한국대기환경학회지 (Journal of Korean Society for Atmospheric Environment)

  • 제33권1호
  • /
  • Pages.11-18
  • /
  • 2017
  • /
  • 1598-7132(pISSN)
  • /
  • 2383-5346(eISSN)
한국대기환경학회 (Korean Society for Atmospheric Environment)
권호 표지
DOI QR코드

DOI QR Code

PM2.5 예보를 위한 모델 성능평가와 편차보정 효과 분석

Model Performance Evaluation and Bias Correction Effect Analysis for Forecasting PM2.5 Concentrations

  • 김영성 (한국외국어대학교 환경학과) ;
  • 최용주 (한국외국어대학교 환경학과) ;
  • 김순태 (아주대학교 환경안전공학과) ;
  • 배창한 (아주대학교 환경안전공학과) ;
  • 박진수 (국립환경과학원 대기환경연구과) ;
  • 신혜정 (국립환경과학원 대기환경연구과)
  • Ghim, Young Sung (Department of Environmental Science, Hankuk University of Foreign Studies) ;
  • Choi, Yongjoo (Department of Environmental Science, Hankuk University of Foreign Studies) ;
  • Kim, Soontae (Department of Environmental and Safety Engineering, Ajou University) ;
  • Bae, Chang Han (Department of Environmental and Safety Engineering, Ajou University) ;
  • Park, Jinsoo (Air Quality Research Division, National Institute of Environmental Research) ;
  • Shin, Hye Jung (Air Quality Research Division, National Institute of Environmental Research)
  • 투고 : 2016.11.26
  • 심사 : 2017.01.02
  • 발행 : 2017.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The performance of a modeling system consisting of WRF model v3.3 and CMAQ model v4.7.1 for forecasting $PM_{2.5}$ concentrations were evaluated during the period May 2012 through December 2014. Twenty-four hour averages of $PM_{2.5}$ and its major components obtained through filter sampling at the Bulgwang intensive measurement station were used for comparison. The mean predicted $PM_{2.5}$ concentration over the entire period was 68% of the mean measured value. Predicted concentrations for major components were underestimated except for $NO_3{^-}$. The model performance for $PM_{2.5}$ generally tended to degrade with increasing the concentration level. However, the mean fractional bias (MFB) for high concentration above the $80^{th}$ percentile fell within the criteria, the level of accuracy acceptable for standard model applications. Among three bias correction methods, the ratio adjustment was generally most effective in improving the performance. Albeit for limited test conditions, this analysis demonstrated that the effects of bias correction were larger when using the data with a larger bias of predicted values from measurement values.

키워드

참고문헌

  1. Appel, K.W., P.V. Bhave, A.B. Gilliland, G. Sarwar, and S.J. Roselle (2008) Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part IIparticulate matter, Atmospheric Environment, 42, 6057-6066. https://doi.org/10.1016/j.atmosenv.2008.03.036
  2. Boylan, J.W. and A.G. Russell (2006) PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models, Atmospheric Environment, 40, 4946-4959. https://doi.org/10.1016/j.atmosenv.2005.09.087
  3. Byun, D.W. and K.L. Schere (2006) Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system, Applied Mechanics Reviews, 59, 51-77. https://doi.org/10.1115/1.2128636
  4. Ghim, Y.S., Y. Choi, S. Kim, C.H. Bae, J. Park, and H.J. Shin (2016) Evaluation of model performance for forecasting fine particle concentrations in Korea using measurement data at the surface (in revision).
  5. Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P.I. Palmer, and C. Geron (2006) Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmospheric Chemistry and Physics, 6, 3181-3210. https://doi.org/10.5194/acp-6-3181-2006
  6. Jeon, H., J. Park, H. Kim, M. Sung, J. Choi, Y. Hong, and J. Hong (2015) The Characteristics of $PM_{2.5}$ concentration and chemical composition of Seoul metropolitan and inflow background area in Korea Peninsula, Journal of the Korean Society of Urban Environment, 15, 261-271. (in Korean with English abstract)
  7. Koo, Y.S., S.-T. Kim, J.-S. Cho, and Y.-K. Jang (2012) Performance evaluation of the updated air quality forecasting system for Seoul predicting $PM_{10}$, Atmospheric Environment, 58, 56-69. https://doi.org/10.1016/j.atmosenv.2012.02.004
  8. McKeen, S., J. Wilczak, G. Grell, I. Djalalova, S. Peckham, E.-Y. Hsie, W. Gong, V. Bouchet, R. Moffet, J. McHenry, J. McQueen, Y. Tang, G.R. Carmichael, M. Pagowski, A. Chan, T. Dye, G. Frost, P. Lee, and R. Mathur (2005) Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004, Journal of Geophysical Research, 110, D21307, doi:10.1029/2005JD005858.
  9. ME (Ministry of Environment) (2013) Improvement of Urbanscale Forecasting System for Particulate Matter (IV), Prepared by Ajou University. (in Korean)
  10. ME and NIER (National Institute of Environmental Research) (2014) Press release, Division of Climate and Air Quality Policy of ME, and Korean Integrated Air Quality Forecast Center and Department of Air Quality Research of NIER, October 27. (in Korean)
  11. NIER (2014) Study on Optimization of the Forecasting Model for Particulate Matter, Prepared by Inha University, Enitech, and Yeungnam University. (in Korean)
  12. Skamarock, W.C. and J.B. Klemp (2008) A time-split nonhydrostatic atmospheric model for weather research and forecasting applications, Journal of Computational Physics, 227, 3465-3485. https://doi.org/10.1016/j.jcp.2007.01.037
  13. USEPA (United States Environmental Protection Agency) (2003) Guidelines for Developing an Air Quality (Ozone and $PM_{2.5}$) Forecasting Program, Research Triangle Park, NC.
  14. Zhang, H., G. Chen, J. Hu, S.H. Chen, C. Wiedinmyer, M. Kleeman, and Q. Ying (2014) Evaluation of a sevenyear air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States, Science of the Total Environment, 473, 275-285.
  15. Zhang, Q., D.G. Streets, G.R. Carmichael, K.B. He, H. Huo, A. Kannari, Z. Klimont, I.S. Park, S. Reddy, J.S. Fu, D. Chen, L. Duan, Y. Lei, L.T. Wang, and Z.L. Yao (2009) Asian emissions in 2006 for the NASA INTEX-B mission, Atmospheric Chemistry and Physics, 9, 5131-5153. https://doi.org/10.5194/acp-9-5131-2009
  16. Zhang, Y., M. Bocquet, V. Mallet, C. Seigneur, and A. Baklanov (2012) Real-time air quality forecasting, Part II: State of the science, current research needs, and future prospects, Atmospheric Environment, 60, 656-676. https://doi.org/10.1016/j.atmosenv.2012.02.041

피인용 문헌

  1. Research and Policy Directions against Ambient Fine Particles vol.33, pp.3, 2017, https://doi.org/10.5572/KOSAE.2017.33.3.191