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Abstract Due to differences between air and debris motions, debris centrifuging creates bias in wind estimates based on Doppler velocities and radar wind retrievals in tornadoes. Anomalous radial divergence, azimuthal wind underestimation, and vertical velocity bias associated with debris centrifuging can lead to erroneous interpretations of tornado intensity and structure from radar data. A novel spectral velocity correction technique is developed to reduce bias by identifying rain and debris motion in radar signals using dual-polarization spectral density estimation and fuzzy logic classification. This technique successfully improves Doppler velocity estimates in simulated S-band polarimetric time series data, although debris concentration modulates both the magnitude and correctability of velocity bias. Large bias magnitudes associated with high debris concentrations are the most difficult to fully correct using this technique, especially at low elevation angles and near the center of the tornado. However, the magnitudes of corrections applied are proportional to the original bias magnitudes, suggesting that the technique performs consistently across low and high debris concentrations. Spectral correction results in an overall 84% reduction in bias in simulations. The spectral correction technique is also applied to dual-polarization S-band radar observations of the 20 May 2013 Moore, Oklahoma tornado. Overall increases in Doppler velocity magnitudes, especially at lower elevation angles, imply that spectral correction can successfully reduce centrifuging bias in observed Doppler velocities.