The TRMM standard rainfall algorithm for the passive microwave sensor (TMI) is known as the Goddard Profiling algorithm. It is a Bayesian scheme that makes use of a cloud resolving model generated a-priori database. The major strength of this scheme resides in the a-priori data base which, while imperfect, is nonetheless the same for any radiometer such as SSMI and AMSR-E. This allows one to apply the same algorithm to distinct sensors flying at different local times without having to tune results to one another. The weakness of the method, unfortunately, was also the a-priori database, which was not complete and could have problems correctly representing the true state of the global precipitation profiles. To overcome this difficulty, a new database using TRMM radar and cloud resolving models has been created to faithfully reproduce observed precipitation profiles. Radar profiles are then adjusted to be consistent with the simultaneous TMI observations. This latter step ensures physical consistency among the sensors and the a-priori database. Results from the Goddard Profiling Algorithm (Version 2008) will be presented. The retrieval is able to reconstruct a-priori database statistics with very high fidelity for both instantaneous scenes as well as longer space and time averages. In addition, the nature of the a-priori database now allows for a much more comprehensive error estimate than has previously been possible.