A data fusion toolbox to optimize precipitation estimates for land surface modeling applications

Decision making agencies, such as NOAA, USBR and USGS, are faced with the problem of inadequate rainfall estimates in the western regions of the United States which does not have a adequate network of in-situ measurements. Hence, satellite-based rainfall estimates offer the promise of improving the precipitation estimates in data-sparse regions with difficult water management problems. The precipitation estimates from the planned Global Precipitation Measurement (GPM) mission will complement a host of existing rainfall products. We are evaluating existing intelligent techniques and also developing new sophisticated techniques to merge various precipitation products and optimize them for land surface and hydrological modeling applications. Since, the planned GPM constellation is still currently under design, a suite of GPM proxy data are being produced using the NRL-Blend Algorithm using different combinations of existing satellites, currently in orbit.