Improved Signal Statistics using a Regression Ground Clutter

The identification, filtering of ground clutter echoes thus separating them from weather radar echo is an ongoing area of research. Most weather radar groups employ a spectral domain technique that requires that the time series is first multiplied by a window function and then spectrum is calculated where spectral components around zero velocity are set to zero.

A disadvantage of applying a window function to the time series is that it attenuates the signal and eliminates some of the information about the weather signal that may be present along with the ground clutter signal. This translates to higher measurement standard deviations for the weather signal.

Another known technique for removing ground clutter signal is a regression filter. It is based on the observation that the ground clutter signal varies very slowly in time whereas weather signals generally vary substantially more. Thus, to remove the slowly varying part of the signal, a regression curve (i.e., a polynomial) is fitted to the signal and then subtracted, thus leaving the weather signal intact. The advantage of the regression filter is that no time domain window is required and thus better weather signal statistics are possible. This has been recently been investigated and shown by Hubbert et al. 2021, Using a Regression Ground Clutter Filter to Improve Weather Radar Signal Statistics: Theory and Simulations, JTECH.

The paper the application of a regression ground clutter to experimental data from NEXRAD and NCAR’s S-Pol radar. Issues such as automated polynomial order selection and interpolation across the zero-velocity gap created by the clutter filtering are addressed. The algorithm is now running in real time on S-Pol.