• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.369-378
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• 2006

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

• 대기
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• 제17권2호
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• pp.207-216
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• 2007

The Moving-nest Typhoon Model (MTM) was installed on the Korea Meteorological Administration (KMA)'s CRAY X1E in 2006 and started its test operation in August 2006 to provide track and intensity forecasts of tropical cyclones. In this study, feasibility of the MTM forecast is compared with the Global Data Assimilation and Prediction System (GDAPS) of the KMA and the operational typhoon forecast models in the Japan Meteorological Agency (JMA), from the sixth tropical cyclone to the twentieth in 2006. Forecast skills in terms of the storm position error of the two KMA models were comparable, but MTM showed a slightly better ability. While both GDAPS and MTM produced larger errors than JMA models in track forecast, the predicted intensity was much improved by MTM, making it comparable to the JMA's typhoon forecast model. It is believed that the Geophysical Fluid Dynamics Laboratory (GFDL) bogus initialization method in MTM improves the ability to forecast typhoon intensity.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.9-17
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• 2013

Purpose: The goal of this study was to develop a methodology for the demand forecast of tractor, riding type rice transplanter and combine harvester using an ARIMA (autoregressive integrated moving average) model, one of time series analysis methods, and to forecast their demands from 2012 to 2021 in South Korea. Methods: To forecast the demands of three kinds of machines, ARIMA models were constructed by following three stages; identification, estimation and diagnose. Time series used were supply and stock of each machine and the analysis tool was SAS 9.2 for Windows XP. Results: Six final models, supply based ones and stock based ones for each machine, were constructed from 32 tentative models identified by examining the ACF (autocorrelation function) plots and the PACF (partial autocorrelation function) plots. All demand series forecasted by the final models showed increasing trends and fluctuations with two-year period. Conclusions: Some forecast results of this study are not applicable immediately due to periodic fluctuation and large variation. However, it can be advanced by incorporating treatment of outliers or combining with another forecast methods.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• 한국수자원학회논문집
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• 제46권12호
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• pp.1249-1263
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• 2013

장기간의 가뭄에 의한 피해를 최소화하기 위해서는 유역에 적합한 가뭄관리 대책의 수립과 함께 미래에 발생하게 될 가뭄을 미리 예측할 수 있는 기술이 구축되어야 한다. 또한 미래의 가뭄에 대한 합리적 대응 방안을 수립하기 위해서는 가뭄의 지속기간(duration)과 심도(severity)의 정량적인 예측이 선행되어야 한다. 본 연구에서는 수문 시계열의 예측에 가장 많이 이용되고 있는 대표적인 통계학적 기법인 인공신경망 모형(Artificial Neural Network Model)과 가뭄지수를 이용하여 남한지역의 서울, 대전, 대구, 광주 등의 4개 기상관측소를 선정하여 가뭄예측을시도하였다. 가뭄 예측을 위하여 남한지역 내 선정한 기상관측소의 관측된 과거 강수량 자료를 이용하여 산정된 SPI (Standardized Precipitation Index)를 입력변수로 하여 다층 퍼셉트론(Multi Layer Perceptron) 인공신경망 모델에 적용하였으며, 매개변수 보정을 위한 학습기간으로 1976~2000년과 2001~2010년을 예측을 위한 검증기간으로 선정하여, 학습 및 예측을 시도하였다. 학습된 최적의 예측모형을 이용하여 서로 다른 선행예보시간(1~6개월)을 갖고 SPI (3), SPI (6), SPI (12)별로 가뭄을 예측하였으며, 가뭄예측 결과, SPI (3)의 경우에는 1개월 선행예보에서만 좋은 결과를 나타내었으며, SPI (6)의 경우 1~3개월 후의 가뭄을 예측하는 경우에 비교적 관측자료와 잘 일치하는 결과를 나타내었다. SPI (12)의 경우에는 약5개월 후까지의 가뭄예측에 양호한 결과를 나타내었다.

• 한국지구과학회지
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• 제36권2호
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• pp.158-170
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• 2015

큰 에디 모의과정을 포함한 WRF 모델 (WRF-LES)을 이용하여 수치모델의 수평공간 규모에 따른 대기경계층 모수화 실험과 LES 모의 결과를 지표층 근처의 풍속 예측에 대하여 비교하였다. 수치실험은 복잡한 산악지형과 해안지역을 포함하는 강원도 지역에서 수평해상도 1 km와 333 m 실험을 수행하였다. 수평해상도 1 km 실험은 대기경계층 모수화 방안을 채택하였으며, 333 m 실험에서는 LES를 이용하였다. 복잡한 산악지역에서의 풍속 예측의 정확성은 수평해상도 1 km 실험 보다 333 m 실험에서 향상되었으며 해안지역에서는 1 km 실험에서 관측과 더 일치하였다. 지표층 근처의 큰 난류를 직접 계산하는 LES 실험은 산악지역의 풍속예측 개선에 기여하였다.

• 대기
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• 제18권3호
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• pp.237-248
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• 2008

A thermal index which considers metabolic heat generation of human body is proposed for operational forecasting. The new thermal index, Perceived Temperature (PT), is forecasted using Weather Research and Forecasting (WRF) mesoscale model and validated. Forecasted PT shows the characteristics of diurnal variation and topographic and latitudinal effect. Statistical skill scores such as correlation, bias, and RMSE are employed for objective verification of PT and input meteorological variables which are used for calculating PT. Verification result indicates that the accuracy of air temperature and wind forecast is higher in the initial forecast time, while relative humidity is improved as the forecast time increases. The forecasted PT during 2007 summer is lower than PT calculated by observation data. The predicted PT has a minimum Root-Mean-Square-Error (RMSE) of $7-8^{\circ}C$ at 9-18 hour forecast. Spatial distribution of PT shows that it is overestimated in western region, while PT in middle-eastern region is underestimated due to strong wind and low temperature forecast. Underestimation of wind speed and overestimation of relative humidity have caused higher PT than observation in southern region. The predicted PT from the mesoscale model gives appropriate information as a thermal index forecast. This study suggests that forecasted PT is applicable to the prediction of health warning based on the relationship between PT and mortality.

• 대기
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• 제21권2호
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• pp.163-172
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• 2011

Numerical forecasting depends on the initial condition error strongly because numerical model is a chaotic system. To calculate the sensitivity of some forecast aspects to the initial condition in the Korea Meteorological Administration (KMA) Unified Model (UM) which is originated from United Kingdom (UK) Meteorological Office (MO), an algorithm to calculate adjoint sensitivities is developed by modifying the adjoint perturbation forecast model in the KMA UM. Then the new algorithm is used to calculate adjoint sensitivity distributions for typhoon DIANMU (201004). Major initial adjoint sensitivities calculated for the 48 h forecast error are located horizontally in the rear right quadrant relative to the typhoon motion, which is related with the inflow regions of the environmental flow into the typhoon, similar to the sensitive structures in the previous studies. Because of the upward wave energy propagation, the major sensitivities at the initial time located in the low to mid- troposphere propagate upward to the upper troposphere where the maximum of the forecast error is located. The kinetic energy is dominant for both the initial adjoint sensitivity and forecast error of the typhoon DIANMU. The horizontal and vertical energy distributions of the adjoint sensitivity for the typhoon DIANMU are consistent with those for other typhoons using other models, indicating that the tools for calculating the adjoint sensitivity in the KMA UM is credible.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2011년도 학술발표회
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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.

• 한국항만경제학회지
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• 제32권4호
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• pp.1-13
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• 2016

본 연구는 System Dynamics를 이용하여 선사 컨테이너 인벤토리의 수요를 장비 Type/size별 예측, Port별 예측, Weekly 예측을 통해 보다 정교한 예측모델을 구축하는 것을 연구의 목적으로 하였다. 예측은 중국의 상하이항과 얀티안항을 대상으로 하였다. 컨테이너 인벤토리는 수요가 많고 유효한 데이터를 산출할 수 있는 Dry 컨테이너 20', 40', High cube 40'으로 한정하였다. 시뮬레이션 기간은 2011년-2017년이며, 선사에서 실제 예측하는 단위인 Weekly 데이터를 활용하였다. 모델의 정확도 검증을 위해 절대비율 평균오차(MAPE)를 적용한 결과 상하이 Dry 40' 수요, 상하이 Dry High cube 40' 수요, 상하이 Dry 20' 공급, 상하이 Dry 40' 공급, 상하이 Dry High cube 40' 공급 예측 모델은 $$0%{\leq_-}MAPE{\leq_-}10%$$에 속하는 매우 정확한 예측 모델로 검증되었다. 그 외의 상하이 수요 공급 예측 모델은 $$10%{\leq_-}MAPE{\leq_-}20%$$에 속해 비교적 정확한 예측 모델로 검증되었다. 얀티안 Dry High cube 40' 수요, Dry 20' 공급 예측 모델은 $$0%{\leq_-}MAPE{\leq_-}10%$$에 속해 매우 정확한 예측 모델이며, 그 외의 얀티안 수요 공급 예측 모델은 $$10%{\leq_-}MAPE{\leq_-}20%$$에 속해 비교적 정확한 예측 모델로 검증되었다. 본 연구의 예측 모델은 실제 선사에서 관리중인 데이터와 비교해도 높은 정확도를 갖는 것으로 나타났다. 본 연구에서 제시된 모델은 지역 수요예측 담당자 및 본부의 인벤토리 컨트롤 담당자가 참고자료로 유용하게 사용 가능하다.