Assessment of Bias in the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Reanalysis Radar-Only Estimate
Brian R Nelson, NOAA Asheville, NCEI/CWC, Asheville, NC, United States, Olivier P Prat, North Carolina Institute for Climate Studies, North Carolina State University, Asheville, United States, Scott E Stevens, Cooperative Institute for Climate and Satellite North Carolina State, Asheville, NC, United States, Dong-Jun Seo, University of Texas at Arlington, Civil Engineeing (Water Resources), Arlington, TX, United States, Jian Zhang, NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma, United States and Kenneth Howard, National Severe Storms Lab, Norman, OK, United States
Abstract:
The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) based on the WSR-88D Next‐generation Radar (NEXRAD) network over Continental United States (CONUS) is nearly completed for the period covering from 2001 to 2012. Reanalysis data are available at 1-km and 5-minute resolution. An important step in generating the best possible precipitation data is to assess the bias in the radar-only product.
In this work, we use data from a combination of rain gauge networks to assess the bias in the NMQ reanalysis. Rain gauge networks such as the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), the Climate Reference Network (CRN), and the Global Historical Climatology Network Daily (GHCN-D) are combined for use in the assessment. These rain gauge networks vary in spatial density and temporal resolution. The challenge hence is to optimally utilize them to assess the bias at the finest resolution possible. For initial assessment, we propose to subset the CONUS data in climatologically representative domains, and perform bias assessment using information in the Q2 dataset on precipitation type and phase.
