In this study, ab initio atmospheric profiles generated through high-resolution calculations from the community weather model WRF, suitably matched up with both TRMM Microwave Imager (TMI) and Precipitation Radar (PR) instruments of the TRMM satellite were used to compute simulated brightness temperatures (BTs) corresponding to SAPHIR frequencies, through an in-house polarized radiative transfer code. An artificial neural network was then constructed and trained to return the near-surface rain (NSR) rate given the six BTs corresponding to SAPHIR. For accomplishing the retrievals, measured BTs of SAPHIR (level 1 data) were used. NSR rates were calculated for two precipitating systems, namely (i) cyclone Neelam and (ii) cyclone Phailin. Rain rates thus estimated were then validated with the TMI-PR combined rain product of TRMM (2A12). The results showed that there is good agreement between the two. An inter-comparison between rain rates derived from MADRAS and SAPHIR was also done. This unexpected ability of the SAPHIR radiances provide us with the rainfall signature opens up new vistas in achieving the mission objectives of Megha-Tropiques.