Evaluation of a Method to Enhance Real-Time, Ground Radar–Based Rainfall Estimates Using Climatological Profiles of Reflectivity from Space

Abstract

Abstract Over mountainous terrain, ground weather radars face limitations in monitoring surface precipitation as they are affected by radar beam blockages along with the range-dependent biases due to beam broadening and increase in altitude with range. These issues are compounded by precipitation structures that are relatively shallow and experience growth at low levels due to orographic enhancement. To improve surface precipitation estimation, researchers at the University of Oklahoma have demonstrated the benefits of integrating the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) products into the ground-based NEXRAD rainfall estimation system using a vertical profile of reflectivity (VPR) identification and enhancement (VPR-IE) approach. However, the temporal resolution of TRMM limits the application of VPR-IE method operationally. To implement the VPR-IE concept into the National Mosaic and Multi-Sensor QPE (NMQ) system in real time, climatological VPRs from 11 years of TRMM PR observations have been characterized for different stratiform/convective rain types, seasons, and surface rain intensities. Then, these representative profiles are used to adjust ground radar–based precipitation estimates in the NMQ system based on different precipitation structures. This study conducts a comprehensive evaluation of the newly developed climatological VPR-IE (CVPR-IE) method on winter events (January, February, and December) in 2011. The statistical analysis reveals that the CVPR-IE method provides a clear improvement over the original radar QPE in the NMQ system for the study region. Compared to physically based VPRs from real-time PR measurements, climatological VPRs have limitations in representing precipitation structure for individual events. A hybrid correction scheme incorporating both climatological and real-time VPR information is desired for better skill in the future.