Abstract: On October 18, 2003, the Defense Meteorological Satellite Program (DMSP) successfully launched the F-16 satellite with the Special Sensor Microwave Imager/Sounder (SSMIS) on board. However, this first SSMIS instrument exhibited several major measurement anomalies due to instabilities in its antenna emission and calibration target. Two algorithms have been developed at Naval Research Laboratory (NRL) and the National Oceanic and Atmospheric Administration (NOAA), respectively, for correcting for these anomalies. After removal of the calibration anomalies, SSMIS data are now much more useful for sounding product retrievals and data assimilation. NOAA generates SSMIS imager products from its legacy SSM/I algorithms. Several new algorithms have been developed to extract from SSMIS the information on clouds and precipitation. In the cloud ice water retrieval algorithm, a parametric relationship relates brightness temperatures to cloud ice water path and particle mean diameter. Atmospheric temperature and water vapor profiles are simultaneously retrieved along with cloud hydrometeor profiles through a one-dimensional variational (1D-Var) retrieval system, which works well under most atmospheric and surface conditions. Rootmean-square (RMS) errors of temperature and water vapor profiles from SSMIS are typically 2K and 15%, respectively, under all weather conditions. A new quality control algorithm and a bias correction algorithm have also been developed for SSMIS data assimilation. Assimilation of SSMIS data in the NOAA Global Forecast System (GFS) results in neutral and small positive impacts on global medium range forecast scores.