Evolving methodologies for multisensor precipitation estimation are being investigated to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and Quantitative Precipitation algorithm package (NMQ) under development at the National Severe Storms Laboratory, the Multisensor Precipitation Estimator package (MPE) currently operational at National Weather Service field offices, and the Self-Calibrating Multivariate Precipitation Retrieval algorithm (SCaMPR) under development within the National Environmental Satellite, Data, and Information Service Center for Satellite Applications and Research. Our goal is to determine which combination of algorithm features offer the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, range correction, and methods of multiple-radar data compositing, all of which vary among NMQ, MPE, and SCaMPR.

All methods described above have been applied to deriving high-resolution (4-km hourly) gridded precipitation estimates over and near the Tar River Basin of North Carolina through the course of several precipitation events during the period December 2004-January 2005. The NMQ and MPE algorithms are driven by identical WSR-88D reflectivity and rain gauge input. The GOES infrared-based SCaMPR algorithm is calibrated by comparison with contemporaneous radar rainrate fields from the NMQ. All results are currently being compared to an independent set of hourly and daily rain gauge reports; the various precipitation grids will later be input to the NWS Hydrology Laboratory - Research Distributed Hydrologic Model, to determine impacts on the quality of its discharge simulations at several gauged points on the Tar River and its tributaries. Further results will be reported in our extended abstract.