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An inverse modeling system for estimating Asian dust emissions was developed by combining the GEOS-Chem chemical transport model with the Green’s function method. We applied the system to two heavy dust storms that occurred in 2014 (10-25 March and 24 May to 5 June), using surface-based polarization optical particle counter (POPC) observations at Fukuoka. Validation by independent observation datasets, including POPC measurements and PM10 observations at Seoul, showed that the use of a posteriori dust emissions improved overestimations in the a priori simulation and achieved much better agreement with observations. Satellite observations, surface synoptic observations, and modeled wind fields indicated that the major dust source region differed between the two dust storms; the major dust outbreak of one storm occurred in the northeastern Gobi Desert, whereas that of the other occurred in the southern Gobi Desert. The a posteriori dust emissions successfully reproduced this difference. Thus, the inverse modeling system developed in this study was able to improve the estimation of not only the intensity but also the geographical distribution of dust emissions.