Journal article
Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia
Atmospheric environment (1994), Vol.271, 118921
12/2021
DOI: 10.1016/j.atmosenv.2021.118921
Abstract
To improve PM2.5 predictions in Northeast Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations. In contrast to the conventional method of BEC estimation that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. By taking the emission uncertainty into account, we found that the standard deviations in the BEC were significantly increased. The standard deviations became around three times larger than those in the conventional method at the surface. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over Northeast Asia using the Community Multiscale Air Quality (CMAQ) model initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1535 sites in China were assimilated every six hours during the campaign period of the Korea-United States Air Quality Study (KORUS-AQ) (1 May–14 June 2016). The data assimilation with the new BEC showed better agreement with the surface PM2.5 observations than with the BEC from the conventional method. Our method was also more consistent with the observations in 24-hour PM2.5 predictions than the conventional method (specifically, with a ∼44% reduction of negative biases). We concluded that increased standard deviations, together with updated horizontal and vertical length scales in the new BEC, improved the data assimilation and short-term predictions of the surface PM2.5. This paper also suggests several research efforts to further improve the BEC for better short-term PM2.5 predictions in Northeast Asia.
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•A new background error covariance(BEC) for PM2.5 predictions was developed.•The new BEC considers the uncertainties in anthropogenic emissions.•Two different emission inventories were used to account for the uncertainty.•24-hour PM2.5 predictions were improved with ∼44% fewer negative biases.
Details
- Title: Subtitle
- Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia
- Creators
- Sojin Lee - Gwangju Institute of Science and TechnologyChul Han Song - Gwangju Institute of Science and TechnologyKyung Man Han - Gwangju Institute of Science and TechnologyDaven K Henze - University of Colorado BoulderKyunghwa Lee - Gwangju Institute of Science and TechnologyJinhyeok Yu - Gwangju Institute of Science and TechnologyJung-Hun Woo - Konkuk UniversityJia Jung - University of HoustonYunsoo Choi - University of HoustonPablo E Saide - University of California, Los AngelesGregory R Carmichael - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Atmospheric environment (1994), Vol.271, 118921
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.atmosenv.2021.118921
- ISSN
- 1352-2310
- eISSN
- 1873-2844
- Language
- English
- Date published
- 12/2021
- Academic Unit
- Center for Global & Regional Environmental Research; Nursing; Chemical and Biochemical Engineering; Civil and Environmental Engineering
- Record Identifier
- 9984203159702771
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