기상 모델의 초기장 및 자료동화 차이에 따른 수도권 지역의 CMAQ 오존 예측 결과- 2007년 6월 수도권 고농도 오존 사례 연구 -
An impact of meteorological Initial field and data assimilation on CMAQ ozone prediction in the Seoul Metropolitan Area during June, 2007
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Air quality models have been widely used to study and simulate many air quality issues. In the simulation, it is important to raise the accuracy of meteorological predicted data because the results of air quality modeling is deeply connected with meteorological fields. Therefore in this study, we analyzed the effects of meteorological fields on the air quality simulation. This study was designed to evaluate MM5 predictions by using different initial condition data and different observations utilized in the data assimilation. Among meteorological scenarios according to these input data, the results of meteorological simulation using National Centers for Environmental Prediction (Final) Operational Global Analysis data were in closer agreement with the observations and resulted in better prediction on ozone concentration. And in Seoul, observations from Regional Meteorological Office for data assimilations of MM5 were suitable to predict ozone concentration. In other areas, data assimilation using both observations from Regional Meteorological Office and Automatical Weather System provided valid method to simulate the trends of meteorological fields and ozone concentrations. However, it is necessary to vertify the accuracy of AWS data in advance because slightly overestimated wind speed used in the data assimilation with AWS data could result in underestimation of high ozone concentrations.
Abstract
Air quality models have been widely used to study and simulate many air quality issues. In the simulation, it is important to raise the accuracy of meteorological predicted data because the results of air quality modeling is deeply connected with meteorological fields. Therefore in this study, we analyzed the effects of meteorological fields on the air quality simulation. This study was designed to evaluate MM5 predictions by using different initial condition data and different observations utilized in the data assimilation. Among meteorological scenarios according to these input data, the results of meteorological simulation using National Centers for Environmental Prediction (Final) Operational Global Analysis data were in closer agreement with the observations and resulted in better prediction on ozone concentration. And in Seoul, observations from Regional Meteorological Office for data assimilations of MM5 were suitable to predict ozone concentration. In other areas, data assimilation using both observations from Regional Meteorological Office and Automatical Weather System provided valid method to simulate the trends of meteorological fields and ozone concentrations. However, it is necessary to vertify the accuracy of AWS data in advance because slightly overestimated wind speed used in the data assimilation with AWS data could result in underestimation of high ozone concentrations.
참고문헌 (29)
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