초기조건과 배출량이 자료동화를 사용하는 미세먼지 예보에미치는 영향 분석

• Published in: 한국대기환경학회지, 31(5) pp. 430 - 436
• Main Authors: 박윤서, 장임석, 조석연
• Format: Journal Article
• Language: Korean
• Published: 한국대기환경학회 01.10.2015
• Subjects: 대기과학
• ISSN: 1598-7132; 2383-5346
• DOI: 10.5572/KOSAE.2015.31.5.430

Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November 2nd to December 1st of 2014. Halving the emission in China reduces the forecasted peak value of PM10 and SO2 in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including PM10 and SO2 in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as PM10 and SO2. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied. KCI Citation Count: 6