Improving air quality forecasting with the assimilation of GOCI AOD retrievals

The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution 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 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).