• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.241-241
• /
• 2017

기후변화로 인하여 강우의 불확실성이 가중되고 홍수, 가뭄 등 물 관련 재해의 발생빈도 및 강도가 증가함에 따라 안정적인 용수공급 등 수자원 관리 및 운영에 어려움을 겪고 있어 예측기반의 수자원 계획 및 운영이 요구되고 있는 실정이다. 우리나라 기상청에서는 2010년 6월 영국기상청과 장기 계절예측시스템의 구축 및 운영에 관한 협정을 체결하였으며 2014년부터 전지구 계절예측시스템 GloSea5(Global seasonal forecasting system version 5)을 현업에 활용하고 있다. GloSea5 모델은 대기(UM), 지면(JULES), 해양(NEMO), 해빙(CICE) 모델이 커플러(OASIS)에 의해 결합된 통합 시스템으로 일단위 자료로 제공된다. 현재 수자원 분야에서는 장기예보자료가 제공되고 있음에도 불구하고 장기예보자료의 불확실성 및 수문 모형 입력자료로의 활용 어려움, 예측자료의 검증 미흡 등으로 기상청에서 제공하는 장기예보를 참고할 뿐 실제로는 과거 관측자료를 기반한 빈도해석 결과를 활용하여 댐 운영 계획을 수립하고 있는 실정이다. 따라서, 본 연구에서는 GloSea5모델에서 제공되는 일 단위 예측 강수량을 수자원 장기이수계획 및 관리에 활용하고자 GloSea5모델의 예측력을 평가하고 수치모델이 가지는 시스템 에러에 대하여 편의보정 및 지점 상세화를 수행하였다. 본 연구의 분석결과는 향후, 저수지 운영계획 및 증가하는 물수요와 불확실한 공급에 대한 의사결정 지원, 가뭄 대비를 위한 물 공급 제한 등에 활용 가능할 것으로 판단된다.

• Atmosphere
• /
• v.28 no.3
• /
• pp.233-245
• /
• 2018

This study explores the Subseasonal-to-Seasonal (S2S) prediction skills of the Northern Hemisphere mid-latitude geopotential height in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment. The prediction skills are quantitatively verified for the period of 1991~2010 by computing the Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS). GloSea5 model shows a higher prediction skill in winter than in summer at most levels regardless of verification methods. Quantitatively, the prediction limit diagnosed with ACC skill of 500 hPa geopotential height, averaged over $30^{\circ}N{\sim}90^{\circ}N$, is 11.0 days in winter, but only 9.1 days in summer. These prediction limits are primarily set by the planetary-scale eddy phase errors. The stratospheric prediction skills are typically higher than the tropospheric skills except in the summer upper-stratosphere where prediction skills are substantially lower than upper-troposphere. The lack of the summer upper-stratospheric prediction skill is caused by zonal mean error, perhaps strongly related to model mean bias in the stratosphere.

• Journal of Korean Society for Atmospheric Environment
• /
• v.34 no.2
• /
• pp.306-320
• /
• 2018

Emission inventory is the essential component for improving the performance of air quality forecasting system. This study evaluated the simulated daily mean $PM_{2.5}$ concentrations in South Korea and China for 1-year period (Sept. 2016~Aug. 2017) using air quality forecasting system which was applied by the emission inventory of E2015 (predicted CAPSS 2015 for South Korea and KORUS 2015 v1 for the other regions). To identify the impacts of emissions on the simulated $PM_{2.5}$, the emission inventory replaced by E2010 (CAPSS 2010 and MIX 2010) were also applied under the same forecasting conditions. These results showed that simulated daily mean $PM_{2.5}$ concentrations had generally suitable performance with both emission data-sets for China (IOA>0.87, R>0.87) and South Korea (IOA>0.84, R>0.76). The impacts of the changes in emission inventories on simulated daily mean $PM_{2.5}$ concentrations were quantitatively estimated. In China, normalized mean bias (NMB) showed 5.5% and 26.8% under E2010 and E2015, respectively. The tendency of overestimated concentrations was larger in North Central and Southeast China than other regions under both E2010 and E2015. Seasonal differences of NMB were higher in non-winter season (28.3% (E2010)~39.3% (E2015)) than winter season (-0.5% (E2010)~8.0% (E2015)). In South Korea, NMB showed -5.4% and 2.8% for all days, but -15.2% and -11.2% for days below $40{\mu}g/m^3$ to minimize the impacts of long-range transport under E2010 and E2015, respectively. For all days, simulated $PM_{2.5}$ concentrations were overestimated in Seoul, Incheon, Southern part of Gyeonggi and Daejeon, and underestimated in other regions such as Jeonbuk, Ulsan, Busan and Gyeongnam, regardless of what emission inventories were applied. Our results suggest that the updated emission inventory, which reflects current status of emission amounts and spatio-temporal allocations, is needed for improving the performance of air quality forecasting.

• Journal of Korea Water Resources Association
• /
• v.49 no.9
• /
• pp.761-771
• /
• 2016

The GloSea5 (Global Seasonal forecasting system version 5) is provided and operated by the KMA (Korea Meteorological Administration). GloSea5 provides Forecast (FCST) and Hindcast (HCST) data and its horizontal resolution is about 60km ($0.83^{\circ}{\times}0.56^{\circ}$) in the mid-latitudes. In order to use this data in watershed-scale water management, GloSea5 needs spatial-temporal downscaling. As such, statistical downscaling was used to correct for systematic biases of variables and to improve data reliability. HCST data is provided in ensemble format, and the highest statistical correlation ($R^2=0.60$, RMSE = 88.92, NSE = 0.57) of ensemble precipitation was reported for the Yongdam Dam watershed on the #6 grid. Additionally, the original GloSea5 (600.1 mm) showed the greatest difference (-26.5%) compared to observations (816.1 mm) during the summer flood season. However, downscaled GloSea5 was shown to have only a -3.1% error rate. Most of the underestimated results corresponded to precipitation levels during the flood season and the downscaled GloSea5 showed important results of restoration in precipitation levels. Per the analysis results of spatial autocorrelation using seasonal Moran's I, the spatial distribution was shown to be statistically significant. These results can improve the uncertainty of original GloSea5 and substantiate its spatial-temporal accuracy and validity. The spatial-temporal reproducibility assessment will play a very important role as basic data for watershed-scale water management.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.43-57
• /
• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.1
• /
• pp.65-78
• /
• 2017

Even though cars have a good effect on modern society, traffic accidents do not. There are traffic laws that define the regulations and aim to reduce accidents from happening; nevertheless, it is hard to determine all accident causes such as road and traffic conditions, and human related factors. If a traffic accident occurs, the traffic law classifies it as 'Negligence of Safe Driving' for cases that are not defined by specific regulations. Meanwhile, as Korea is already growing rapidly elderly population with more than 65 years, so are the number of traffic accidents caused by this group. Therefore, we studied predictive and comparative analysis of the number of traffic accidents caused by 'Negligence of Safe Driving' by dividing it into two groups : All-ages and Elderly. In this paper, we used empirical monthly data from 2007 to 2015 collected by TAAS (Traffic Accident Analysis System), identified the most suitable ARIMA forecasting model by using the four steps of the Box-Jenkins method : Identification, Estimation, Diagnostics, Forecasting. The results of this study indicate that ARIMA $(1, 1, 0)(0, 1, 1)_{12}$ is the most suitable forecasting model in the group of All-ages; and ARIMA $(0, 1, 1)(0, 1, 1)_{12}$ is the most suitable in the group of Elderly. Then, with this fitted model, we forecasted the number of traffic accidents for 2 years of both groups. There is no large fluctuation in the group of All-ages, but the group of Elderly shows a gradual increase trend. Finally, we compared two groups in terms of the forecast, suggested a countermeasure plan to reduce traffic accidents for both groups.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.25 no.4
• /
• pp.331-338
• /
• 2012

Irregularity data inspected by EM-120, an railway inspection system in Korea includes unavoidable incomplete and erratic information, so it is encountered lots of problem to analyse those data without appropriate pre-data-refining processes. In this research, for the efficient management and maintenance of railway system, characteristics and problems of the detected track irregularity data have been analyzed and efficient processing techniques were developed to solve the problems. The correlation between track irregularity and seasonal changes was conducted based on ARIMA model analysis. Finally, time series analysis was carried out by various forecasting model, such as regression, exponential smoothing and ARIMA model, to determine the appropriate optimal models for forecasting track irregularity progress.

• Journal of the Korean earth science society
• /
• v.45 no.1
• /
• pp.19-36
• /
• 2024

The global seasonal forecasting system version 6 (GloSea6) operated by the Korea Meteorological Administration for 1- and 3-month prediction products does not include complex atmospheric chemistry-aerosol physical processes (UKCA). In this study, low-resolution GloSea6 and GloSea6 coupled with UKCA (GloSea6-UKCA) were installed in a CentOS-based Linux cluster system, and preliminary prediction results for the spring of 2000 were examined. Low-resolution versions of GloSea6 and GloSea6-UKCA are highly needed to examine the effects of atmospheric chemistry-aerosol owing to the huge computational demand of the current high resolution GloSea6. The spatial distributions of the surface temperature and daily precipitation for April 2000 (obtained from the two model runs for the next 75 days, starting from March 1, 2000, 00Z) were compared with the ERA5 reanalysis data. The GloSea6-UKCA results were more similar to the ERA5 reanalysis data than the GloSea6 results. The surface air temperature and daily precipitation prediction results of GloSea6-UKCA for spring, particularly over East Asia, were improved by the inclusion of UKCA. Furthermore, compared with GloSea6, GloSea6-UKCA simulated improved temporal variations in the temperature and precipitation intensity during the model integration period that were more similar to the reanalysis data. This indicates that the coupling of atmospheric chemistry-aerosol processes in GloSea6 is crucial for improving the spring predictions over East Asia.

• Atmosphere
• /
• v.31 no.2
• /
• pp.215-227
• /
• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

• Atmosphere
• /
• v.33 no.5
• /
• pp.493-503
• /
• 2023

This study investigates the downward influences of sudden stratospheric warming (SSW) in February 2018 using a subseasonal-to-seasonal forecast model, Global Seasonal forecasting system version 6 (GloSea6). To quantify the influences of SSW on the tropospheric prediction skills, free-evolving (FREE) forecasts are compared to stratospheric nudging (NUDGED) forecasts where zonal-mean flows in the stratosphere are relaxed to the observation. When the models are initialized on 8 February 2018, both FREE and NUDGED forecasts successfully predicted the SSW and its downward influences. However, FREE forecasts initialized on 25 January 2018 failed to predict the SSW and downward propagation of negative Northern Annular Mode (NAM). NUDGED forecasts with SSW nudging qualitatively well predicted the downward propagation of negative NAM. In quantity, NUDGED forecasts exhibit a higher mean squared skill score of 500 hPa geopotential height than FREE forecasts in late February and early March. The surface air temperature and precipitation are also better predicted. Cold and dry anomalies over the Eurasia are particularly well predicted in NUDGED compared to FREE forecasts. These results suggest that a successful prediction of SSW could improve the surface prediction skills on subseasonal-to-seasonal time scale.