• Journal of the Korean earth science society
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• v.44 no.1
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• pp.1-12
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• 2023

Oceanic wind waves have been recognized as one of the important indicators of global warming and climate change. It is necessary to study the spatial and temporal variability of significant wave height (SWH) and wave direction in the Yellow Sea and a part of the East China Sea, which is directly affected by the East Asian monsoon and climate change. In this study, the spatial and temporal variability including seasonal and interannual variability of SWH and wave direction in the Yellow Sea and East China Sea were analyzed using European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data. Prior to analyzing the variability of SWH and wave direction using the model reanalysis, the accuracy was verified through comparison with SWH and wave direction measurements from Ieodo Ocean Science Station (I-ORS). The mean SWH ranged from 0.3 to 1.6 m, and was higher in the south than in the north and higher in the center of the Yellow Sea than in the coast. The standard deviation of the SWH also showed a pattern similar to the mean. In the Yellow Sea, SWH and wave direction showed clear seasonal variability. SWH was generally highest in winter and lowest in late spring or early summer. Due to the influence of the monsoon, the wave direction propagated mainly to the south in winter and to the north in summer. The seasonal variability of SWH showed predominant interannual variability with strong variability of annual amplitudes due to the influence of typhoons in summer.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.502-517
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• 2019

Global warming and rapid climate change have long affected the characteristics of typhoons in the Northwest Pacific, which has induced increasing devastating disasters along the coastal regions of the Korean peninsula. Synthetic Aperature Radar (SAR), as one of the microwave sensors, makes it possible to produce high-resolution sea surface wind field around the typhoon under cloudy atmospheric conditions, which has been impossible to obtain the winds from satellite optical and infrared sensors. The Geophysical Model Functions (GMFs) for sea surface wind retrieval from SAR data requires the input of wind direction, which should be based on the accurate estimation of the center of the typhoon. This study estimated the typhoon centers using Sentinel-1A images to improve the problem of typhoon center detection method and to reflect it in retrieving the sea surface wind. The results were validated by comparing with the typhoon best track data provided by the Korea Meteorological Administration (KMA) and Japan Meteorological Agency (JMA), and also by using infrared images of Himawari-8 satellite. The initial center position of the typhoon was determined by using VH polarization, thereby reducing the possibility of error. The detected center showed a difference of 23.76 km on average with the best track data of the four typhoons provided by the KMA and JMA. Compared to the typhoon center estimated by Himawari-8 satellite, the results showed an average spatial variation of 11.80 km except one typhoon located near land with a large difference of 58.73 km. This result suggests that high-resolution SAR images can be used to estimate the center and retrieve sea surface wind around typhoons.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.72-81
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• 2022

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.

• 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 ${\leq}25m\;s^{-1}$.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.11-36
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• 2021

The solar annual (Sa) and semiannual (Ssa) tides account for much of the non-uniform annual and seasonal variability observed in sea levels. These non-equilibrium tides depend on atmospheric variations, forced by changes in the Sun's distance and declination, as well as on hydrographic conditions. Here we employ tidal harmonic analyses to calculate Sa and Ssa harmonic constants for 21 Korean coastal tidal stations (TS), operated by the Korea Hydrographic and Oceanographic Agency. We used 19 year-long (1999 to 2017) 1 hr-interval sea level records from each site, and used two conventional harmonic analysis (HA) programs (Task2K and UTide). The stability of Sa harmonic constants was estimated with respect to starting date and record length of the data, and we examined the spatial distribution of the calculated Sa and Ssa harmonic constants. HA was performed on Incheon TS (ITS) records using 369-day subsets; the first start date was January 1, 1999, the subsequent data subset starting 24 hours later, and so on up until the final start date was December 27, 2017. Variations in the Sa constants produced by the two HA packages had similar magnitudes and start date sensitivity. Results from the two HA packages had a large difference in phase lag (about 78°) but relatively small amplitude (<1 cm) difference. The phase lag difference occurred in large part since Task2K excludes the perihelion astronomical variable. Sensitivity of the ITS Sa constants to data record length (i.e., 1, 2, 3, 5, 9, and 19 years) was also tested to determine the data length needed to yield stable Sa results. HA results revealed that 5 to 9 year sea level records could estimate Sa harmonic constants with relatively small error, while the best results are produced using 19 year-long records. As noted earlier, Sa amplitudes vary with regional hydrographic and atmospheric conditions. Sa amplitudes at the twenty one TS ranged from 15.0 to 18.6 cm, 10.7 to 17.5 cm, and 10.5 to 13.0 cm, along the west coast, south coast including Jejudo, and east coast including Ulleungdo, respectively. Except at Ulleungdo, it was found that the Ssa constituent contributes to produce asymmetric seasonal sea level variation and it delays (hastens) the highest (lowest) sea levels. Comparisons between monthly mean, air-pressure adjusted, and steric sea level variations revealed that year-to-year and asymmetric seasonal variations in sea levels were largely produced by steric sea level variation and inverted barometer effect.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.364-365
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• 2018

선박의 안전운항을 위해 해양기상 및 수로환경정보는 필수정보로서 현재 Weather Fax나 수로서지를 통해 정보를 활용하고 있다. e-Navigation 전략이행계획에는 해사서비스목록(MSP)을 정의 하였으며, 해양기상 및 수로환경정보를 전자 데이터로 서비스 하기 위해 서비스 개념과 기술 사양을 개발 중이다. 한국형 e-Navigation 서비스 개발 연구에서는 기상청과 국립해양조사원에서 생산하는 데이터를 이용하고 S-100 기반의 국제표준에 따른 데이터 서비스 기술을 개발하고 있다. 본 연구에서는 한국형 e-Navigation 서비스 아키텍처와 개발 기술을 기반으로 해양기상 및 수로환경정보 서비스 운영 개념을 설계 하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.156-158
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• 2019

S-126 해양물리환경 국제표준에 대하여 S-101 전자해도 표준과 중첩하여 ECDIS(Electronic Chart Display and Information System) 상에 표현하는 방안에 대하여 연구하였다. 수로서지의 텍스트 기반 자료 형태인 해양물리현상을 기호화하여 전자해도에 중첩하여 표현함으로써 S-126 표준 정보를 ECDIS 사용자에게 보다 명확하게 전달하고, 그 결과 선박사고 및 안전항행에 기여할 것으로 기대된다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.587-601
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• 2020

The ocean circulation was simulated in the East Sea and Ulleungdo-Dokdo region using ROMS (Regional Ocean Modeling System) model. By adopting the East Sea 3 km model and the HYCOM 9 km data, Ulleungdo 1 km model and Ulleungdo-Dokdo 300 m model were constructed with one-way grid nesting method. During the model development, a correction method was proposed for the distortion of the open boundary data which may be caused by the bathymetry data difference between the mother and child models and the interpolation/extrapolation method. Using this model, a super-high resolution ocean circulation with a horizontal resolution of 300 m near the Ulleungdo and Dokdo region was simulated for year 2018. In spite of applying the same conditions except for the initial and boundary data, the numerical models result indicated significantly different characteristics in the study area. Therefore, these results were compared and verified by using the surface current data estimated by satellites altimeter data and temperature data from NIFS (National Institute of Fisheries Science). They suggest that in general, the improvement of the one-way grid nesting with the HYCOM data on RMSE, Mean Bias, Pattern correlation and Vector correlation is greater in 300 m model than in the 1 km model. However, the nesting results of using East Sea 3 km model showed that simulations of the 1 km model were better than 300 m model. The models better resolved distinct ridge/trough structures of isotherms in the vertical sections of water temperature when using the higher horizontal resolution. Furthermore, Karman vortex street was simulated in Ulleungdo-Dokdo 300 m model due to the terrain effect of th islands that was not shown in the Ulleungdo 1 km model.