The Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm combines the high spatial resolution and continuous availability of Geostationary Operational Environmental Satellite (GOES) data with the relative accuracy of microwave-based estimates of rainfall rate. This is accomplished by using discriminant analysis and stepwise multiple linear regression to select from a menu of available GOES-related parameters and calibrate them against Special Sensor Microwave/Imager (SSM/I)-based rain rate estimates in order to derive estimates of rainfall coverage and rate. A number of improvements have been made to the SCaMPR algorithm in anticipation of its transition to real-time production. These improvements include the expansion of the calibration data set by adding rain rate estimates from the Advanced Microwave Sounding Unit (AMSU) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the addition of new predictor data (including data from the GOES visible and near-IR channels, Eta model output fields, and several parameters derived from GOES IR data), and a self-calibrating correction for orographic influences on precipitation. The effects of these changes will be illustrated through several test cases covering a variety of seasons, geographic regions, and meteorological regimes.