• Atmosphere
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• v.30 no.2
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• pp.141-154
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• 2020

Seasonal forecast is growing in demand, as it provides valuable information for decision making and potential to reduce impact on weather events. This study examines how operational climate prediction systems can be reliable, producing the probability forecast in seasonal scale. A reliability diagram was used, which is a tool for the reliability by comparing probabilities with the corresponding observed frequency. It is proposed for a method grading scales of 1-5 based on the reliability diagram to quantify the reliability. Probabilities are derived from ensemble members using hindcast data. The analysis is focused on skill for 2 m temperature and precipitation from climate prediction systems in KMA, UKMO, and ECMWF, NCEP and JMA. Five categorizations are found depending on variables, seasons and regions. The probability forecast for 2 m temperature can be relied on while that for precipitation is reliable only in few regions. The probabilistic skill in KMA and UKMO is comparable with ECMWF, and the reliabilities tend to increase as the ensemble size and hindcast period increasing.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.71-77
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• 2011

In this paper, asymptotic results are investigated when a parametric transformation is applied to ARMA models. The conditions are determined to ensure the strong consistency and the asymptotic normality of maximum likelihood estimators and the correct coverage probability of the forecast interval obtained by the transformation and backtransformation approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.89-98
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• 2007

In this paper the economic value of weather forecasts is valuated for profit-oriented enterprise decision-making situations. Value is estimated in terms of monetary profits (or benefits) resulted from the forecast user's decision under the specific payoff structure, which is represented by a profit/loss ratio model combined with a decision function and a value score (VS). The forecast user determines a business-related decision based on the probabilistic forecast, the user's subjective reliability of the forecasts, and the payoff structure specific to the user's business environment. The VS curve for a meteorological forecast is specified by a function of the various profit/loss ratios, providing the scaled economic value relative to the value of a perfect forecast. The proposed valuation method based on the profit/loss ratio model and the VS is adapted for hypothetical sets of forecasts and verified for site-specific probability of precipitation forecast of 12 hour and 24 hour-lead time, which is generated from Korea meteorological administration (KMA). The application results show that forecast information with shorter lead time can provide the decision-makers with great benefits and there are ranges of profit/loss ratios in which high subjective reliability of the given forecast is preferred.

• The Bulletin of The Korean Astronomical Society
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• v.30 no.1
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• pp.51.1-51.1
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• 2005

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.87-98
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• 2022

In recent years, frequent localized heavy rainfalls, which have a lot of rainfall in a short period of time, have been increasingly causing flooding damages. To prevent damage caused by localized heavy rainfalls, Hydrological Quantitative Precipitation Forecast (HQPF) was developed using the Local ENsemble prediction System (LENS) provided by the Korea Meteorological Administration (KMA) and Machine Learning and Probability Matching (PM) techniques using Digital forecast data. HQPF is produced as information on the impact of heavy rainfall to prepare for flooding damage caused by localized heavy rainfalls, but there is a tendency to overestimate the low rainfall intensity. In this study, we improved HQPF by expanding the period of machine learning data, analyzing ensemble techniques, and changing the process of Probability Matching (PM) techniques to improve predictive accuracy and over-predictive propensity of HQPF. In order to evaluate the predictive performance of the improved HQPF, we performed the predictive performance verification on heavy rainfall cases caused by the Changma front from August 27, 2021 to September 3, 2021. We found that the improved HQPF showed a significantly improved prediction accuracy for rainfall below 10 mm, as well as the over-prediction tendency, such as predicting the likelihood of occurrence and rainfall area similar to observation.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.1-9
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• 2007

A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.

• Atmosphere
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• v.25 no.1
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• pp.67-83
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• 2015

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.162-168
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• 2010

Information is known to be a key element for the successful operation of a supply chain, which is required of the efficient ordering strategies and accurate predictions of demands. This study proposes a method to effectively utilize the meteorological forecast information in order to make decisions about ordering and prediction of demands by using the Taguchi experimental design. It is supposed that each echelon in a supply chain determines the order quantity with the prediction of precipitation in the next day based on probability forecast information. The precipitation event is predicted when the probability of the precipitation exceeds a chosen threshold. Accordingly, the choice of the threshold affect the performances of a supply chain. The Taguchi method is adopted to deduce a set of thresholds for echelons which is least sensitive to changes in environmental conditions, such as variability of demand distributions and production periods. A simulation of the beer distribution game was conducted to show that the set of thresholds found by the Taguchi method can reduce the cumulative chain cost, which consists of inventory and backlog costs.

• Water Engineering Research
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• v.5 no.2
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• pp.101-109
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• 2004

Aggregating information by combining forecasts from two or more forecasting methods is an alternative to using forecasts from just a single method to improve forecast accuracy. This paper describes the development and use of a monthly inflow forecast model based on an optimal linear combination (OLC) of forecasts derived from naive, persistence, and Ensemble Streamflow Prediction (ESP) forecasts. Using the cross-validation technique, the OLC model made 1-month ahead probabilistic forecasts for the Chungju multi-purpose dam inflows for 15 years. For most of the verification months, the skill associated with the OLC forecast was superior to those drawn from the individual forecast techniques. Therefore this study demonstrates that OLC can improve the accuracy of the ESP forecast, especially during the dry season. This study also examined the value of the OLC forecasts in reservoir operations. Stochastic Dynamic Programming (SDP) derived the optimal operating policy for the Chungju multi-purpose dam operation and the derived policy was simulated using the 15-year observed inflows. The simulation results showed the SDP model that updated its probability from the new OLC forecast provided more efficient operation decisions than the conventional SDP model.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.111-112
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• 2008

Although two types of uncertainty such as randomness and fuzziness simultaneously exist in power systems, yet they have been treated as distinct fields to evaluate the power system reliability. Thus, this paper presents a reliability assessment method based on a combined concept of fuzzy and probability. To reflect the two-fold uncertainty, a modified load duration curve(MLDC) is proposed using the probability distribution of historical load data in which a fuzzy model for the peak load forecast is embedded. IEEE RTS system was used to demonstrate the usefulness and applicability of the proposed method, and the reliability indices were obtained using the proposed MLDC. The results show a wider insight into impact of load fuzziness on uncertainties of reliability indices for power systems.