Open AccessWavelet transform‐based methods for removal of ground clutter and denoising the radar wind profiler data
Author(s) -
Allabakash Shaik,
Yasodha Polisetti,
Venkatramana Reddy Sakirevupalli,
Srinivasulu Parvatala
Publication year - 2015
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2014.0312
Subject(s) - wind profiler , clutter , radar , remote sensing , wavelet , radiosonde , computer science , environmental science , noise (video) , radar horizon , wavelet transform , signal (programming language) , pulse doppler radar , meteorology , geology , artificial intelligence , radar imaging , telecommunications , geography , image (mathematics) , programming language
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.