• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2009.10a
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• pp.168-171
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• 2009

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2001.08a
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• pp.219-225
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• 2001

• Journal of the Korean earth science society
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• v.24 no.1
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• pp.7-21
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• 2003

The state-of-art third generation wave prediction model WAM was applied to the Korean seas for a winter monsoon period of January 1997. The wind field used in the present study is the global NSCAT-ERS/NCEP blended winds, which was further interpolated using a bi-cubic spline interpolator to fine grid limited area shallow water regime surrounding the Korean seas. To evaluate and investigate the accuracy of WAM, the hindcasted wave heights are compared with observed data from two shallow water buoys off Chil-Bal and Duk-Juk. A detailed study has been carried with the various meteorological parameters in observed buoy data and its inter-dependency on model computed wave fields was also investigated. The RMS error between the observation and model computed wave heights results to 0.489 for Chil-Bal and 0.417 for Duk-Juk. A similar comparison between the observation and interpolated winds off Duk-Juk show RMS error of 2.28 which suggest a good estimate for wave modelling studies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.3
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• pp.133-139
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• 1987

Three disastrous floods experienced over the tidal reaches of the Han River are investigated numerically with the use of one-dimensional finite-difference tidal flow model. The hindcast scheme involves processing flood hydrograph at Indogyo to provide the necessary discharge variation at upstream boundary and predicting tidal elevations at downstream boundary that computes the response in terms of flood levels and discharges within the tidal reaches. Computed results of 1925, 1972 and 1984 floods are presented and discussed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.5
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• pp.306-315
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• 2010

As developing the large-scale offshore wind farm is expected, the preliminary environmental impact assessment is very essential. In this study, the wave hindcast model is verified based on observed data at the coast around Wido which is among the candidate sites for developing the offshore wind farm. In addition, the effect of the wind turbine foundations on wave height is analyzed when total 35 wind turbines including monopile foundations of 5 m in diameter are installed. Calculation result of significant wave height is in good accord with observed data since the RMS error is 0.35 m. Moreover, it is found that the presence of the wind turbine foundations hardly affects wave height as wave damping ratio is less than 1%.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.20-27
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• 2011

A system for fine-resolution long-range weather forecast is introduced in this study. The system is basically consisted of a global-scale coupled general circulation model (CGCM) and Weather Research and Forecast (WRF) regional model. The system makes use of a data assimilation method in order to reduce the initial shock or drift that occurs at the beginning of coupling due to imbalance between model dynamics and observed initial condition. The long-range predictions are produced in the system based on a non-linear ensemble method. At the same time, the model bias are eliminated by estimating the difference between hindcast model climate and observation. In this research, the predictability of the forecast system is studied, and it is illustrated that the system can be effectively used for the high resolution long-term weather prediction. Also, using the system, fine-resolution climatological data has been produced with high degree of accuracy. It is proved that the production of agrometeorological variables that are not intensively observed are also possible.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.451-461
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• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2000.09a
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• pp.37-46
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• 2000

한반도 주변해역은 고조위와 태풍의 내습에 의한 강한 파랑으로 특징지워진다. 저조위때에 나타나는 1700$\textrm{km}^2$에 달하는 갯벌로 이루어진 서해안과 황해 동쪽 지역의 해안의 조건은 경기만에 l0m 범위의 높은 조위 차와 동계 계절풍과 태풍으로 인한 4-5m에 달하는 높은 파고를 발생시키는 고에너지와 높은 탁도 환경을 만들어 내는 매개체가 된다. (중략)

• Atmosphere
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• v.25 no.2
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• pp.323-337
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• 2015

This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The model's prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Monsoon (WNPM) index that exhibits a prediction skill up to 4- to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members.