Mean-Field Rainfall Bias Studies for WSR-88D

Abstract

Real-time radar-rainfall bias adjustment procedures for weather surveillance Doppler radar (WSR-88D) are investigated. Statistical analysis of the mean-field bias is performed on a 2-year record of WSR-88D observations from Tulsa, Okla., and rain gauge measurements from a dense network under the radar umbrella. The analysis shows strong seasonal effect. A data-based Monte Carlo simulation experiment is performed on the same data to quantify the sampling error of estimated bias for varying rain gauge network densities. Simulation results show (1) that the sampling error decreases proportionally to the square of the rain gauge network density; and (2) that the sampling error is higher in the warm season. The performance of three mean-field radar-rainfall bias estimation and prediction algorithms is investigated. The algorithms include the WSR-88D precipitation adjustment procedure, the adaptive error parameter technique, and the maximum likelihood autoregressive model. A Monte Carlo simulation experiment is used to assess the algorithms' error statistics for the two seasons, three accumulation timescales, and two modes of operation (prediction and update). Results show significant seasonal and time-scale effects.