 This study develops an aerosol data assimilation and forecast system using WIF chemistry and 3D-VAR to assimilate GOSI-AOD and surface PM observations in Northeast Asia at 15 km resolution during the Corus AQ intensive observing period. The simulation domain covers Northeast Asia at 15 km horizontal resolution, and the assimilation and forecast skill is evaluated for the Korea U.S. air quality, Corus 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 GOSI-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 GOSI-AOD suggests the non-negligible difference between column representing AOD and the surface PM concentration. This article was authored by Gang Han Kim, Sun Hee Lee, Jung Ho Im, and others. We are article.tv, links in the description below.