To quantify the impact of the direct aerosol effect accurately, this study incorporated the Geostationary Ocean Color Imager (GOCI) aerosol optical depth (AOD) into a coupled meteorology-chemistry model. We designed three model simulations to observe the impact of AOD assimilation and aerosol feedback during the KORUS-AQ campaign (May–June 2016). By assimilating the GOCI AOD with high temporal and spatial resolutions, we improve the statistics from the comparison AOD and AERONET data (root-mean-square error: 0.12, R: 0.77, index of agreement: 0.69, mean-absolute error: 0.08). The inclusion of the direct effect of aerosols produces the best model performance (root-mean-square error: 0.10, R: 0.86, index of agreement: 0.72, mean-absolute error: 0.07). AOD values increased as much as 0.15, which is associated with an average reduction in solar radiation of -31.39 W/m2, a planetary boundary layer height (-104.70 m), an air temperature (-0.58 °C), and a surface wind speed (-0.07 m/s) over land. In addition, concentrations of major gaseous and particulate pollutants at the surface (SO2, NO2, NH3, (Formula presented.), (Formula presented.), (Formula presented.), and PM2.5) increase by 7.87–34%, while OH concentration decreases by -4.58%. Changes in meteorology and air quality appear to be more significant in high-aerosol loading areas. The integrated process rate analysis shows decelerated vertical transport, resulting in an accumulation of air pollutants near the surface and the amount of nitrate, which is higher than that of sulfate because of its response to reduced temperature. We conclude that constraining aerosol concentrations using geostationary satellite data is a prerequisite for quantifying the impact of aerosols on meteorology and air quality.
Bibliographical noteFunding Information:
This study was partially supported by the National Institute of Environment Research (NIER) and the National Strategic Project-Fine Particle of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of Health and Welfare (MOHW) (NRF-2017M3D8A1092022). We would like to thank all of the scientists and administrators who prepared the GOCI and MODIS satellite data, the AERONET data, the RTG SST data, the FNL data, and the DC-8 aircraft measurements data during the KORUS-AQ campaign period. The GOCI AOD YAER V2 data were provided by Jhoon Kim. The Terra and Aqua MODIS Level 2 AOD data were obtained from the Level-1 and Atmosphere Archive and distribution system (LAADS) Distributed Active Archive Center (DAAC), of the Goddard Space Flight Center (https://ladsweb.modaps.eosdis.nasa.gov). The AERONET data can be retrieved from https://aeronet.gsfc.nasa.gov. The CALIPSO data were obtained from https://subset.larc.nasa.gov/calipso. The NCEP FNL operational global analysis data are available from https://rda.ucar.edu/datasets/ds083.2. The RTG SST data are available from https://polar.ncep.noaa.gov/sst/ophi. The NASA DC-8 aircraft measurements data during the KORUS-AQ campaign are available to download from https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq?MERGE=1 (DOI: 10.5067/Suborbital/KORUSAQ/DATA 01). The model outputs from the primary experiments can be downloaded from ftp://spock.geosc.uh.edu/outgoing/JGR_2019_JIAJUNG. The simulations were run on the University of Houston Linux clusters.
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All Science Journal Classification (ASJC) codes
- Atmospheric Science
- Earth and Planetary Sciences (miscellaneous)
- Space and Planetary Science