• 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.

• Ocean and Polar Research
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• v.33 no.4
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• pp.497-506
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• 2011

The rapid industrial and technological development has made the human activities for the utilization of marine resources more complex. Marine spatial management is a space-based approach. It is a comprehensive and integrated management approach. The ultimate goal of marine spatial management is the "sustainable use" of marine resources. The partial approach is applied in the existing marine spatial management, mainly coastal zones which involves integrated approach. Also this showed various limitations including restricted mostly to coastal zones, and limitation to implementation tools. However, for marine spatial management to have a reasonable approach that attaches importance to the relationship between humans and the holistic ecosystem, it is important to internalize a central principle in marine spatial management that focuses on the sustainable use of marine resources. In the present study, four central principles are proposed that will eventually be applied through marine spatial management planning tools. These principles are 1) the establishment of a cooperative decision making and planning system that is based on stakeholder participation; 2) scientific assessment of the current status and impact on the basis of ecology, sociology, and economics; 3) reasonable and optimal spatial assignment based on the forecasting of future-use characteristics and environmental changes; and 4) ascribing importance to the implementation of the results of rational planning processes.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.1
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• pp.72-81
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• 2012

Using artificial neural network (ANN) technique, auction prices for common mackerel were forecasted with the daily total sale and auction price data at the Busan Cooperative Fish Market before introducing Total Allowable Catch (TAC) system, when catch data had no limit in Korea. Virtual input data produced from actual data were used to improve the accuracy of prediction and the suitable neural network was induced for the prediction. We tested 35 networks to be retained 10, and found good performance network with regression ratio of 0.904 and determination coefficient of 0.695. There were significant variations between training and verification errors in this network. Ideally, it should require more training cases to avoid over-learning, which leads to improve performance and makes the results more reliable. And the precision of prediction was improved when environmental factors including physical and biological variables were added. This network for prediction of price and catch was considered to be applicable for other fishes.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.4
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• pp.407-438
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• 2013

Current status and future direction of air quality modeling for monitoring and forecasting air quality in East Asia were discussed in this paper. An integrated air quality modeling system, combining (1) emission processing and modeling, (2) meteorological model simulation, (3) chemistry-transport model (CTM) simulation, (4) ground-based and satellite-retrieved observations, and (5) data assimilation, was introduced. Also, the strategies for future development of the integrated air quality modeling system in East Asia was discussed in this paper. In particular, it was emphasized that the successful use and development of the air quality modeling system should depend on the active applications of the data sets from incumbent and upcoming LEO/GEO (Low Earth Orbit/Geostationary Earth Orbit) satellites. This is particularly true, since Korea government successfully launched Geostationary Ocean Color Imager (GOCI) in June, 2010 and has another plan to launch Geostationary Environmental Monitoring Spectrometer (GEMS) in 2018, in order to monitor the air quality and emissions in/around the Korean peninsula as well as over East Asia.

• Atmosphere
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• v.27 no.3
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• pp.261-276
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• 2017

The main objectives of this study are to introduce Global Seasonal forecasting system version5 (GloSea5) of KMA and to evaluate the performance of ensemble prediction of system. KMA has performed an operational seasonal forecast system which is a joint system between KMA and UK Met office since 2014. GloSea5 is a fully coupled global climate model which consists of atmosphere (UM), ocean (NEMO), land surface (JULES) and sea ice (CICE) components through the coupler OASIS. The model resolution, used in GloSea5, is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, we evaluate the performance of this system using by RMSE, Correlation and MSSS for ensemble mean values. The forecast (FCST) and hindcast (HCST) are separately verified, and the operational data of GloSea5 are used from 2014 to 2015. The performance skills are similar to the past study. For example, the RMSE of h500 is increased from 22.30 gpm of 1 week forecast to 53.82 gpm of 7 week forecast but there is a similar error about 50~53 gpm after 3 week forecast. The Nino Index of SST shows a great correlation (higher than 0.9) up to 7 week forecast in Nino 3.4 area. It can be concluded that GloSea5 has a great performance for seasonal prediction.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.449-451
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• 2009

Sea level rise and increase of the typhoon/hurricane intensity due to global warming have threaten coastal areas for residential and industrial and have been widely studied. In this study we showed our recent efforts on sea wind which is one of critical factors for safe maritime traffic and prediction for storm surges and waves. Currently, most of numerical weather models in korea do not have sufficient spatial and temporal resolutions, therefore we set up a find grid(about 9km) sea wind prediction system that predicts every 12 hours for three day using Weather Research and Forecasting(WRF). This system covers adjacent seas around korean peninsula Comparisons of two observed data, Ieodo Ocean Research station(IORS) and Yellow Sea Buoy(YSB), showed reasonable agreements and by data assimilation we will improve better accurate sea winds in near future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.129-137
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• 2016

In the present study we verified performance of feed-forward control algorithm using short term prediction of ship motion information by taking advantage of developed numerical simulation model of FPSO motion. Up until now, various studies have been conducted about thrust control and allocation for dynamic positioning systems maintaining positions of ships or marine structures in diverse sea environmental conditions. In the existing studies, however, the dynamic positioning systems consist of only feedback control gains using a motion of vessel derived from environmental loads such as current, wind and wave. This study addresses dynamic positioning systems which have feedforward control gain derived from forecasted value of a motion of vessel occurred by current, wind and wave force. In this study, the future motion of vessel is forecasted via Brown's Exponential Smoothing after calculating the vessel motion via a selected mathematical model, and the control force for maintaining the position and heading angle of a vessel is decided by the feedback controller and the feedforward controller using PID theory and forecasted vessel motion respectively. For the allocation of thrusts, the Lagrange Multiplier Method is exploited. By constructing a simulation code for a dynamic positioning system of FPSO, the performance of feedforward control system which has feedback controller and feedforward controller was assessed. According to the result of this study, in case of using feedforward control system, it shows smaller maximum thrust power than using conventional feedback control system.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• 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.