Abstract
Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.
Highlights
With the recent increase in chemical and aerosol observations in the troposphere, chemical data assimilation is expected to play an essential role in improving air quality forecasting, in the real-time environment
Various data assimilation techniques have been developed for many decades, they were predominantly applied in the context of numerical weather prediction (NWP) (Kalnay, 2003) and have not been extensively exploited for the prediction of air pollution
Surface PM2.5 observations marked as black dots show that the air quality becomes distinctively aggravated for the last 7 d, which is related to the long-range transport of air pollutants
Summary
With the recent increase in chemical and aerosol observations in the troposphere, chemical data assimilation is expected to play an essential role in improving air quality forecasting, in the real-time environment. Surface concentrations are directly affected by the transport and dispersion of chemical species through advection, convection, vertical diffusion, and surface fluxes. They are strongly driven by external forcing such as anthropogenic and natural emissions. For the operational air quality forecasting in South Korea, the Korean National Institute of Environmental Research (NIER) performs chemical simulations at 3 km resolution at present (Chang et al, 2016). For such a high-resolution application and for situations with very high aerosol concentrations, these fast-varying complex mecha-
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