• Ocean and Polar Research
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• v.31 no.4
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• pp.369-377
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• 2009

Performance of the Korea Ocean Research and Development Institute (KORDI) operational storm surge prediction system for the Korean coast is presented here. Results for storm surge hindcasts and forecasts calculations were analyzed. The KORDI storm surge system consists of two important components. The first component is atmospheric models, based on US Army Corps of Engineers (CE) wind model and the Weather Research and Forecasting (WRF) model, and the second components is the KORDI-storm surge model (KORDI-S). The atmospheric inputs are calculated by the CE wind model for typhoon period and by the WRF model for non-typhoon period. The KORDI-S calculates the storm surges using the atmospheric inputs and has 3-step nesting grids with the smallest horizontal resolution of ${\sim}$300 m. The system runs twice daily for a 72-hour storm surge prediction. It successfully reproduced storm surge signals around the Korean Peninsula for a selection of four major typhoons, which recorded the maximum storm surge heights ranging from 104 to 212 cm. The operational capability of this system was tested for forecasts of Typhoon Nari in 2007 and a low-pressure event on August 27, 2009. This system responded correctly to the given typhoon information for Typhoon Nari. In particular, for the low-pressure event the system warned of storm surge occurrence approximately 68 hours ahead.

• Journal of the Korean Society of Marine Environment & Safety
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• v.6 no.2
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• pp.47-56
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• 2000

This paper describes a simple prediction method of beach recession induced by storm surge. In order to evaluate the severest beach erosion, it is assumed that maximum beach recession occurs at the coming of storm surge overlapped with spring tide. Consequently, total surge lev디 becomes the sum of storm surge level and tidal range. Generally, storm surge level around Korea is small compared with tidal range. Therefore total surge can be expressed as the series of surges, which have same duration as tide. Through the case studies, the author Investigates correlation between tidal range, duration, wave condition, beach slope and beach recession.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.1
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• pp.93-100
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• 2008

A multi-nesting grid storm surge model, Korea Ocean Research and Development Institute-Storm surge model, was calibrated to simulate storm surges. To check the performance of this storm surge model, a series of numerical experiments were explored including tidal calibration, the influence of the open boundary condition, the grid resolutions, and typhoon paths on the surge heights using the typhoon Maemi, which caused a severe coastal disasters in Sep. 2003. In this study the meteorological input data such as atmospheric pressure and wind fields were calculated using CE wind model. Total 11 tidal gauge station records with 1-minute interval data were compared with the model results and the storm surge heights were successfully simulated. The numerical experiments emphasized the importance of meteorological input and fine-mesh grid systems on the precise storm surge prediction. This storm surge model could be used as an operational storm surge prediction system after more intensive verification.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.12-18
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• 2005

Typhoon Maemi landed on the southeast coast of Korea and caused a severe storm surge in Jinhae Bay and Masan Bay. The tide gage in Masan Port recorded the storm surge of a maximum of more than 2m and the area of more than 700m from the Seo Hang Wharf was flooded by the storm surge. They had not met such an extremely severe storm surge since the opening of the port. Then storm surge was hindcasted with a numerical model. The typhoon pressure was approximated by Myers' empirical model and super gradient wind around the typhoon eye wall was considered in the wind estimation. The land topography surrounding Jinhae Bay and Masan Bay is so complex that the computed wind field was modified with the 3D-MASCON model. The motion of seawater due to the atmospheric forces was simulated using a one-layer model based on non-linear long wave approximation. The Janssen's wave age dependent drag coefficient on the sea surface was calculated in the wave prediction model WAM cycle 4 and the coefficient was inputted to the storm surge model. The result shows that the storm surge hindcasted by the numerical model was in good agreement with the observed one.

• Atmosphere
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• v.16 no.4
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• pp.259-267
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• 2006

In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.242-242
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• 2017

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.841-849
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• 2008

Natural disaster such as inundation due to the typhoon induced storm-surge has inflicted severe losses on the coastal area. The problem of global warming and sea surface rising has issued and thus influences the increase of frequency and potential power of storm-surge. What we can do is to make intelligent effort to predict and prevent the losses through the early warning and prevention activity from the accurate prediction and forecasting about the time-varying storm-surge height and its arriving time resulted from the numerical simulation with sea observations. In this paper, we developed the web service based GIS-Enabled storm-surge visualization system to predict and prevent the storm-surge disasters. Moreover. for more accurate topography around coastal area and fine-grid storm-surge numerical model, we have accomplished GIS-based coastal mapping through LiDAR measurement.

• Journal of Ocean Engineering and Technology
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• v.27 no.4
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• pp.33-44
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• 2013

IIn this study, a wave-surge-tide coupling numerical model was developed to consider nonlinear interaction. Then, this model was applied and calculations were made for a storm surge on the southeast coast. The southeast coast was damaged by typhoon "Maemi" in 2003. In this study, we used a nearshore wind wave model called SWAN (Simulating WAves Nearshore). In addition, the Meyer model was used for the typhoon model, along with an ocean circulation model called POM (Princeton Ocean Model). The wave-surge-tide coupling numerical model could calculate exact parameters when each model was changed to consider the nonlinear interaction.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.892-903
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• 2020

The rapid and accurate prediction of storm-surge height during typhoon attacks is essential in responding to coastal disasters. Most methods used for predicting typhoon data are based on numerical modeling, but numerical modeling takes significant computing resources and time. Recently, various studies on the expeditious production of predictive data based on artificial intelligence have been conducted, and in this study, artificial intelligence-based storm-surge height prediction was performed. Several learning data were needed for artificial intelligence training. Because the number of previous typhoons was limited, many synthesized typhoons were created using the tropical cyclone risk model, and the storm-surge height was also generated using the storm surge model. The comparison of the storm-surge height predicted using artificial intelligence with the actual typhoon, showed that the root-mean-square error was 0.09 ~ 0.30 m, the correlation coefficient was 0.65 ~ 0.94, and the absolute relative error of the maximum height was 1.0 ~ 52.5%. Although errors appeared to be somewhat large at certain typhoons and points, future studies are expected to improve accuracy through learning-data optimization.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.37-43
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• 2018

The storm surge is caused by an typhoons and it is not easy to predict the location, strength, route of the storm. Therefore, research using a scenario for storms occurrence has been conducted. In Korea, hazard maps for various scenarios were produced using the storm surge numerical simulation. Such a method has a disadvantage in that it is difficult to predict when other scenario occurs, and it is difficult to cope with in real time because the simulation time is long. In order to compensate for this, we developed a method to predict the storm surge damage by using research database. The risk grade prediction for the storm surge was performed predominantly in the study area of the East coast. In order to estimate the equation, COMSOL developed by COMSOL AB Corporation was utilized. Using some assumptions and limitations, the form of the basic equation was derived. the constants and coefficients in the equation were estimated by the trial and error method. Compared with the results, the spatial distribution of risk grade was similar except for the upper part of the map. In the case of the upper part of the map, it was shown that the resistance coefficient, k was calculated due to absence of elevation data. The SIND model is a method for real-time disaster prediction model and it is expected that it will be able to respond quickly to disasters caused by abnormal weather.