The Office of Hydrologic Development of the National Weather Service (NWS/OHD) has developed and implemented an operational quantitative precipitation estimation algorithm called the Multisensor Precipitation Estimator (MPE). This algorithm optimally merges radar rainfall estimates with rain gauge rainfall data and produces multisensor precipitation estimates. However, this algorithm has limited applicability in regions where the radar beam is blocked by terrain and the rain gauge network density is poor, such as the mountainous regions of the western continental United States. A possible solution to this problem is to use satellite precipitation estimates to fill the gaps where radar has difficulty ‘seeing' precipitation.

In this study, we explore integrating the Hydroestimator product produced by the National Environmental Satellite Data and Information Service (NESDIS) into MPE. These products are derived from the GOES based infrared sensor and Numerical Weather Prediction (NWP) model output. The Hydroestimator products are routinely available in the Advanced Weather Interactive Processing System (AWIPS), but are not readily usable quantitatively for operational hydrologic forecasting because of a lack of absolute accuracy. Following the MPE paradigm, in this work we correct systematic biases in these data and optimally merge the bias-corrected satellite precipitation estimates (SPE) with the rain gauge data. Hourly bias-corrected SPE and satellite-gauge merged products thus generated are validated against independent cooperative rain gauge network. The results of this study from one region in the eastern United States and one region in the western United States for the period October 2002 to June 2003 will be presented.