Wavelet transform-based methods for removal of ground clutter and denoising the radar wind profiler data

Various non-atmospheric signals contaminate radar wind profiler (RWP) data, which produce bias in estimation of moments and wind velocity. Especially, in ultra high frequency (UHF) RWPs, ground clutter severely degrades wind velocity estimation. Furthermore, at higher altitudes, noise dominates the clear air signal. Thus, the important tasks of signal processing in a RWP are (i) to eliminate the clutter signal, (ii) to detect the weak atmospheric signals buried inside the noise and (iii) to improve signal-to-noise ratio. Wavelet analysis is a powerful tool to differentiate the characteristics of the ground clutter and noise from the atmospheric turbulence echo at the time series level. The authors have implemented the signal processing for lower atmospheric wind profiler radar at National Atmospheric Research Laboratory, Gadanki, India, using wavelet transforms. In this study, they present the implementation approach and results. The wavelet-based algorithms use different threshold levels to identify and remove ground clutter and to denoise the data. The obtained results using this method are validated with collocated global positioning system radiosonde data.