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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-116
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-116
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 15 May 2020

Submitted as: development and technical paper | 15 May 2020

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This preprint is currently under review for the journal GMD.

The impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions in CMAQ v5.2.1 over East Asia

Sojin Lee1,2, Chul Han Song3, Kyung Man Han3, Daven K. Henze4, Kyunghwa Lee3,5, Jinhyeok Yu3, Jung-Hun Woo6, Jia Jung7, Yunsoo Choi7, Pablo E. Saide8, and Gregory R. Carmichael9 Sojin Lee et al.
  • 1The Seoul Institute, Seoul, South Korea
  • 2Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, South Korea
  • 3School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
  • 4Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
  • 5Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research (NIER), Incheon, South of Korea
  • 6Department of Advanced Technology Fusion, Konkuk University, Seoul, 143-701, South Korea
  • 7Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA
  • 8Department of Atmospheric and Oceanic Sciences, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA
  • 9Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA

Abstract. For the purpose of improving PM prediction skills in East Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations that accounts for the uncertainties in anthropogenic emissions. In contrast to the conventional method to estimate the BEC that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over East Asia using Community Multi-scale Air Quality (CMAQ) initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1,535 sites in China were assimilated every six hours during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–14 June 2016). Data assimilation with our new BEC showed better agreement with the surface PM2.5 observations than that with the conventional method. Our method also showed closer agreement with the observations in 24-hour PM2.5 predictions with ~ 44 % fewer negative biases than the conventional method. We conclude that increased standard deviations, together with horizontal and vertical length scales in the new BEC, tend to improve the data assimilation and short-term predictions for the surface PM2.5. This paper also suggests further research efforts devoted to estimating the BEC to improve PM2.5 predictions.

Sojin Lee et al.

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