At this time, most current data assimilation systems use dewpoint depression data, converted to an appropriate moisture variable (relative humidity or mixing ratio), provided by rawinsondes as the lone source of moisture information. Because of the poor spatial and temporal characteristics of this data, additional moisture data are necessary to better resolve the global moisture field. This study investigates the impact of using the Special Sensor Microwave/Imager (SSM/I) total precipitable water (TPW) estimates as an additional source of moisture information. One forecast and four data assimilation experiments were performed to determine the impact of assimilating SSM/I TPW estimates into the NASA/Goddard Earth Observing System (version 1 ) Data Assimilation System (GEOS-1 DAS). It is shown that assimilation of SSM/I TPW estimates improves the precipitation pattern in the Tropics. In addition, a known dry bias in the GEOS-1 DAS was reduced by over 50% and observation minus first guess (O − F) error variance is reduced by nearly 50% after only 3 days of assimilation. Improvements were also noted in monthly and 6-h-averaged precipitation patterns when compared to other independent estimates.