• 한국대기환경학회지
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• 제10권3호
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• pp.197-202
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• 1994

The research described in this paper is conducted to estimate the short-term concentrations of nonreactive pollutants such as CO and TSP from vehicle emissions near Kyungbu Highway. An emphasis is placed on the development of a model for a hourly traffic volume for each vehicle type, which is based on real traffic data. By using the model and the calculated emission factor due to vehicle speed for each vehicle type, the emission rate of CO and TSP for each traffic line is computed. The hourly emission rate and meteorological data are used to simulate by HIWAY-2 for the distance of 5m and 10m from the downwind edge of Kyungbu Highway located in relatively uncomplicated terrain.

• 대기
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• 제32권4호
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• pp.353-365
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• 2022

This paper evaluates precipitation forecast skill of Global/Regional Integrated Model system (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type (C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequencies are 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantly overestimates precipitation occurrence (0.1 mm d^-1) for all types of winter precipitation. Objective measured skill scores of GRIMs are comparably high for L type and O type. Except for precipitation occurrence, the model shows high predictability for L type precipitation with the most unbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuring rare events due to its high dependency on the sample size, as in the case of Critical Success Index as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivity on the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplement the limit of ETS. The evaluation via SEDS shows that the prediction skill score for L type is the highest in the range of 5.0, 10.0 mm d^-1 and the score for O type is the highest in the range of 1.0, 20.0 mm d^-1. C type has the lowest scores in overall range. The difference in precipitation forecast skill by precipitation type can be explained by the spatial distribution and intensity of precipitation in each representative case.

• 한국대기환경학회지
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• 제12권5호
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• pp.529-540
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• 1996

Dispersion experiment using SF$_{6}$ tracer was performed in the flat field of Chunchon Basin during four nights from August 29 to September 2, 1991. The purpose of this study is to analyze toe horizontal distribution of tracer concentration under the strong stable conditions and to evaluate the results calculated by INPUFF model. Incase of high wind speed, plume spread of SF$_{6}$ concentration appeared in narrow area of the downwind and the standard deviation of the horizontal wind angle (.sigma.$_{a}$) was amall. However, the SF$_{6}$ was spread widely in cases of low wind speed because of the large .sigma.$_{a}$. The result of the INPUFF model was similar to the observed distribution of the SF$_{6}$ concentration. It is proved that the Gaussian puff model is useful when wind direction varies significantly.tly.tly.tly.

• 한국환경과학회지
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• 제5권6호
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• pp.699-712
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• 1996

For the efficient control of atmospheric quality, it is so important to predict the influence accurately of which the air pollutant emitted into the atmosphere. Atmospheric dispersion model enables to simulate and grasp the atmospheric condition occurred due to the emission of pollutants. The result of model is largely affected by the amount of emission, the characteristics of physical and chemical process, meteorological input data, and the receptor which the concentration is calculated. The aim of this research, therefore, is to suggest more suitable model in Pusan area than other areas by performing TCM2, CDM2.0 and ISCLT2 models. As the basic work for executing the model, we computed the amount of emission of air pollutants in Pusan at 1992 and analyzed the occurrence frequency of atmospheric stability for recent decade(1985~19941, CDM2.0 showed the similar result relatively with observed value in the case of full year(1992), fall and winter, and ISCLT2 brought more suitable result in spring for Pusan area. As the result of this research, in future, it is necessary for us to develop the numerical model considering the topographical characteristics, to select the proper observation site and to increase the observation site for Pusan.

• 해양환경안전학회지
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• 제7권2호
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• pp.39-48
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• 2001

In recent years, the marine traffic congestion has increased due to the expansion of vessel traffic volume in Korean coastal waterway. Heavy traffic could bring serious marine casualties which cause the loss of human lives, properties and marine pollution in coastal area. The prevention of marine accidents has been a major topic in marin society and various policies and countermeasures have been developed, applied to the industries. VTS(Vessel Traffic Services) is considered as one of effective method to promote marine safety but it needs relatively huge amount of budgets to build and also number of personnels for the operation. Thus prior to establishing the VTS. It should be surveyed the marine traffics, general conditions of waterway including geographical, meteorological characteristics and assessed to find the most reasonable area and places for the system. Therefore this paper aims to develop the method for this evaluation through the hierarchical evaluation structure model in connection with ISM(interpretive structural modeling) and AHP(analytic hierarchy process) methods. The model in this paper is applied to 4 coastal area in Korean waterway as candidates and found that the priority for the needs of VTS should be in order such as Yosu, Wando, Mokpo, Geoje coastal area.

• 대기
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• 제24권4호
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• pp.503-514
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• 2014

Evaluation of predictability of numerical models for tropical cyclone track was performed using along-and cross-track component. The along-and cross-track bias were useful indicators that show the numerical models predictability associated with cause of errors. Since forecast errors, standard deviation and consistency index of along-track component were greater than those of cross-track component, there was some rooms for improvement in alongtrack component. There was an overall slow bias. The most accurate model was JGSM for 24-hour forecast and ECMWF for 48~96-hour forecast in direct position error, along-track error and cross-track error. ECMWF and GFS had a high variability for 24-hour forecast. The results of predictability by track type showed that most significant errors of tropical cyclone track forecast were caused by the failure to estimate the recurvature phenomenon.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.305-305
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• 2022

A better approach for assessing meteorological drought occurrences is increasingly important in mitigating and adapting to the impacts of climate change, as well as strategies for developing early warning systems. The present study defines meteorological droughts as a period with an abnormal precipitation deficit based on monthly precipitation data of 18 gauging stations for the Han River watershed in the past (1974-2015). This study utilizes a Bayesian parameter estimation approach to analyze the effects of climate change on future drought (2025-2065) in the Han River Basin using the Coupled Model Intercomparison Project Phase 6 (CMIP6) with four bias-corrected general circulation models (GCMs) under the Shared Socioeconomic Pathway (SSP)2-4.5 scenario. Given that drought is defined by several dependent variables, the evaluation of this phenomenon should be based on multivariate analysis. Two main characteristics of drought (severity and duration) were extracted from precipitation anomalies in the past and near-future periods using the copula function. Three parameters of the Archimedean family copulas, Frank, Clayton, and Gumbel copula, were selected to fit with drought severity and duration. The results reveal that the lower parts and middle of the Han River basin have faced severe drought conditions in the near future. Also, the bivariate analysis using copula showed that, according to both indicators, the study area would experience droughts with greater severity and duration in the future as compared with the historical period.

• 자원환경지질
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• 제39권3호
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• pp.255-268
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• 2006

58개 기상관측소에서 최근 22년간(1981-2002) 측정된 기상 자료를 이용하여 국내의 기온(SAT) 및 지면온도(GST)의 시공간적 변동 경향을 분석하였다. 먼저 관측 자료로부터 각 관측소의 평균기온(MSAT)과 평균지면온도(MGST)를 계산하였으며, 다중선형회귀분석을 통해 MSAT와 MGST를 예측할 수 있는 회귀식을 산정하였다. 회귀모형의 회귀변수는 관측소의 위도 및 고도이다. 회귀모형의 추정치와 실제 관측값의 결정계수($R^2$)는 각각 0,92와 0.94로 나타나 모형의 예측 정확성이 매우 높은 것으로 분석되었다. MGST는 지열펌프 시스템 설계의 주요 입력 변수이므로 최근 지열에너지자원 활용 분야에서 매우 중요하게 다루어지는 변수이다. 따라서 제시된 회귀모형은 신뢰할만한 관측 자료가 없는 지역에서 MGST를 추정하는데 매우 유용하게 이용될 수 있을 것으로 예상된다. SAT 자료에 대한 선헝회귀분석을 통해 지구온난화 및 도시화에 기인한 기온 상승의 장기 추세 변동성을 탐색하였다. 1개 관측소를 제외한 57개 관측소에서 $0.005{\sim}0.088^{\circ}C/yr$ 범위의 기온증가율을 가지는 추세 변동이 확인되었다. 또한 GST에 영향을 미치는 기상요소로서 일사량, 지구복사, 강수량 및 적설량 자료를 분석하였다. GST는 주로 SAT 및 일사량에 의하여 결정되지만 강수 및 증발에 의한 토양의 열용량 변화, 적설에 의한 대기와 지표면 차단, 지구복사에 영향을 줄 수 있는 대기의 조건 변화 등이 복합적인 변동 요인으로 작용하는 것으로 나타났다.

• 한국수자원학회논문집
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• 제52권1호
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• pp.21-33
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• 2019

본 연구에서는 비슬산 이중편파 Radar 자료와, GPM 위성자료 및 21개 (Korea Meteorological Administration, KMA) 지상강우자료를 활용하여 분포형 강우-유출 모형(KIneMatic wave STOrm Runoff Model2, KIMSTORM2)을 이용해 남강댐 유역($2,293km^2$)을 대상으로 유출해석을 수행하였다. 모형의 유출 해석은 2016년 10월 5일 02:00~09:00 총 8시간 동안 최대강우강도 33 mm/hr, 유역평균 총 강우량 82 mm이 발생한 태풍 차바(CHABA)를 대상으로 하였으며, Radar 및 GPM 자료와 조건부합성(Conditional Merging, CM) 기법을 적용한 Radar (CM-corrected Radar) 및 GPM (CM-corrected GPM) 자료를 각각 활용하여 결과를 비교하였다. 이 때, 공간 강우자료에 유출 검보정은 남강댐 유역 내 3개의 수위관측 지점(산청, 창촌, 남강댐)을 대상으로 실시하였으며, 모형의 매개변수 초기토양수분함량, 지표와 하천의 Manning 조도계수를 이용하여 검보정하였다. 유출 결과는 결정계수(Determination coefficient, $R^2$), Nash-Sutcliffe의 모형효율계수(NSE) 및 유출용적지수(Volume Conservation Index, VCI)를 산정하였다. 그 결과 CM-corrected Radar, GPM 자료가 평균 $R^2$는 0.96, NSE의 경우 0.96, 유출용적지수(VCI)는 1.03으로 가장 우수한 결과를 나타내었다. 최종적으로 CM 기법을 이용한 보정된 공간분포자료는 기존의 자료에 비해 시공간적으로 정확한 홍수 예측에 사용 될 것으로 판단된다.

• 대한원격탐사학회지
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• 제36권6_3호
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• pp.1711-1720
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• 2020

최근 급속한 산업화와 도시화로 인해 인위적으로 발생하는 미세먼지(Particulate matter, PM)는 기상 조건에 따라 이동 및 분산되면서 피부와 호흡기 등 인체에 악영향을 미친다. 본 연구는 기상인자를 multiple linear regression(MLR), support vector machine(SVM), 그리고 random forest(RF) 모델의 입력자료로 하여 서울시 PM10 농도를 예측하고, 모델 간 성능을 비교 평가하는데 그 목적을 둔다. 먼저 서울시에 소재한 39개소 대기오염측정망(air quality monitoring sites, AQMS)에서 관측된 PM10 농도 자료를 8:2 비율로 구분하여 모델 훈련과 검증 데이터셋으로 사용되었다. 또한 기상관측소(automatic weather system, AWS)에서 관측되고 있는 자료 중 9개 기상인자(평균기온, 최고기온, 최저기온, 일 강수량, 평균풍속, 최대순간풍속, 최대순간풍속풍향, 황사발생유무, 상대습도)가 모델의 입력자료로 선정되었다. 각 AQMS에서 관측된 PM10 농도와 MLR, SVM, 그리고 RF 모델에 의해 예측된 PM10 농도 간 결정계수(R2)는 각각 0.260, 0.772, 그리고 0.793이었고, RF 모델이 PM10 농도 예측에 가장 높은 성능을 나타냈다. 특히 모델 검증에 사용되는 AQMS 중 관악구와 강남대로 AQMS는 상대적으로 AWS에 가까워 SVM과 RF 모델에서 높은 정확도를 나타냈다. 종로구 AQMS는 AWS에서 비교적 멀리 떨어져 있지만, 인접한 두 AQMS 데이터가 모델 학습에 사용되었기 때문에 두 모델에서 높은 정확도를 나타냈다. 반면 용산구 AQMS는 AQMS 및 AWS에서 비교적 멀리 떨어져 있기에 두 모델의 성능이 낮게 나타냈다.