The wind power industry has been growing extremely fast in the past several years. As more extremely large wind turbines are being installed within the vicinity of radars, their Electromagnetic Interference (EMI) to current radar networks cannot be ignored. The EMI caused by wind turbines is called Wind Turbine Clutter (WTC). Unlike stationary ground clutter, an operating wind turbine has moving rotor blades, which create complicated time-varying Doppler spectrum modulation. Therefore, classic ground clutter filters fail to remove WTC from radar data. Weather radar is especially susceptible to WTC because the data quality relies on accurate spectral moment estimations.

WTC spectrum modulation patterns depend not only on the shape, size, and material of the wind turbine, but also various motions like blade rotation, yaw and pitch. Therefore, the returned signal within the time the radar scans over a wind turbine is not easily predicted from scan to scan. In order to remove this non-stationary interference from weather signal, the mitigation scheme must be adaptive.

Most clutter filters focus on the spectral characteristics of the clutter and design the optimal filter response to null the clutter spectrum while preserving useful target information. Thus, the ideal adaptive WTC clutter filter needs to track the spectrum change of all WTC contaminated data from scan to scan, which is practically impossible considering the unpredictability. The method proposed here, however, will focus on the spectral characteristics of weather signal and alleviate WTC in Doppler domain.

The Power Spectral Density (PSD) represents the power weighted radial velocity distribution within a resolution volume. On the contrary, the distribution of power weighted radial velocity estimations from resolution cells within the same area can be considered the PSD of that area. If the weather signal has spatial continuity, which it should be the case under stratiform conditions, for example, then the PSD of a large area can be used as a prior information to weight the PSD of each cell. This adaptive weighting can help alleviate clutter distinguishable from weather in spectrum domain. WTC detection is first performed to locate contaminated cells. Power and radial velocity are estimated from nearby uncontaminated cells classified as weather. The normalized PSD of the entire area is generated from these estimates and used to weight the PSD of each contaminated cell. This adaptive WTC mitigation processing is tested on WSR-88D data of a large wind farm in Kansas. The results show that the accuracy of moment estimates is greatly improved.