A Bayesian Approach for Integrated Raindrop Size Distribution (DSD) Retrieval on an X-Band Dual-Polarization Radar Network

A raindrop size distribution (DSD) retrieval method for a weather radar network consisting of several X-band dual-polarization radars is proposed. An iterative maximum likelihood (ML) estimator for DSD retrieval in a single radar was developed in the authors' previous work, and the proposed algorithm in this paper extends the single-radar retrieval to radar-networked retrieval, where ML solutions in each single-radar node are integrated based on a Bayesian scheme in order to reduce estimation errors and to enhance accuracy. Statistical evaluations of the proposed algorithm were carried out using numerical simulations. The results with eight radar nodes showed that the bias and standard errors are -0.05 and 0.09 in log(N-w); and N-w (mm(-1) m(-3)) and 0.04 and 0.09 in D-0 (mm) in an environment with fluctuations in dual-polarization radar measurements (normal distributions with standard deviations of 0.8 dBZ, 0.2 dB, and 1.5 degrees in Z(Hm), Z(DRm), and phi(DPm), respectively). Further error analyses indicated that the estimation accuracy depended on the number of radar nodes, the ranges of varying mu, the raindrop axis ratio model, and the system bias errors in dual-polarization radar measurements.