Quality Control of Canadian Radar Reflectivity Data

1. Partial funding for this research was provided under NOAA-OU Cooperative Agreement #NA17RJ1227 Quality Control of Canadian Radar Reflectivity Data Valliappa Lakshmanan, Jian Zhang, Carrie Langston University of Oklahoma & National Severe Storms Laboratory, Norman OK, USA What we did New version 88D version Raw data We modified the WDSS-II Quality Control Neural Network (QCNN) so that it would be able to QC Canadian radar reflectivity data XDR June 6, ‘07 Why Canadian data? So that we can include Canadian data into our 4-dimensional real-time reflectivity mosaics. These mosaics are used by both severe weather algorithms and by precipitation estimation algorithms at NSSL. New version 88D version Raw data XDR June 6, ‘07 Why Adapt QCNN? • Operates in real-time, on virtual volumes so that elevations are cleaned tilt-by-tilt. • Excellent preprocessing filters • Speckle removal • Entropy check to remove radar test patterns • Sun-strobe check to remove radial contamination • Neural network trained to classify range gates • Based on texture features computed from reflectivity, 3D volumetric features of reflectivity, velocity and spectrum width • Objective and data-driven technique • Post-processing based on region growing provides high (99.9%) accuracy New version 88D version Raw data WGJ Oct 1, ‘07 The changes we made to QCNN and reason for change Can I try QCNN on my radar data? Yes, you can! Download the software from http://www.wdssii.org/ Please do stop me if you see me in the hallway! I’d love to address any questions or comments.