• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2008.09a
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• pp.200-203
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• 2008

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2008.09b
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• pp.200-203
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• 2008

• Journal of Ocean Engineering and Technology
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• v.13 no.2 s.32
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• pp.116-128
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• 1999

A deep water wave prediction model applicable to the East Sea is presnted. This model incorporates rediative transter of energy specrum, atmospheric input form the wind, nonlinear interaction, and energy dissipation by white capping. The propagation scheme by Gadd shows satisfactory results and the characteristics of the nonlinear interaction is simulated well by discrete interaction approximatiion. The application of the model to the sea around the Korean Peninsula shows reasonable agreement with the observation.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.8 no.3
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• pp.262-273
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• 2003

We have analyzed the characteristics of the monthly mean sea surface winds and wind waves near the Korean marginal seas in the 2002 year on the basis of prediction results of the sea surface winds from MM5/KMA model, which is being used for the operation system at the Korea Meteorological Administration and the third generation wave model, WAVEWATCH-III. which takes the sea surface winds derived from MM5/KMA model as the initial data. Statistical comparisons have been applied with both the marine meteorological observation buoy and the TOPEX/POSEIDON satellite wave heights data to verify the model results. The correlation coefficients between the models and observation data reach up to about 60-80％, supporting that these models satisfactorily simulate the sea surface winds and wave heights even at the coastal regions except for Chilbal-Do located very close to the land. Based on these verification results, the distributions of monthly mean sea surface winds, significant wave heights, wave lengths and wave periods around the Korean marginal seas during 2002 year have been represented.

• Atmosphere
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• v.16 no.4
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• pp.303-318
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• 2006

A parameterization of gravity wave drag induced by cumulus convection (GWDC) proposed by Chun and Baik is implemented in the KMA operational global NWP model (GDAPS), and effects of the GWDC on the forecast for July 2005 by GDAPS are investigated. The forecast result is compared with NCEP final analyses data (FNL) and model's own analysis data. Cloud-top gravity wave stresses are concentrated in the tropical region, and the resultant forcing by the GWDC is strong in the tropical upper troposphere and lower stratosphere. Nevertheless, the effect of the GWDC is strong in the mid- to high latitudes of Southern Hemisphere and high latitudes of Northern Hemisphere. By examining the effect of the GWDC on the amplitude of the geopotential height perturbation with zonal wavenumbers 1-3, it is found that impact of the GWDC is extended to the high latitudes through the change of planetary wave activity, which is maximum in the winter hemisphere. The GWDC reduces the amplitude of zonal wavenumber 1 but increases wavenumber 2 in the winter hemisphere. This change alleviates model biases in the zonal wind not only in the lower stratosphere where the GWDC is imposed, but also in the whole troposphere, especially in the mid- to high latitudes of Southern Hemisphere. By examining root mean square error, it is found that the GWDC parameterization improves GDAPS forecast skill in the Southern Hemisphere before 7 days and partially in the Northern Hemisphere after about 5 days.

• Atmosphere
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• v.17 no.1
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• pp.1-15
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• 2007

A constraint based on climatology is introduced to the cost function of the three-dimensional variational assimilation (3dVar) to correct the error of the zonal mean wind structure in the global data assimilation system at Korea Meteorological Administration (KMA). The revised cost function compels the analysis fit to the chosen climatology while keeping the balance between the variables in the course of analysis. The constraint varies selectively with the vertical level and the horizontal scale of the motion. The zonally averaged wind field from European Centre for Medium-Range Weather Forecasts Re-Analysis 40 (ERA-40) is used as a climatology field in the constraint. The constraint controls only the zonally averaged stratospheric long waves with total wave number less than 20 to fix the error of the large scale wind field in the stratosphere. The constrained 3dVar successfully suppresses the erroneous westerly in the stratospheric analysis promptly, and has been applied on the operational global 3dVar system at KMA.

• Journal of Ocean Engineering and Technology
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• v.25 no.2
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• pp.108-112
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• 2011

When oil spills occur in the ocean because of a ship collision or grounding, the oil in the sea will spread to the coastline. To effectively and promptly prevent such an oil spread, the prediction of the direction and speed of the spreading oil must be made. By applying the coastal wave diffusion theory with a consideration of the effects of wind and current, the oil spreading direction and speed can be predicted promptly so that the National Disaster Prevention System can effectively and promptly take countermeasures against the attack and contamination of the coastline by such oil bands.

• Atmosphere
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• v.31 no.4
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• pp.395-404
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• 2021

In order to investigate downslope windstorm by using more detailed observation, we observed 6 cases at 3 sites - Inje, Yongpyeong, and Bukgangneung - during "3-D Meteorological Observation Project in Yeongdong region of Gangwon province, South Korea in 2020." The results from analysis of the project data were as follows. First, AWS data showed that a subsidence inversion layer appeared in 800~700 hPa on the windward side and 900~850 hPa on the leeward side. Second, before strong wind occurred, the inversion layer had descended to about 880~800 hPa. Third, with mountain wave breaking, downslope wind was intensified at the height of 2~3 km above sea level. After the downslope wind began to descend, the subsidence inversion layer developed. When the subsidence inversion layer got close to the ground, wind peak occurred. In general, UM (Unified Model) GDAPS (Global Data Assimilation Prediction System) have had negative bias in wind speed around peak area of Taebaek mountain range, and positive bias in that of East Sea coast area. The stronger wind blew, the larger the gap between observed and predicted wind speed by GDAPS became. GDAPS predicted strong p-velocity at 0600 LST 25 Apr 2020 (4^th case) and weak p-velocity at 2100 LST 01 Jun 2020 (6^th case) on the lee-side of Taebaek mountain range near Yangyang. As hydraulic jump theory was proved, which is known as a mechanism of downslope windstorm in Yeongdong region, it was confirmed that there is a relationship between p-velocity of lee-side and wind speed of eastern slope of Taebaek mountain range.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.5
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• pp.223-233
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• 2018

The predictability of winter storm waves using KMA's operational wind forecasts has been studied to predict wind waves and swells in the East coast of Korea using SWAN. The nested model were employed along the East coast of Korea to simulate the wave transformation in the coastal area and wave dissipation term of whitecapping is optimized to improve swell prediction accuracy. In this study, KMA's operational meteorological models (RDAPS and LDAPS) are used as input wind fields. In order to evaluate model accuracy, we also simulate wind waves and swells using ECMWF reanalysis and KIOST WRF wind and they are compared with the KMA's operational wave model and the wave measurement data on the offshore and onshore stations. As a result, it has the lowest RMSE and the highest correlation coefficient in the onshore when the input wind fields are KMA's operational meteorological forecasts. In the offshore, all of the simulate results shows good agreements with similar error statistics. It means that it is very feasible to use SWAN model with the modified whitecapping factor and KMA's operational meteorological forecasts for predicting the wind waves and swells in the East coast of Korea.

• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.