On the Use of a Radial-Based Noise Power Estimation Technique to Improve Estimates of the Correlation Coefficient on Dual-Polarization Weather Radars

Abstract

weather surveillance radar antenna intercepts thermal radiation from various sources, including the ground, the sun, the sky, and precipitation. In the radar receiver, this external radiation produces noise that adds to the receiver internal noise and results in the system noise power varying with the antenna position. If these variations are not captured, they translate into erroneous signal powers because these are computed via subtraction of noise power measurements from the overall power estimates. This may lead to biased meteorological variables at low to moderate signal-to-noise ratios if those are computed using signal power estimates. In dual-polarization radars, this problem is even more pronounced, particularly for correlation coefficient estimates that use noise power measurements from both the horizontal and vertical channels. An alternative is to use estimators that eliminate the need for noise corrections but require sufficient correlation of signals in sample time, which limits their applicability. Therefore, when the use of the latter is inappropriate, the quality of correlation coefficient estimates can be improved by computing them using sufficiently accurate noise powers measured at each antenna position. An effective technique that estimates the noise powers in real time at each scan direction and in parallel with weather data collection has been proposed. Herein, the impacts of such a technique on the estimation of the correlation coefficient are investigated. The results indicate that the use of more accurate noise power estimates can significantly reduce the bias of correlation coefficient estimates, thus visibly improving the correlation coefficient fields. This is expected because the correlation coefficient is computed using noise power measurements from both the horizontal and vertical channels.