• Proceedings of the Korea Air Pollution Research Association Conference
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• 2005.11a
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• pp.289-291
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• 2005

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.531-546
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• 2018

Air quality forecasting system with Asian dust emissions was developed in East Asia, and $PM_{10}$ forecasting performance of chemical transport model with Asian dust emissions was validated and evaluated. The chemical transport model (CTM) with Asian dust emission was found to supplement $PM_{10}$ concentrations that had been under-estimated in China regions and improved statistics for performance of CTM, although the model were overestimated during some periods in China. In Korea, the prediction model adequately simulated inflow of Asian dust events on February 22~24 and March 16~17, but the model is found to be overestimated during no Asian dust event periods on April. However, the model supplemented $PM_{10}$ concentrations, which was underestimated in most regions in Korea and the statistics for performance of the models were improved. The $PM_{10}$ forecasting performance of air quality forecasting model with Asian dust emissions tends to improve POD (Probability of Detection) compared to basic model without Asian dust emissions, but A (Accuracy) has shown similar or decreased, and FAR (False Alarms) have increased during 2017.Therefore, the developed air quality forecasting model with Asian dust emission was not proposed as a representative $PM_{10}$ forecast model in South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.236-236
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• 2016

현재 댐운영 계획 수립 시 매월 유지해야 하는 저수량의 범위를 나타낸 기준수위가 사용되고 있으며 매년 홍수기 말에 현재의 수문 상황과 장래의 전망을 통한 시기별 연간, 월간 댐운영 계획을 수립하고 있다. 물관리의 이수측면에서 댐수위 운영계획 수립과 홍수기 운영목표 수위를 결정하는데 활용하기 위해서는 계절단위, 연단위의 기상정보가 필요하다. 본 연구에서는 기상청에서 운영하고 제공하는 전지구 계절예측시스템 GloSea5(Global Seasonal forecasting system version 5)자료를 활용하여 금강유역에 적용하고자 하였다. GloSea5는 전지구계절예측시스템으로 대기(UM), 지면(JULES), 해양(NEMO), 해빙(CICE)모델이 서로 결합되어 하나의 시스템으로 구성되어 있으며 공간 수평해상도는 N216($0.83^{\circ}{\times}0.56^{\circ}$)으로 중위도에서 약60km이다. Hindcast자료는 유럽중기예보센터(ECMWF)에서 생산된 ERA-Interim 재분석장을 대기 모델의 초기장으로 사용하며 기간은 1996~2009년의 총 14년이다. 예보자료의 검증은 예보의 질을 결정하는 과정으로 Brier Skill Score (BSS), Reliability Diagrams, Relative Operating, Characteristics (ROC)등을 통해 정확성과 오차에 의한 예보의 성능을 검증하였다. 또한 Glosea5의 통계적 상세화를 수행하여 다양한 변수가 갖는 계통적인 지역 오차를 보정함으로써 자료의 신뢰도를 향상시키고자 하였으며 이는 이후 수문모델과의 연계 시 보다 정확하고 효율적인 댐운영에 활용할 수 있는 기후예측정보를 제공할 수 있을 것으로 판단된다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.271-277
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• 2017

As a result of rapid industrialization, air pollutants are seriously threatening the health of the people, the forecast is becoming more and more important. In forecasting air quality, it is very important to create a reliable initial field because the initial field input to the air quality forecasting model affects the accuracy of the forecast. There are several methods for enhancing the initial field input. One of the necessary techniques is data assimilation. The number of operations and the time required for such data assimilation is exponentially increased as the forecasting area is widened and the number of observation sites increases. Therefore, as the forecast size increases, it is difficult to apply the existing sequential processing method to a field requiring fast processing speed. In this paper, we propose a method that can process Cresman's method, which is one of the data assimilation techniques, in real time using CUDA. As a result, the proposed parallel processing method using CUDA improved at least 35 times faster than the conventional sequential method and other parallel processing methods.

• Journal of Environmental Impact Assessment
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• v.28 no.3
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• pp.183-200
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• 2019

In this study, the characteristics of seasonal $PM_{2.5}$ behavior in South Korea and other Northeast Asian regions were analyzed by using the $PM_{2.5}$ ground measurement data, weather data, WRF and CMAQ models. Analysis of seasonal $PM_{2.5}$ behavior in Northeast Asia showed that $PM_{2.5}$ concentration at 6 IMS sites in South Korea was increased by long-distance transport and atmospheric congestion, or decreased by clean air inflow due to seasonal weather characteristics. As a result of analysis by applying BFM to air quality model, the contribution from foreign countries dominantly influenced the $PM_{2.5}$ concentrations of Baengnyeongdo due to the low self-emission and geographical location. In the case of urban areas with high self-emissions such as Seoul and Ulsan, the $PM_{2.5}$ contribution from overseas was relatively low compared to other regions, but the standard deviation of the season was relatively high. This study is expected to improve the understanding of the air pollutant phenomenon by analyzing the characteristics of $PM_{2.5}$ behavior in Northeast Asia according to the seasonal weather condition change. At the same time, this study can be used to establish the air quality policy in the future, knowing that the contribution of $PM_{2.5}$ concentration to the domestic and overseas can be different depending on the regional emission characteristics.

• Journal of Environmental Science International
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• v.25 no.7
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• pp.1029-1042
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• 2016

The initial and boundary conditions are important factors in regional chemical transport modeling systems. The method of generating the chemical boundary conditions for regional air quality models tends to be different from the dynamically varying boundary conditions in global chemical transport models. In this study, the impact of real time Copernicus atmosphere monitoring service (CAMS) re-analysis data from the modeling atmospheric composition and climate project interim implementation (MACC) on the regional air quality in the Korean Peninsula was carried out using the community multi-scale air quality modeling system (CMAQ). A comparison between conventional global data and CAMS for numerical assessments was also conducted. Although the horizontal resolution of the CAMS re-analysis data is not higher than the conventionally provided data, the simulated particulate matter (PM) concentrations with boundary conditions for CAMS re-analysis is more reasonable than any other data, and the estimation accuracy over the entire Korean peninsula, including the Seoul and Daegu metropolitan areas, was improved. Although an inland area such as the Daegu metropolitan area often has large uncertainty in PM prediction, the level of improvement in the prediction for the Daegu metropolitan area is higher than in the coastal area of the western part of the Korean peninsula.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.289-299
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• 2015

The objective of the present study is to develop statistical quantitative forecast model for PM10 concentration over Seoul. We used three types of data (weather observation data in Korea, the China's weather observation data collected by GTS, and air quality numerical model forecasts). To apply the daily forecast system, hourly data are converted to daily data and then lagging was performed. The potential predictors were selected based on correlation analysis and multicollinearity check. Model validation has been performed for checking model stability. We applied two models (multiple regression model and threshold regression model) separately. The two models were compared based on the scatter plot of forecasts and observations, time series plots, RMSE, skill scores. As a result, a threshold regression model performs better than multiple regression model in high PM10 concentration cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.595-601
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• 2018

Recently, as the fine dust level has risen rapidly, there is a great interest. Exposure to fine dust is associated with the development of respiratory and cardiovascular diseases and has been reported to increase death rate. In addition, there exist damage to fine dusts continues at industrial sites. However, exposure to fine dust is inevitable in modern life. Therefore, predicting and minimizing exposure to fine dust is the most efficient way to reduce health and industrial damages. Existing fine dust prediction model is estimated as good, normal, poor, and very bad, depending on the concentration range of the fine dust rather than the concentration value. In this paper, we study and implement to predict the PM10 level by applying the Artificial neural network algorithm and the K-Nearest Neighbor algorithm, which are machine learning algorithms, using the actual weather and air quality data.