Abstract
Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars and this is widely done in the spectral domain via use of the Discrete Fourier Transform (DFT). To accomplish spectral clutter filtering, the I (in-phase) and Q (quadrature) time series are multiplied by a window function, such as the Blackman window, which suppresses clutter leakage. Subsequently, the clutter is filtered from the Doppler spectrum by setting spectral coefficients to zero where there is clutter signal. Recently, it has been shown with simulations that a regression clutter filter is a viable alternative. To demonstrate the regression filter, it is applied to radar data from both National Science Foundation-National Center for Atmospheric Research (NSF-NCAR) S-band polarimetric radar (S-Pol), and NEXRAD (Next Generation Weather Radar). A regression filter can be applied directly to the non-windowed time series data, thus avoiding the signal attenuation from a window function, which is required for spectral domain clutter filters such as GMAP (Gaussian Model Adaptive Processing). It is shown that the regression filter rejects clutter as effectively as the spectral technique, or better, but has the distinct advantage that standard error of estimates of the radar variables are in general improved by about 25% to 50% over a comparable spectral domain filter. The regression filter is straightforward and can be executed in real time.
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