• Journal of Ocean Engineering and Technology
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• v.34 no.1
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• pp.26-36
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• 2020

An extreme value analysis of metocean data which include wave, wind, and current data is a prerequisite for the operation and survival of offshore structures. The purpose of this study was to provide information about the return wave, wind, and current values for the Barents Sea using extreme value analysis. Hindcast datasets of the Global Reanalysis of Ocean Waves 2012 (GROW2012) for a waves, winds and currents were obtained from the Oceanweather Inc. The Gumbel distribution, 2 and 3 parameters Weibull distributions and log-normal distribution were used for the extreme value analysis. The least square method was used to estimate the parameters for the extreme value distribution. The return values, including the significant wave height, spectral peak wave period, wind speed and current speed at surface, were calculated and it will be utilized to design offshore structures to be operated in the Barents Sea.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1507-1515
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• 2021

If chlorophyll-a is estimated through ocean color remote sensing, it is able to understand the global distribution of phytoplankton and primary production. However, there are missing data in the ocean color observed from the satellites due to the clouds or weather conditions. In thisstudy, the missing data of the GOCI (Geostationary Ocean Color Imager) chlorophyll-a product wasreconstructed by using DINEOF (Data INterpolation Empirical Orthogonal Functions). DINEOF reconstructs the missing data based on spatio-temporal data, and the accuracy was cross-verified by removing a part of the GOCI chlorophyll-a image and comparing it with the reconstructed image. In the study area, the optimal EOF (Empirical Orthogonal Functions) mode for DINEOF wasin 10-13. The temporal and spatialreconstructed data reflected the increasing chlorophyll-a concentration in the afternoon, and the noise of outliers was filtered. Therefore, it is expected that DINEOF is useful to reconstruct the missing images, also it is considered that it is able to use as basic data for monitoring the ocean environment.

• Journal of Environmental Science International
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• v.17 no.12
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• pp.1353-1361
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• 2008

Quality control of Argo(Array for Real-time Geostrophic Oceanography) data is crucial by reason that salinity measurements are liable to experience some drift and offset due to biofouling, contamination of sensor and wash-out of biocide. The automated Argo real-time quality control has a limit of sorting data quality, so that WJO program is adopted as standardized method of Argo delayed mode quality control (DMQc) in the world that is a precise quality control method. We conducted DMQC on pressure, temperature and salinity measured by Argo floats in the Pacific Ocean including expert evaluation. Particularly, salinity data were corrected using WJO program. 4 salinity profiles of Argo delayed mode were compared with nearby in situ CTD data and other Argo data in deep layer where oceanographic conditions are stable in time and space. The differences of both salinities were lower than target accuracy of Argo. As compared with the difference of salinities before DMQC, those after DMQC decreased by 60-80 percent. Quality of delayed mode salinity data seemed to be improved correcting salinity data suggested by WJO program.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.590-593
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• 2004

Extraction of ocean surface current velocity offers important physical oceanographic parameters especially on understanding ocean environment. Although Remote Sensing techniques were highly developed, the investigation of ocean surface current using Synthetic Aperture Radar (SAR) is not an easy task. This paper presents the results of ocean surface current observation using Doppler Centroid of ERS-1 SAR data obtained off the coast of Korea peninsula. We employed the concept, in which Doppler frequency shift and the ocean surface current are closely related, to evaluate ocean surface current. Moving targets cause Doppler frequency shift of the back scattered radar waves of SAR, thus the line-of-sight velocity component of the scatters can be evaluated. The Doppler frequency shift can be measured by estimating the difference between Doppler Centroid of raw SAR data and reference Doppler Centroid. Theoretically, the Doppler Centroid is zero; however, squinted antenna which is affected by several physical factors causes Doppler Centroid to be nonzero. The reference Doppler Centroid can be obtained from measurements of sensor trajectory, attitude and Earth model. The estimated Doppler Centroid was compensated by considering the accurate attitude estimation of ERS-1 SAR. We could verify the correspondence between the estimated ocean surface current and observed in-situ data in the error bound.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.105-112
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• 2006

Freak waves in the ocean are recently drawing much attention as a natural disaster to ocean structures and navigating ships as well. Several observation data, among them the Draupner New Year Wave, show the very impressive feature of Freak waves whose wave height is up to three times as high as the significant wave height of surrounding waves, In addition, Freak wave appears as an isolated very high crest in somewhat stationary random waves of same order in their wavelengths. Bearing such characteristics in mind, one notices its extraordinary steepness. This strongly suggests that Freak wave is not long lived but transient nature on the whole. A great number of studies to explain these natures were published from both theoretical and numerical point of view. However it is not sure if they are applicable to actual ocean environment. In this paper, we deal with the results concerning abnormal and/or Freak waves from in-situ ocean wave data and point out several remarks to the problems lain behind the contributions in this context. A physical experiment is described to reinforce the subject discussed from the observation data.

• Ocean and Polar Research
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• v.42 no.2
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• pp.115-131
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• 2020

The effective range of surface current data observed by high-frequency radar (HFR) operated in the northern coastal area of Jeju Island by Korea Institute of Ocean Science and Technology was estimated and the distribution and variability of the M₂ tidal current of the Jeju Strait was analyzed. To evaluate the HFR data, the M₂ tidal current corrected from 25 hours current data observed by the Korea Hydrographic and Oceanographic Agency (KHOA) was compared with the M₂ tidal current in the Jeju Strait analyzed from the surface currents of HFR. The reliability of HFR data was confirmed by analyzing the characteristics of the tide components of these two data sets, and the effective range of HFR data was estimated through temporal and spatial analysis. The observation periods of HFR used in the analysis were from 2012 to 2014, and it was confirmed that there is a difference in the effective range of HFR data according to the observation time. During the analysis periods, the difference between the M₂ current ellipses from the data of KHOA and the HFR was greater in the eastern than in the western part of the Jeju Strait, and represented a high reliability in the western and central parts of the Jeju Strait. The tidal current of the Jeju Strait analyzed using the HFR data revealed a seasonal variability a relatively weak in summer and a strong in winter, about a 17% fluctuations between the summer and winter based on the length of the semi-major axis of tidal ellipse. Appraisals and results of regarding the characteristics and seasonal variability of the M₂ tidal current in the Jeju Strait using HFR data have not been previously reported, so the results of this study are considered meaningful.

• Journal of the Korean earth science society
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• v.40 no.4
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• pp.315-328
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• 2019

Since the Coastal Zone Color Scanner (CZCS)/Nimbus-7 was launched in 1978, a variety of studies have been conducted to retrieve ocean color variables from multi-satellites. Several algorithms and formulations have been suggested for estimating ocean color variables based on multi band data at different wavelengths. Chlorophyll-a (chl-a) concentration is one of the most important variables to understand low-level ecosystem in the ocean. To retrieve chl-a concentrations from the satellite observations, an appropriate algorithm depending on water properties is required for each satellite sensor. Most operational empirical algorithms in the global ocean have been developed based on the band-ratio approach, which has the disadvantage of being more adapted to the open ocean than to coastal areas. Alternative algorithms, including the semi-analytical approach, may complement the limits of band-ratio algorithms. As more sensors are planned by various space agencies to monitor the ocean surface, it is expected that continuous monitoring of oceanic ecosystems and environments should be conducted to contribute to the understanding of the oceanic biosphere and the impact of climate change. This study presents an overview of the past and present algorithms for the estimation of chl-a concentration based on multi-satellite data and also presents the prospects for ongoing and upcoming ocean color satellites.

• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• Proceedings of the KSRS Conference
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• v.2
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• pp.740-743
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• 2006

We identify two distinct finer-scale frontal bands: 'Mainland China Coastal Front' (MCCF) and 'Kuroshio Front' (KF). The MCCF is along the 50-m isobath with large temperature gradient. The front is a boundary between the Mainland China Coastal Current and the offshore shelf waters. On the other hand, the KF is extending from the northeastern coast of Taiwan toward the northeast and into the shelf of south ECS. It forms a broad semicircle-shape and curving along 100-m isobath, it also deviates from eastward at around 26.5N-122E and leaves the shelf of ECS. This front should be the boundary between the Kuroshio water and the other shelf waters.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.159-161
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• 2007

Geostationary Ocean Color Imager (GOCI), the world-first ocean remote sensing instrument on geostationary Communication, Ocean, Meteorological Satellite (COMS), will be able to take a picture of a large region several times a day (almost with every one hour interval). We, KORDI, are in charge for developing the GOCI data processing system (GDPS) which is the basic software for processing the data from GOCI. The GDPS will be based on windows operating system to produce the GOCI level 2 data products (useful for oceanographic environmental analysis) automatically in real-time mode. Also, the GDPS will be a user-interactive program by well-organized graphical user interfaces for data processing and visualization. Its products will be the chlorophyll concentration, amount of total suspended sediments (TSS), colored dissolved organic matters (CDOM) and red tide from water leaving radiance or remote sensing reflectance. In addition, the GDPS will be able to produce daily products such as water current vector, primary productivity, water quality categorization, vegetation index, using individual observation data composed from several subscenes provided by GOCI for each slit within the target area. The resulting GOCI level 2 data will be disseminated through LRIT using satellite dissemination system and through online request and download systems. This software is carefully designed and implemented, and will be tested by sub-contractual company until the end of this year. It will need to be updated in effect with respect to new/improved algorithms and the calibration/validation activities.