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Considering that the statistics of the phase and the power of weather signals in the spectral domain are different from those statistics for echoes from stationary objects, a spectrum clutter identification (SCI) algorithm has been developed to detect ground clutter using single polarization radars, but SCI can be extended for dual-pol radars. SCI examines both the power and phase in the spectral domain and uses a simple Bayesian classifier to combine four discriminants: spectral power distribution, spectral phase fluctuations, spatial texture of echo power, and spatial texture of spectrum width to make decisions as to the presence of clutter that can corrupt meteorological measurements. This work is focused on detecting ground clutter mixed with weather signals, even if the clutter power to signal power ratio is low. The performance of the SCI algorithm is shown by applying it to radar data collected by University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering.