Incorporating NASA space-borne precipitation research products into National Mosaic QPE operational system for improved precipitation measurements

Currently the NSSL/OU National Mosaic Quantitative Precipitation Estimation (NMQ) system incorporates data from all WSR-88D radars and automated rain gauge networks in the conterminous US. These automated rainfall estimates are transferred to forecasters in real time for product evaluation and eventual utilization in creating their operational precipitation products. Despite these recent advances, reliable ground-based precipitation measurements are difficult to obtain over all regions of the world, including many mountainous regions (e.g., Intermountain West of the US), due to the lack of adequate ground radar coverage from intervening terrain blockages. The limitations of rain gauges and weather radar systems and the successor of the more-than-decade-long high-quality space-borne weather radar highlight the attraction of space-based meteorological satellites to obtain seamless regional and global precipitation information from the vantage point of weather forecasting, modeling the hydrological cycle, and climate studies. Also, this study can enable us to continuously incorporate the to-be-upgraded GPM (Global Precipitation Mission) to the future polarimetric NEXRAD based NMQ system. In this study, we demonstrate the integration of the Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) products (4-km precipitation quantity, types, and 250 meter vertical profile of reflectivity) into the NMQ ground based rainfall estimation system to fill in gaps with existing NEXRAD radar coverage. We utilize the high resolution (250m) Vertical Profile of Reflectivity (VPR) provided by TRMM PR to improve the current NMQ VPR correction scheme for surface rainfall estimation in mountainous regions where ground radar has coarse resolution or overshooting. It is shown that the combined information helps to better resolve the vertical structure and leads to more accurate surface rainfall estimates.