• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.131-131
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• 2018

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• Korean Journal of Agricultural Science
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• v.38 no.3
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• pp.571-581
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• 2011

This study develops a mid-term preceding observation model of radish to complement an existing short-term agricultural observation model. The first purpose of the study is to extend a three seasonal classification(spring, summer, fall) of fruit-vegetables to a four seasonal classification that involves the winter additionally. This allows us to verify the reason for demand and supply unbalance and unstable price of radish. The second purpose is to construct a mid-term preceding observation model that would be used to forecast planted areas, output, monthly shipment and price. To achieve these purposes, several multiple regression models are estimated. A system is consisted of a planted areas equation, a yield equation, monthly shipment distribution equation, and monthly price equation. To calculate output an auxiliary equation is involved in the system and the consumer price index etc are considered as exogenous variables.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.529-541
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• 2023

Due to the growing concern over forecasting extreme weather events such as droughts caused by climate change, there has been a rising interest in seasonal meteorological forecasts that offer ensemble predictions for the upcoming seven months. Nonetheless, limited research has been conducted in South Korea, particularly in assessing their effectiveness at the catchment-scale. In this study, we assessed the accuracy of ECMWF's seasonal forecasts (including precipitation, temperature, and evapotranspiration) for the period of 2011 to 2020. We focused on 12 multi-purpose reservoir catchments and compared the forecasts to climatology data. Continuous Ranked Probability Skill Score method is adopted to assess the forecast skill, and the linear scaling method was applied to evaluate its impact. The results showed that while the seasonal meteorological forecasts have similar skill to climatology for one month ahead, the skill decreased significantly as the forecast lead time increased. Compared to the climatology, better results were obtained in the Wet season than the Dry season. In particular, during the Wet seasons of the dry years (2015, 2017), the seasonal meteorological forecasts showed the highest skill for all lead times.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.22-34
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• 2017

The K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model), a distributed rainfall-runoff model of K-water, calculates predicted runoff and water surface level of a dam using precipitation data. In order to obtain long-term hydrometeorological information, K-DRUM requires long-term weather forecast. In this study, we built a system providing long-term hydrometeorological information using predicted rainfall ensemble of GloSea5(Global Seasonal Forecast System version 5), which is the seasonal meteorological forecasting system of KMA introduced in 2014. This system produces K-DRUM input data by automatic pre-processing and bias-correcting GloSea5 data, then derives long-term inflow predictions via K-DRUM. Web-based UI was developed for users to monitor the hydrometeorological information such as rainfall, runoff, and water surface level of dams. Through this UI, users can also test various dam management scenarios by adjusting discharge amount for decision-making.

• Atmosphere
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• v.26 no.4
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• pp.599-615
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• 2016

This study investigates the accuracy of short-term ocean predictions during the development of ocean stratification for the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 5 (GloSea5) as well as the effect of atmosphere-ocean coupling on the predictions through a series of sensitive numerical experiments. Model performance is evaluated using the marine meteorological buoys at seas around the Korean peninsular (KP), Tropical Atmosphere Ocean project (TAO) buoys over the tropical Pacific ocean, and ARGO floats data over the western North Pacific for boreal winter (February) and spring (May). Sensitive experiments are conducted using an ocean-atmosphere coupled model (i.e., GloSea5) and an uncoupled ocean model (Nucleus for European Modelling of the Ocean, NEMO) and their results are compared. The verification results revealed an overall good performance for the SST predictions over the tropical Pacific ocean and near the Korean marginal seas, in which the Root Mean Square Errors (RMSE) were $0.31{\sim}0.45^{\circ}C$ and $0.74{\sim}1.11^{\circ}C$ respectively, except oceanic front regions with large spatial and temporal SST variations (the maximum error reached up to $3^{\circ}C$). The sensitive numerical experiments showed that GloSea5 outperformed NEMO over the tropical Pacific in terms of bias and RMSE analysis, while NEMO outperformed GloSea5 near the KP regions. These results suggest that the atmosphere-ocean coupling substantially influences the short-term ocean forecast over the tropical Pacific, while other factors such as atmospheric forcing and the accuracy of simulated local current are more important than the coupling effect for the KP regions being far from tropics during the development of ocean stratification.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.169-177
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• 2020

The purpose of this study is to forecast the sales of convenience store flagship products with nonlinear characteristics and time series characteristics. According to the results, the sales of 'Ice Cup' began to increase from March, reached the highest value in summer, especially July and August, and then decreased, revealing a seasonal pattern. Cigarettes showed a seasonal pattern of higher sales in summer and lower sales in winter and was predicted to decrease in sales in the future. This study provides an academic implication in that it focused on the top-selling products that affected an increase in financial performance in a specific convenience store, a method that has been hardly adopted by the existing studies.

• Journal of Distribution Science
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• v.14 no.11
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• pp.83-90
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• 2016

Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

• Atmosphere
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• v.34 no.1
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• pp.23-34
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• 2024

The Korea Meteorological Administration has improved medium-range weather forecasts by implementing post-processing methods to minimize numerical model errors. In this study, we employ a statistical correction technique known as the minimum continuous ranked probability score (CRPS) to refine medium-range forecast guidance. This technique quantifies the similarity between the predicted values and the observed cumulative distribution function of the Unified Model Ensemble Prediction System for Global (UM EPSG). We evaluated the performance of the medium-range forecast guidance for surface air temperature and relative humidity, noting significant enhancements in seasonal bias and root mean squared error compared to observations. Notably, compared to the existing the medium-range forecast guidance, temperature forecasts exhibit 17.5% improvement in summer and 21.5% improvement in winter. Humidity forecasts also show 12% improvement in summer and 23% improvement in winter. The results indicate that utilizing the minimum CRPS for medium-range forecast guidance provide more reliable and improved performance than UM EPSG.

• Environmental Engineering Research
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• v.22 no.2
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.