Real-Time Radar Rainfall Estimation. Part II: Case Study

Abstract

The performance of a real-time radar rainfall estimation algorithm is examined based on an extensive dataset of volume scan reflectivity and rain gauge rainfall measurements from the WSR-88D site in Melbourne, Florida. Radar rainfall estimates are evaluated based on the following radar–rain gauge statistics: mean difference (bias), normalized root-mean-square difference, and correlation coefficient. The spatiotemporal scales of interest are hourly accumulations over 4 km × 4 km grids. First, the authors demonstrate the convergence properties of the algorithm’s adaptive parameter estimation procedure and conduct sensitivity tests of the system with respect to changes in the parameter values. Second, the major components of the algorithm are compared with the operational WSR-88D Precipitation Processing Subsystem. The authors show reduction in the radar–rain gauge root-mean-square difference up to 40%, resulting from the new parameterization schemes and the real-time calibration procedure. When rainfall classification is included, the reduction is higher (up to 50%). The authors show that correction for rain field advection moderately improves estimation accuracy (up to 20%). Finally, the authors show that the algorithm can effectively remove range-dependent systematic errors in radar observations.