The objective of our work is to develop a physically based algorithm to estimate orographic rainfall fields at high spatial and temporal resolution and with high-accuracy and statistical rigor relying on infrared imagery from Meteosat, and passive (TRMM-TMI) and active microwave (TRMM-PR) data, simulations using a cloud-scale model (CSM), and raingauge observations. This algorithm is assembled by combining a suite of algorithms that build on current and previous TRMM related research (Barros et al. 2000, Lang and Barros 2002, Barros and Lang 2003, Lang and Barros 2003, Barros et al. 2003 and 2004, Magagi and Barros 2003) including: 1) a scaling algorithm to extrapolate limited-area satellite data from specific overpasses to a region of interest; 2) a sliding 3D Fourier Transform based algorithm to combine IR and microwave (passive and active) data to expand the spatial domain of TRMM-PR overpasses (regionalization algorithm); 3) a data-assimilation algorithm to blend high-resolution NWP model output, reconstructed PR fields and raingauge observations; and finally 4) a rainfall estimation algorithm to improve the spatial and temporal resolution of existing products. In this paper, we present the algorithm and test evaluation results for a four-year period (1999-2002) in the Himalayas.