• 대기
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• 제28권4호
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• pp.509-520
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• 2018

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

• 한국해안·해양공학회논문집
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• 제30권2호
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• pp.84-94
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• 2018

해양파랑 예측에 있어 단일 수치모델의 불확실성을 보완하기 위하여 앙상블 기법을 적용한 지역 파랑예측시스템을 구축하였다. 기상청 전지구 대기 수치모델의 확률예측시스템에서 생산되는 24개 앙상블 해상풍을 입력자료로 이용, 87시간까지 파랑 예측자료를 생산하였으며, 기상청 계류부이 관측자료와 다양한 통계방법을 적용하여 검증을 수행하였다. 2일예측 이후의 앙상블 예측평균의 평균제곱근오차(RMSE)는 단일모델예측에 비하여 향상된 결과를 보였으며, 특히 3일예측의 경우 단일모델예측 대비 RMSE가 약 15% 정도 향상되었다. 이것은 앙상블 기법이 수치모델의 불확실성을 감소시켜 예측정확도 향상에 크게 기여한 것으로 보인다. ROC(Relative Operating Characteristic) 분석결과, 전체 예측시간에 대하여 ROC 영역이 모두 0.9 이상을 보여 확률예측 성능이 뛰어남을 보였으며, 앙상블 파랑예측 결과가 해상 확률예보에 유용하게 활용될 수 있을 것으로 판단된다.

• 대기
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• 제30권4호
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• pp.405-420
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• 2020

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.

• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• 대한산업공학회지
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• 제24권4호
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• pp.485-492
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• 1998

Most forecasting models often fail to produce appropriate forecasts because we build a model based on the assumption of the data being generated from the only one stochastic process. However, in many real problems, the time series data are generated from one stochastic process for a while and then abruptly undergo certain structural changes. In this paper, we assume the basic underlying process is the simple state-space model with random level and deterministic drift but interrupted by three types of exogenous shocks: level shift, drift change, outlier. A Bayesian procedure to detect, estimate and adapt to the structural changes is developed and compared with simple, double and adaptive exponential smoothing using simulated data and the U.S. leading composite index.

• 한국콘텐츠학회논문지
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• 제13권11호
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• pp.382-395
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• 2013

기상정보 제공자들은 예보사용자들이 제공되는 예보서비스에 얼마나 만족하고 있는지를 알고 싶어 한다. 더 나은 예보서비스 제공을 위해서 각국의 기상 커뮤니티들은 사용자들의 만족도에 관한 다양한 설문조사를 진행하고 있다. 하지만 대부분의 설문조사들이 단순하게 사용자들이 얼마나 서비스에 만족하는지를 질문하고 있기 때문에 설문 결과의 설명력이 떨어지고, 예보서비스의 전략수립에 활용되기 어렵다. 본 연구에서는 예보의 가치를 평가하는 유용한 도구인 $2{\times}2$ 검증테이블에 기존의 비용-손실이 아닌 만족-불만족 개념을 적용하여 상해와 서울에서 제공된 24시간 강수예보의 만족가치를 도출하였다. 그리고 예보에 대한 개인의 만족가치뿐만 아니라 예보 사용자 그룹의 만족가치를 평가하였다. 그 결과, 확정예보 사용자 그룹의 만족가치를 높이기 위해선 예보 정확도의 향상이 유용하지만, 확률예보의 경우엔 사용자 그룹의 불만족 정도와 예보확률의 사용 분포를 파악하는 것이 중요한 것으로 나타났다. 따라서 기상 커뮤니티들이 예보 사용자들에게 더 나은 만족가치를 제공할 수 있는 방안을 찾는데 도움을 줄 수 있을 것이다.

• 한국항만학회지
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• 제13권1호
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• pp.27-40
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• 1999

This study aims to apply and examine the stochastic approach for empty container repositioning and leasing problem. For this a case study has been carried out on actual data such as various cost components and traffic flow. The results reveal that the proposed methodology produces more realistic results than the conventional deterministic approaches. It is also found that the results are significantly affected by the accuracy of demand and supply forecast.

• 한국건설관리학회논문집
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• 제2권2호
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• pp.90-97
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• 2001

공공기관에서 발주하는 건설공사의 계약에 있어서 가장 중요한 것은 합리적인 방법으로 결정된 적정한 예정가격을 기준으로 계약을 체결하는 것이다. 적정예정가격을 산정하기 위해서는 먼저 기수행한 실적공사비를 근간으로 하여 건설공사의 다양성과 불확실성을 반영할 수 있는 비용자료의 축적이 선결과제라 할 수 있다. 따라서 본 연구는 실적공사비적산방식에 따라 과거의 가장 유사한 실적자료에 기초한 확률적 비용개념을 도입하여 실적공사비 데이터 베이스 구축 모형과 이를 통한 예정공사비 산정방법 및 전산화 방안을 제시하고자 한다.

• 대기
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• 제28권3호
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• pp.247-261
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• 2018

The objective of this paper is to compare probabilistic temperature forecasts from different regional and global ensemble prediction systems over PyeongChang area. A statistical post-processing method is used to take into account combination and calibration of forecasts from different numerical prediction systems, laying greater weight on ensemble model that exhibits the best performance. Observations for temperature were obtained from the 30 stations in PyeongChang and three different ensemble forecasts derived from the European Centre for Medium-Range Weather Forecasts, Ensemble Prediction System for Global and Limited Area Ensemble Prediction System that were obtained between 1 May 2014 and 18 March 2017. Prior to applying to the post-processing methods, reliability analysis was conducted to identify the statistical consistency of ensemble forecasts and corresponding observations. Then, ensemble model output statistics and bias-corrected methods were applied to each raw ensemble model and then proposed weighted combination of ensembles. The results showed that the proposed methods provide improved performances than raw ensemble mean. In particular, multi-model forecast based on ensemble model output statistics was superior to the bias-corrected forecast in terms of deterministic prediction.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.174-178
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• 2015

This paper presents a new method for forecasting construction project cost and time at completion or at any intermediate time horizon of the project duration. The method is designed to overcome identified limitations of current applications of earned value method in forecasting project cost and time. The proposed method usesfuzzy set theory to model uncertainties associated with project performance and it integrates the earned value technique and the contractors' judgement. The fuzzy set theory is applied as an alternative approach to deterministic and probabilistic methods. Using fuzzy set theory allows contractors to: (1) perform risk analysis for different scenarios of project performance indices, and (2) perform different scenarios expressing vagueness and imprecision of forecasted project cost and time using a set of measures and indices. Unlike the current applications of Earned Value Method(EVM), The proposed method has a numberof interesting features: (1) integrating contractors' judgement in forecasting project performance; (2) enabling contractors to evaluate the risk associated with cost overrun in much simpler method comparing with that of simulation, and (3) accounting for uncertainties involved in the forecasting project cost.