대기경계층 모수화 차이에 의한 WRF-CMAQ 미세먼지 모의 농도 차이 분석: 2019년 3월 수도권 연무 사례를 중심으로
In this study, the biases arising from different Planetary Boundary Layer (PBL) parameterizations were assessed in Seoul metropolitan area for the period of March 2∼6 2019, when a extremely high PM2.5 concentration were observed. We employed WRF-CMAQ and carried out three sensitivity tests from diff...
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Published in | 한국대기환경학회지(국문) Vol. 37; no. 6; pp. 835 - 852 |
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Main Authors | , |
Format | Journal Article |
Language | Korean |
Published |
한국대기환경학회
01.12.2021
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Subjects | |
Online Access | Get full text |
ISSN | 1598-7132 2383-5346 |
DOI | 10.5572/KOSAE.2021.37.6.835 |
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Summary: | In this study, the biases arising from different Planetary Boundary Layer (PBL) parameterizations were assessed in Seoul metropolitan area for the period of March 2∼6 2019, when a extremely high PM2.5 concentration were observed. We employed WRF-CMAQ and carried out three sensitivity tests from different PBL parameterizations: Yonsei University (YSU) as a nonlocal scheme, Mellor-Yamada-Janjic (MYJ) as a local scheme, and hybrid local-nonlocal scheme named Asymmetric Convective Model2 (ACM2). Our simulations of three different PBL schemes yielded 6∼30% of NMB (Normalized Mean Bias) of PBL height (PBLH) against observations between schemes, and showed different characteristics between day and night.
During daytime, nonlocal PBL schemes showed seemingly better PBLH simulations, whereas local scheme simulated better for nighttime for our study period. However, all schemes underestimated nocturnal PBLH, thereby inducing higher surface PM2.5 concentrations. On the average, the bias ranges of PM2.5 were about 11.1% against ground measurements. However, our PBLH tests induced 9.9% variances for daytime, whereas nighttime PM2.5 bias ranges were about 13.2%, indicating much higher uncertainties in nighttime PBLH for the given period. This suggests that more comprehensive measurement-modeling PBL studies especially for nighttime are essential for the improvement of daily mean or nocturnal PM2.5 prediction capabilities. KCI Citation Count: 0 |
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Bibliography: | https://doi.org/10.5572/KOSAE.2021.37.6.835 |
ISSN: | 1598-7132 2383-5346 |
DOI: | 10.5572/KOSAE.2021.37.6.835 |