AbstractConcentrations of ambient particulate matter (such as PM_2.5 and PM₁₀) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM_2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM_2.5 prediction systems. The short-term predictability of PM_2.5 (and PM₁₀) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM_2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM_2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM_2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM_2.5 prediction systems using only LEO satellite-derived observations.