A Maximum Likelihood Approach to Estimation of Vector Velocity in Doppler Radar Networks

In this paper, a vector velocity estimation approach based on the maximum likelihood technique operating on moment data within the overlapping area of a network of Doppler radars is presented. The relationships between the estimated vector velocity, the statistics of the measured signal, the characteristics of the observing geometry and volume, and the hardware and signal processing parameters are all derived. The most relevant error sources to the network measurements are derived and incorporated into the overall estimation process. The relationship between the measurement and estimation errors is identified, and exploited, so that estimation performance can be measured and, if necessary, improved through the means of error norm minimization. Techniques for mitigating errors in the synthesized reflectivity and velocity folding are presented as well. Results with error metrics are shown for several typical weather observation scenarios that include error sources and simulated data. Finally, it is shown how the technique may be used to provide useful information for the design and intercomparison of various Doppler radar network geometries.