate information has been generated by means of long-term wave hindcasting using available meteorological data. In this paper, a set of deep water wave data obtained from KORDI(2003) were analyzed for extreme wave heights. These wave data at 67 stations off the Korean coast from 1979 to 1998 were arranged in the 16 directions. The probability distributions considered in this research were the FT-I and Weibull distribution. For each of these distributions, the method proposed by Goda(2004) was applied to estimate the parameters. For judgment of best fitting, MIR criterion proposed by Goda and Gobune(1990) was used. FT-I distribution which best fits to the 886 data, while Weibull(k=0.75) 81 data, Weibull(k=1.00) 105 data.
Journal of the Korean Society of Marine Environment & Safety
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v.27
no.5
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pp.618-629
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2021
The Fukushima nuclear power plant (NPP) accident caused by the East Japan Earthquake in 2011 and the recent increase in the frequency of earthquakes in Korea have caused safety concerns regarding radionuclide exposure. In addition, the Tokyo Electric Power Company (TEPCO) in Japan recently decided to release radionuclide-contaminated water from Fukushima's NPP into the Pacific Ocean, raising public concerns that the possibility of radionuclide contamination through both domestic- and foreign fishery products is increasing. Although many studies have been conducted on the input of artificial radionuclides into the Pacific after the Fukushima NPP accident, studies on the distribution and accumulation of artificial radionuclides in marine products from East Asia are lacking. Therefore, in this study, we attempted to explore recent research on the distribution of artificial radionuclides (e.g., 137Cs, 239+240Pu, 90Sr, and etc.) in marine products from Korean seas after the Fukushima NPP accident. In addition, we also discuss future research directions as it is necessary to prepare for likely radiation accidents in the future around Korea associated with the new nuclear facilities planned by 2030 in China and owing to the discharge of radionuclide-contaminated water from the Fukushima NPP.
KIM GUEBUEM;HWANG DONG-WOON;RYU JAE-WOONG;LEE YONG-WOO
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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v.10
no.4
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pp.204-212
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2005
Recognition has emerged that nutrient inputs from the submarine discharge of fresh, brackish, and marine groundwaters into the coastal ocean are comparable to the inputs via river discharge. The coastal areas of the Korea peninsula and adjacent seas exhibit particular importance in the role of submarine groundwater discharge (SGD), in terms of the magnitude of SGD and associated continental material fluxes. For example, in the southern sea of Korea, SGD transports excess nutrients into the coastal regions and thus appears to influence ecosystem changes such as the outbreak of red tides. Around volcanic island, Jeju, which is composed of high permeability rocks, the amount of SGD is higher by orders of magnitude relative to the eastern coast of North America where extensive SGD studies have been conducted. In particular, nutrient discharge through SGD exerts a significant control on coastal ecosystem changes and results in benthic eutrophication in semi-enclosed Bang-du bay, Jeju. In the entire area of the Yellow Sea, tile submarine discharge of brackish groundwater and associated nutrients are found to rival the river discharges into the Yellow Sea, including those through Yangtze River, Han River, etc. In the eastern coast of the Korea peninsula, SGD is significantly higher during summer than winter due to high hydraulic gradients and due to wide distribution of high permeability sandy zones, faults, and fractures. On the other hand, in the estuarine water, downstream construction of the dam in the Nakdong River, SGD was highest when the river discharge was lowest (but water level of the dam was highest). This suggests that even though there is no visible freshwater discharge into this estuary, the discharge of chemical species is significant through SGD. On the basis of the results obtained from the coastal areas of the Korea peninsula, SGD is considered to be an important pathway of continental contaminants influencing tidal-flat ecosystems, red tides, and coral ecology. Thus, future costal management should pay great attention to the impact of SGD on coastal pollution and eutrophication.
The relationships between wave and wind around the Korean Peninsula have been analyzed with the data from the buoys moored at five stations (Dugjug-do, Chilbal-do, Geomoon -do, Geoje-do, Donghae) by Korea Meteorological Administration. Generally, the relationship between wave and wind is the highest at the stations in the West Sea and the lowest at the stations in the South Sea, and the middle at the station in the East Sea. The characteristics shown at each station are as follows. Highest wave is developed at Chilbal-do with strong northwesterly wind in winter because the sea is opened in the wind direction and wave is amplified by shoaling effect. At Chilbal-do, wave directions coincide with wind directions relatively well. On the other hand, waves are not fully developed at Dugjug-do in winter due to limited fetch since the sea is blocked by Hwanghae-do in the northwest direction. The limitation in fetch is more serious at the stations in the South Sea. In the South Sea, the direction of dominant northerly wind is blocked by land so that wave heights are small even with very strong northerly wind. In the South Sea, whatever wind direction is, waves dominantly come in the direction from the East China Sea, which are from the south at Geomoon-do and the southwest at Geoje-do. At these directions, waves are coming even with weak wind. At the station in the East Sea, waves are highly developed due to vast area, but not so high as in Chilbal-do because wind and wave directions do not coincide in many cases. As shown, wind direction is important in the wave development as well as wind speed. The reason is that the fetch is determined by wind direction. In the case of long-lasted wind with fixed direction at Chilbal-do and Dugjug-do, wave directions are well coincident with wind directions and wave heights increase with response time, which is the duration between the highest wind and wave. However, in the case of disagreement between wind and wave directions at the station in the East Sea, wave heights do not increase as highly as at Chilbal-do and Dugjug-do in spite of strong wind and longer response time. The results show us that waves are highly developed with strong wind, long fetch, and long duration, and also show that wave development ratios are different at different stations due to environmental factors such as the direction towards sea or land, bottom topography, and the scales of adjacent seas.
Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R2 of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.
Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.
Kim, Ji Hye;Eom, Hyun-Min;Choi, Jong-Kuk;Lee, Sang-Min;Kim, Young-Ho;Chang, Pil-Hun
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
/
v.20
no.1
/
pp.1-15
/
2015
Impacts of Sea Surface Temperature (SST) assimilation to the prediction of upper ocean temperature is investigated by using a regional ocean forecasting system, in which 3-dimensional optimal interpolation is applied. In the present study, Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset is adopted for the daily SST assimilation. This study mainly compares two experimental results with (Exp. DA) and without data assimilation (Exp. NoDA). When comparing both results with OSTIA SST data during Sept. 2011, Exp. NoDA shows Root Mean Square Error (RMSE) of about $1.5^{\circ}C$ at 24, 48, 72 forecast hour. On the other hand, Exp. DA yields the relatively lower RMSE of below $0.8^{\circ}C$ at all forecast hour. In particular, RMSE from Exp. DA reaches $0.57^{\circ}C$ at 24 forecast hour, indicating that the assimilation of daily SST (i.e., OSTIA) improves the performance in the early SST prediction. Furthermore, reduction ratio of RMSE in the Exp. DA reaches over 60% in the Yellow and East seas. In order to examine impacts in the shallow costal region, the SST measured by eight moored buoys around Korean peninsula is compared with both experiments. Exp. DA reveals reduction ratio of RMSE over 70% in all season except for summer, showing the contribution of OSTIA assimilation to the short-range prediction in the coastal region. In addition, the effect of SST assimilation in the upper ocean temperature is examined by the comparison with Argo data in the East Sea. The comparison shows that RMSE from Exp. DA is reduced by $1.5^{\circ}C$ up to 100 m depth in winter where vertical mixing is strong. Thus, SST assimilation is found to be efficient also in the upper ocean prediction. However, the temperature below the mixed layer in winter reveals larger difference in Exp. DA, implying that SST assimilation has still a limitation to the prediction of ocean interior.
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