Improving Coverage and Accuracy of Precipitation Estimates for NOAA and NWS through a Multi-Sensor Precipitation Scheme (78)
Steven Martinaitis, University of Oklahoma/CIMMS and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
Andrew Osborne, University of Oklahoma/CIMMS and NOAA/OAR/National Severe Storms Laboratory
Carrie Langston, University of Oklahoma/CIMMS and NOAA/OAR/National Severe Storms Laboratory
Jian Zhang, NOAA/OAR/National Severe Storms Laboratory
Kenneth Howard, NOAA/OAR/National Severe Storms Laboratory
Accurate high resolution QPE products are critical to operational decision making, flood prediction, and climatological assessments conducted by the National Oceanic and Atmospheric Administration (NOAA), the National Weather Service (NWS), and the National Water Center (NWC). Two primary sources for estimating precipitation are from radar and gauge networks however, there are considerable coverage gaps with both sensor suites, particularly in the intermountain West, Hawaii, and Alaska. A new precipitation product is being developed in the Multi-Radar Multi-Sensor (MRMS) system that blends together locally gauge-corrected radar quantitative precipitation estimates (QPE), the Mountain Mapper QPE product that maps hourly gauge observations onto background precipitation climatologies, satellite-derived QPE, and high resolution model quantitative precipitation forecasts (QPFs).
The radar-based QPE is the foundational precipitation source, and its influence within the new multi-sensor QPE product is defined by an updated MRMS Radar Quality Index product. Gaps in radar coverage are then filled using a combination of the other precipitation sources based on their strengths. Multiple fail safes are included to handle instances when precipitation products or variables that weight each source are missing. This multi-sensor QPE product for the MRMS system is initially developed for the contiguous United States and will eventually be implemented and tailored to other domains, such as Hawaii, Alaska, and the Caribbean. The final product will allow for improved hydrologic forcing for the National Water Model as well as provide a more accurate precipitation field to be used by the NWS and other entities for flood prediction and water resource management. The overall product design and case study results will be presented.