Abstract
Abstract. In this paper, we apply wavelet thresholding for removing automatically ground and intermittent clutter (airplane echoes) from wind profiler radar data. Using the concept of discrete multi-resolution analysis and non-parametric estimation theory, we develop wavelet domain thresholding rules, which allow us to identify the coefficients relevant for clutter and to suppress them in order to obtain filtered reconstructions.Key words. Meteorology and atmospheric dynamics (instruments and techniques) – Radio science (remote sensing; signal processing)
Highlights
Radar Wind Profilers (RWP) are versatile tools used to routinely probe the Earth’s atmosphere. This technology originally developed for studying the dynamics of the middle atmosphere in the seventies (Hardy and Gage, 1990) is, very prominent in the meteorological research community
Meteorological services started using these systems operationally within the Global Observing System (GOS) (see Monna and Chadwick (1998)). Most of these RWP employ the Doppler-beam swinging (DBS) method for the determination of the vertical profile of the horizontal wind and, under certain conditions, the vertical wind component. These radars transmit short electromagnetic pulses in a fixed beam direction and sample the small fraction of the electromagnetic field backscattered to the antenna
We propose a modified signal processing technique for RWPs
Summary
Radar Wind Profilers (RWP) are versatile tools used to routinely probe the Earth’s atmosphere. Signal processing ends with the estimation of the moments of the Doppler spectrum and further data processing is performed to determine the wind and other meteorological parameters using measurements from all radar beams. This distinction, which goes back originally to Keeler and Passarelli (1990), has become more and more blurred, since some modern algorithms make use of the moments of the Doppler spectrum with the help of continuity and other information (Wilfong et al, 1999b). We will refer here to the usually applied and well established “classical” signal processing, as described by Tsuda (1989), Rottger and Larsen (1990), among others
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