H24E-04
Merging Radar Quantitative Precipitation Estimates (QPEs) from the High-resolution NEXRAD Reanalysis over CONUS with Rain-gauge Observations

Tuesday, 15 December 2015: 16:45
3022 (Moscone West)
Olivier P Prat1,2, Brian R Nelson3, Scott E Stevens2, Elsa Nickl2, Dong-Jun Seo4, Beomgeun Kim4, Jian Zhang5 and Youcun Qi6, (1)CICS-NC, Asheville, NC, United States, (2)CICS-NC/NCSU, Asheville, NC, United States, (3)NOAA/NCEI/CWC, Asheville, NC, United States, (4)University of Texas at Arlington, Arlington, TX, United States, (5)NOAA/NSSL, Norman, OK, United States, (6)CIMMS/OU, Norman, OK, United States
Abstract:
The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (Nexrad) network over the Continental United States (CONUS) is completed for the period covering from 2002 to 2011. While this constitutes a unique opportunity to study precipitation processes at higher resolution than conventionally possible (1-km, 5-min), the long-term radar-only product needs to be merged with in-situ information in order to be suitable for hydrological, meteorological and climatological applications.

The radar-gauge merging is performed by using rain gauge information at daily (Global Historical Climatology Network–Daily: GHCN-D), hourly (Hydrometeorological Automated Data System: HADS), and 5-min (Automated Surface Observing Systems: ASOS; Climate Reference Network: CRN) resolution. The challenges related to incorporating differing resolution and quality networks to generate long-term large-scale gridded estimates of precipitation are enormous. In that perspective, we are implementing techniques for merging the rain gauge datasets and the radar-only estimates such as Inverse Distance Weighting (IDW), Simple Kriging (SK), Ordinary Kriging (OK), and Conditional Bias-Penalized Kriging (CBPK). An evaluation of the different radar-gauge merging techniques is presented and we provide an estimate of uncertainty for the gridded estimates. In addition, comparisons with a suite of lower resolution QPEs derived from ground based radar measurements (Stage IV) are provided in order to give a detailed picture of the improvements and remaining challenges.