• 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.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.33.2-33.2
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• 2008

Korea Astronomy and Space Science Institute (KASI) is developing an empirical model for Korean Space Weather Prediction Center (KSWPC). This model predicts the geomagnetic storm strength (Dst minimum) by using only CME parameters, such as the source location (L), speed (V), earthward direction (D), and magnetic field orientation of an overlaying potential field at CME source region. To derive an empirical formula, we considered that (1) the direction parameter has best correlation with the storm strength (2) west $15^{\circ}$ offset from the central meridian gives best correlation between the source location and the storm strength (3) consideration of two groups of CMEs according to their magnetic field orientation (southward or northward) provide better forecast. In this talk, we introduce current status of the empirical storm prediction model development.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.1
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• pp.17-23
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• 2009

The purpose of this study is possibility of the heavy rainfall prediction using instability index. The convective instability index using this study is Convective Available Potential Energy(CAPE) concerned the growth energy of the storm, Bulk Richardson Number(BRN) concerned the type and strength of the storm, and Sotrm Relative Helicity(SRH) concerned maintenance of the storm. To verify the instability index, the simulation of heavy rainfall case experiment by Numerical Weather Prediction(NWP) model(MM5) are designed. The results of this study summarized that the heavy rainfall related to the high instability index and the proper combination of one more instability index made the higher heavy rainfall prediction.

• 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.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.271-278
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• 2011

Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.

• 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 Advanced Marine Engineering and Technology
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• v.33 no.4
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• pp.595-602
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• 2009

In this study, a two-dimensional storm surges/tide prediction model called the Storm surges/Tide Operational Model (STORM) was applied as the operational forecast model of the Korea Meteorological Administration (KMA). The operational model results were verified for two years (2006-2007) using observed results from tidal stations. Comparisons of modeled and observed storm surges show that larger differences at the western coast of Korea than at the southern and eastern coasts. The averaged root mean square error between the modeled and observed storm surges height are 0.16 m and 0.10 m in 2006 and 2007, respectively.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.6
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• pp.361-373
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• 2010

Analysis has been made on the tide/storm surges characteristics near the Korean marginal seas in the 2008 and 2009 years using operational ocean prediction model of the Korea Meteorological Administration(KMA). In order to evaluate its performance, its results were compared with the observed data by tidal stations around Korean Peninsula. The model used in this study predicts very well the characteristics of tide/storm surges near the Korean Peninsula. Simulated storm surges show the evident effects of Typhoons in summer season. The averaged root mean square error(RMSE) of 48 hr forecasting between the modeled and observed storm surges are 0.272 and 0.420 m in 2008 and 2009, respectively. Due to strong sea winds, the highest storm surges heights was found in summer season of 2008, however, in 2009, the high storm surges heights was also found in other seasons. When Typhoon Kalmaegi(2008) and Morokot(2009) approached to Korean Peninsular, the accuracy of model predictions is almost same as annual mean value but the precision accuracy for Typhoon Morakot is lower than of Typhoon Kalmaegi similar to annual results.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.1-9
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• 2024

In this paper, we propose BRAIDS, a boot storm mitigation plan consisting of an AI-based VDI usage prediction system and a virtual machine boot scheduler system, to alleviate boot storms and improve service stability. Virtual Desktop Infrastructure (VDI) is an important technology for improving an organization's work productivity and increasing IT infrastructure efficiency. Boot storms that occur when multiple virtual desktops boot simultaneously cause poor performance and increased latency. Using the xgboost algorithm, existing VDI usage data is used to predict future VDI usage. In addition, it receives the predicted usage as input, defines a boot storm considering the hardware specifications of the VDI server and virtual machine, and provides a schedule to sequentially boot virtual machines to alleviate boot storms. Through the case study, the VDI usage prediction model showed high prediction accuracy and performance improvement, and it was confirmed that the boot storm phenomenon in the virtual desktop environment can be alleviated and IT infrastructure can be utilized efficiently through the virtual machine boot scheduler.