Identification and Mitigation of Wind Turbine Clutter using Spectral CMD

Wind Turbine Clutter (WTC) is more difficult to mitigate than stationary clutter, because the motion of the turbine blades leads to echoes that spread the returned power across the Doppler spectrum. We present an approach that treats dual polarization spectra as 2-dimensional fields of (M gates x N Doppler spectral points), where N is the length of the time series for a gate of radar data. The outcome provides a representation of ZDR, PHIDP and RHOHV in the Doppler spectral domain. Using lessons learned from the well-tested Clutter Mitigation Decision (CMD) algorithm for the detection of stationary normal propagation (NP) and anomalous propagation (AP) clutter, we use the various spectral representations of the radar variables as a function of radar range to construct 2-D fields to compute so-called 'texture' metrics. Combining these metrics using fuzzy logic leads to a new 'Spectral CMD' field, from which we can deduce the presence of Wind Turbine Clutter. We also describe and demonstrate a method for removing the clutter in the spectral domain, after which the moments can be computed and displayed. The results show that the spectral CMD approach has potential for wind turbine clutter detection and mitigation. Unfortunately the mitigation step generally results in the removal of the majority of the spectral points so that estimates of any possible underlying weather data are difficult.