• Journal of the Korean earth science society
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• v.33 no.3
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• pp.259-268
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• 2012

To investigate the fluctuation of marine aerosol number concentration at each different size with vertical winds in ocean area, aerosol particles and vertical wind components were measured in the Ieodo Ocean Research Station, which is located to 419 km southwest of Marado, the southernmost island of Korea, from 8 to 22 June 2009. The Laser Particle Counter (LPC) and ultrasonic anemometer were used to measure the number of aerosol particles and vertical wind speed. Surface weather chart, NCEP/NCAR reanalysis data and sounding data were used to analyze the synoptic condition. The distribution of aerosol number concentration had a large fluctuation of bigger particles more than 1.0 ${\mu}m$ in diameter by vertical wind speed during precipitation. The aerosol particles larger than 1.0 ${\mu}m$ in diameter increased as the wind changed from downward to upward during precipitation. The aerosol number concentration of bigger size than 1.0 ${\mu}m$ in diameter increased about 5 times when vertical velocity was about 0.4 $ms^{-1}$. In addition, the accumulation and coarse mode aerosol number concentration decreased about 45% and 92%, respectively compared to concentrations during precipitation period. It is considered that vertical wind plays an important role for the increasing of coarse mode aerosol number concentration compared to the large aerosol particles sufficiently removed by the scavenging effect of horizontal winds. Therefore, the upward vertical winds highly contribute to the formation and increase in aerosol number concentration below oceanic boundary layer.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.924-942
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• 2021

The expansion of the Changjiang Diluted Water (CDW) plume during summer is known to be a major factor influencing phytoplankton diversity, community structure, and the regional marine environment of the northern East China Sea (ECS). The discharge of the CDW plume was very high in the summer of 2020, and cruise surveys and stationary monitoring were conducted to understand the dynamics of changes in environmental characteristics and the impact on phytoplankton diversity and community structure. A cruise survey was conducted from August 16 to 17, 2020, using R/V Eardo, and a stay survey at the Ieodo Ocean Research Station (IORS) from August 15 to 21, 2020, to analyze phytoplankton diversity and community structure. The southwestern part of the survey area exhibited low salinity and high chlorophyll a fluorescence under the influence of the CDW plume, whereas the southeastern part of the survey area presented high salinity and low chlorophyll a fluorescence under the influence of the Tsushima Warm Current (TWC). The total chlorophyll a concentrations of surface water samples from 12 sampling stations indicated that nano-phytoplankton (20-3 ㎛) and micro-phytoplankton (> 20 ㎛) were the dominant groups during the survey period. Only stations strongly influenced by the TWC presented approximately 50% of the biomass contributed by pico-phytoplankton (< 3 ㎛). The size distribution of phytoplankton in the surface water samples is related to nutrient supplies, and areas where high nutrient (nitrate) supplies were provided by the CDW plume displayed higher biomass contribution by micro-phytoplankton groups. A total of 45 genera of nano- and micro-phytoplankton groups were classified using morphological analysis. Among them, the dominant taxa were the diatoms Guinardia flaccida and Nitzschia spp. and the dinoflagellates Gonyaulax monacantha, Noctiluca scintillans, Gymnodinium spirale, Heterocapsa spp., Prorocentrum micans, and Tripos furca. The sampling stations affected by the TWC and low in nitrate concentrations presented high concentrations of photosynthetic pico-eukaryotes (PPE) and photosynthetic pico-prokaryotes (PPP). Most sampling stations had phosphate-limited conditions. Higher Synechococcus concentrations were enumerated for the sampling stations influenced by low-nutrient water of the TWC using flow cytometry. The NGS analysis revealed 29 clades of Synechococcus among PPP, and 11 clades displayed a dominance rate of 1% or more at least once in one sample. Clade II was the dominant group in the surface water, whereas various clades (Clades I, IV, etc.) were found to be the next dominant groups in the SCM layers. The Prochlorococcus group, belonging to the PPP, observed in the warm water region, presented a high-light-adapted ecotype and did not appear in the northern part of the survey region. PPE analysis resulted in 163 operational taxonomic units (OTUs), indicating very high diversity. Among them, 11 major taxa showed dominant OTUs with more than 5% in at least one sample, while Amphidinium testudo was the dominant taxon in the surface water in the low-salinity region affected by the CDW plume, and the chlorophyta was dominant in the SCM layer. In the warm water region affected by the TWC, various groups of haptophytes were dominant. Observations from the IORS also presented similar results to the cruise survey results for biomass, size distribution, and diversity of phytoplankton. The results revealed the various dynamic responses of phytoplankton influenced by the CDW plume. By comparing the results from the IORS and research cruise studies, the study confirmed that the IORS is an important observational station to monitor the dynamic impact of the CDW plume. In future research, it is necessary to establish an effective use of IORS in preparation for changes in the ECS summer environment and ecosystem due to climate change.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.153-178
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• 2018

Most operational uses of wind speed data require measurements at, or estimates generated for, the reference height of 10 m above mean sea level (AMSL). On the Ieodo Ocean Research Station (IORS), wind speed is measured by instruments installed on the lighthouse tower of the roof deck at 42.3 m AMSL. This preliminary study indicates how these data can best be converted into synthetic 10 m wind speed data for operational uses via the Korea Hydrographic and Oceanographic Agency (KHOA) website. We tested three well-known conventional empirical neutral wind profile formulas (a power law (PL); a drag coefficient based logarithmic law (DCLL); and a roughness height based logarithmic law (RHLL)), and compared their results to those generated using a well-known, highly tested and validated logarithmic model (LMS) with a stability function (${\psi}_{\nu}$), to assess the potential use of each method for accurately synthesizing reference level wind speeds. From these experiments, we conclude that the reliable LMS technique and the RHLL technique are both useful for generating reference wind speed data from IORS observations, since these methods produced very similar results: comparisons between the RHLL and the LMS results showed relatively small bias values ($-0.001m\;s^{-1}$) and Root Mean Square Deviations (RMSD, $0.122m\;s^{-1}$). We also compared the synthetic wind speed data generated using each of the four neutral wind profile formulas under examination with Advanced SCATterometer (ASCAT) data. Comparisons revealed that the 'LMS without ${\psi}_{\nu}^{\prime}$ produced the best results, with only $0.191m\;s^{-1}$ of bias and $1.111m\;s^{-1}$ of RMSD. As well as comparing these four different approaches, we also explored potential refinements that could be applied within or through each approach. Firstly, we tested the effect of tidal variations in sea level height on wind speed calculations, through comparison of results generated with and without the adjustment of sea level heights for tidal effects. Tidal adjustment of the sea levels used in reference wind speed calculations resulted in remarkably small bias (<$0.0001m\;s^{-1}$) and RMSD (<$0.012m\;s^{-1}$) values when compared to calculations performed without adjustment, indicating that this tidal effect can be ignored for the purposes of IORS reference wind speed estimates. We also estimated surface roughness heights ($z_0$) based on RHLL and LMS calculations in order to explore the best parameterization of this factor, with results leading to our recommendation of a new $z_0$ parameterization derived from observed wind speed data. Lastly, we suggest the necessity of including a suitable, experimentally derived, surface drag coefficient and $z_0$ formulas within conventional wind profile formulas for situations characterized by strong wind (${\geq}33m\;s^{-1}$) conditions, since without this inclusion the wind adjustment approaches used in this study are only optimal for wind speeds ${\leq}25m\;s^{-1}$.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.219-225
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• 2004

Following a preceding study if Shin et al.(2004), wave fields for a month if September if 2003 are simulated based on the modified WAM cycle 4 model that enables the precise wave hindcasting with fine spatial meshes, and characteristics of extreme waves at the south roast if Koreo are analyzed The accuracy if applied wave model is verified by comparing computed wave parameters and corresponding ones measured at Ieodo ocean research station. The wave hindasting if typhoon 'Maemi' with an hour time interval reveals the extreme wave characteristics at 4 primary locations if south coast of Korea as follows: 1) At the front sea of Chaguido in the south of Jeju-do, the maximum significant wave height, moon wave period and mean wave direction appear to be 7.41m, 13.65s and $6.4^{\circ}$, respectively at 16:00 KST if Sep. 12, 2003. 2) At the entrance of Masan Bay, 12.50m, 13.65s and $1.2^{\circ}$ at 21:00 KST if Sep. 12. 3) At the front sea of Suyoung Bay, 13.85m, 13.81s and $0.2^{\circ}$ at 22;00 KST if Sep. 12. 4) At the front sea of Ulsan port, 11.00m, 13.25s and $28^{\circ}$ at 23:00 KST if Sep.

• Ocean and Polar Research
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• v.42 no.3
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• pp.195-210
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• 2020

The observed time series from the Korea Ocean Research Stations (KORS) in the Yellow and East China Seas (YECS) have various sources of noise, including bio-fouling on the underwater sensors, intermittent depletion of power, cable leakage, and interference between the sensors' signals. Besides these technical issues, intricate waves associated with background tidal currents tend to result in substantial oscillations in oceanic time series. Such technical and environmental issues require a regionally optimized automatic quality control (QC) procedure. Before the achievement of this ultimate goal, we examined the approach of the Ocean Observatories Initiative (OOI)'s standard QC to investigate whether this procedure is pertinent to the KORS. The OOI QC consists of three categorized tests of global/local range of data, temporal variation including spike and gradient, and sensor-related issues associated with its stuck and drift. These OOI QC algorithms have been applied to the water temperature time series from the Ieodo station, one of the KORS. Obvious outliers are flagged successfully by the global/local range checks and the spike check. Both stuck and drift checks barely detected sensor-related errors, owing to frequent sensor cleaning and maintenance. The gradient check, however, fails to flag the remained outliers that tend to stick together closely, as well as often tend to mark probably good data as wrong data, especially data characterized by considerable fluctuations near the thermocline. These results suggest that the gradient check might not be relevant to observations involving considerable natural fluctuations as well as technical issues. Our study highlights the necessity of a new algorithm such as a standard deviation-based outlier check using multiple moving windows to replace the gradient check and an additional algorithm of an inter-consistency check with a related variable to build a standard QC procedure for the KORS.

• Ocean and Polar Research
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• v.30 no.3
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• pp.303-312
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• 2008

Despite the impacts of the climate changes on the pelagic ecosystem, few studies have examined the pelagic-benthic coupling in the adjacent East China Sea and Korea Strait. Therefore, the species composition and abundance of the macrobenthic community, as well as the potential food sources of benthic fauna were investigated in the present study using stable isotope analysis (${\delta}^{13}C\;and\;{\delta}^{15}N$) for suspended particulate organic matter (SPOM), sedimentary organic matter (SOM), phytoplankton, and zooplankton. A total of 157 macrobenthic fauna were collected, and the density of the macrobenthic fauna ranged from 4 to 434 ind./0.25 $m^2$, with an average density of 149 ind./0.25 $m^2$. The density of the benthic fauna increased moving from offshore shelf sites to coastal sites adjacent to the Korea Strait. Cluster analysis showed that the macrobenthic communities consisted of three distinct groups: group A in the Korea Strait, group B in the East China Sea, and group C near Ieodo. The dominant species in group A were the amphipods Photis japonica and Ampelisca miharaensis, followed by the polychaete Scolotoma longifolia. Environmental variables, such as the temperature of the seawater and sediment, and oxygen, and chlorophyll a levels, appeared to affect the structure of the community, suggesting the importance of coupling with the pelagic system. The ${\delta}^{13}C$ values of SPOM and zooplankton ranged from -22.97 to -23.5% and -19.92 to -21.86%, respectively, showing a relatively narrow range(<1%) between the two components. The difference between the ${\delta}^{13}C$ values of SOM and pelagic organic matter was also within 1%, suggesting that the SOM originated from the pelagic system, which is an important factor controlling the macrobenthic community.

• Ocean and Polar Research
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• v.32 no.3
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• pp.213-228
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• 2010

In late 2010, the Korea Hydrographic and Oceanographic Administration proposed a national monitoring project involving the deployment of 8 realtime ocean data buoys. The area occupied by the buoy-array, located south of the Ieodo Ocean Research Station, can be regarded as a kind of gateway to Korean waters with respect to warm currents and the shipping industry. The acronym for the project, KOGA (Korea Ocean Gate Array) was derived from this aspect. To ensure the success of the project, international cooperation with the neighboring countries of China and Japan is highly desirable. Once KOGA is successfully launched and the moored buoys start to produce data, the data will be applied to various areas such as data assimilation for operational oceanography, circulation dynamics, biogeochemical studies, satellite observations, and air-sea interactions. The aim of this paper is to provide suggestions for KOGA planning and applications.

• Journal of Navigation and Port Research
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• v.28 no.8
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• pp.745-751
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• 2004

Following a preceding study of Shin et al.(2004), wave fields for a month of September of 2003 are simulated based on the modified WAM cycle 4 model that enables the precise wave hindcasting with fine spatial meshes, and characteristics of extreme waves at the south coast of Korea are analyzed The accuracy of applied wave model is verified by comparing computed wave parameters and corresponding ones measured at Ieodo ocean research station. The wave hindcasting of typhoon 'Maemi' with an hour time interval reveals the extreme wave characteristics at 4 primary locations of south coast of Korea as follows: 1) At the front sea of Chaguido in the south of Jeju-do, the maximum significant wave height, mean wave period and mean wave direction appear to be 7.41m, 13.65s and $6.4^{\circ}$ respectively at 16:00 KST of Sep. 12, 2003. 2) At the entrance of Masan Bay, 12.50m, 13.65s and $1.2^{\circ}$ at 21:00 KST of Sep. 12. 3) At the front sea of Suyoung Bay, 13.85m, 13.81s and $0.2^{\circ}$ at 22;00 KST of Sep. 12. 4) At the front sea of Ulsan port, l1.00m, 13.25s and $2.8^{\circ}$ at 23:00 KST of Sep. 12.