Aerosol data assimilation and forecast using Geostationary Ocean Color Imager aerosol optical depth and in-situ observations during the KORUS-AQ observing period

Abstract

This study develops an aerosol data assimilation and forecast system using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the three-dimensional variational (3D-VAR) data assimilation method. The system assimilates the aerosol optical depth (AOD) from the Geostationary Ocean Color Imager (GOCI) satellite and surface particulate matter (PM) observations. The simulation domain covers Northeast Asia at 15 km horizontal resolution, and the assimilation and forecast skill is evaluated for the Korea-US Air Quality (KORUS-AQ) intensive observing period. Observing system experiments (OSEs) are conducted to examine the changes in quality of assimilation and forecast skills sensitive to the assimilated observational input data. The baseline model simulation underestimates AOD and surface PM concentration in most regions, in which the assimilation of satellite and in-situ data improves the mean biases and spatial distribution. Moreover, it improves the forecast skill of the surface concentration of PM10 and PM2.5. The results from the OSEs indicate that the assimilation of GOCI AOD only slightly enhances the forecast skill. However, most of the skill improvement comes from the surface PM assimilation, showing a practically useful level of skill until 12 hours from the initial state. The marginal improvement in the PM10 forecasts by the GOCI AOD suggests the non-negligible difference between column-representing AOD and the surface PM concentration.