This study presents an initial assessment of the quality of radiances measured from SAPHIR (Sounder for Probing Vertical Profiles of Humidity) on board Megha-Tropiques (Indo-French joint satellite), launched by the Indian Space Research Organisation on 12 October 2011. The radiances measured from SAPHIR are compared with those simulated by the radiative transfer model (RTM) using radiosondes measurements, Atmospheric Infrared Sounder retrievals, and National Centers for Environmental Prediction (NCEP) analyzed fields over the Indian subcontinent, during January to November 2012. The radiances from SAPHIR are also compared with the similar measurements available from Microwave Humidity Sounder (MHS) on board MetOp-A and NOAA-18/19 satellites, during January to November 2012. A limited comparison is also carried out between SAPHIR measured and the RTM computed radiances using European Centre for Medium-Range Weather Forecasts analyzed fields, during May and November 2012. The comparison of SAPHIR measured radiances with RTM simulated and MHS observed radiances reveals that SAPHIR observations are of good quality. After the initial assessment of the quality of the SAPHIR radiances, these radiances have been assimilated within the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system. Analysis/forecast cycling experiments with and without SAPHIR radiances are performed over the Indian region during the entire month of May 2012. The assimilation of SAPHIR radiances shows considerable improvements (with moisture analysis error reduction up to 30%) in the tropospheric analyses and forecast of moisture, temperature, and winds when compared to NCEP analyses and radiances measurement obtained from MHS, Advanced Microwave Sounding Unit-A, and High Resolution Infrared Sounder. Assimilation of SAPHIR radiances also resulted in substantial improvement in the precipitation forecast skill when compared with satellite-derived rain. Overall, initial results show the usefulness of SAPHIR radiances in the numerical weather prediction data assimilation systems.