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Ann. Geophys., 19, 815-824, 2001
www.ann-geophys.net/19/815/2001/
© European Geosciences Union 2001
Application of model-based spectral analysis to wind-profiler radar observations
E. Boyer1, M. Petitdidier2, W. Corneil2, C. Adnet3, and P. Larzabal1,4
1LESiR/ENS Cachan, UPRESA 8029, 61 avenue du président Wilson, 94235 Cachan cedex, France
2CETP, 10-12 Avenue de l’Europe, 78140 Vprélizy, France
3THALES Air Dfense, 7-9 rue des Mathurins, Bagneux France
4IUT de Cachan, CRIIP, Universitpré Paris Sud, 9 avenue de la division Leclerc, 94 234 Cachan cedex, France
Abstract. A classical way to reduce a radar’s data
is to compute the spectrum using FFT and then to identify the different peak
contributions. But in case an overlapping between the different echoes
(atmospheric echo, clutter, hydrometeor echo. . . ) exists, Fourier-like
techniques provide poor frequency resolution and then sophisticated
peak-identification may not be able to detect the different echoes. In order to
improve the number of reduced data and their quality relative to Fourier
spectrum analysis, three different methods are presented in this paper and
applied to actual data. Their approach consists of predicting the main
frequency-components, which avoids the development of very sophisticated
peak-identification algorithms. The first method is based on cepstrum
properties generally used to determine the shift between two close identical
echoes. We will see in this paper that this method cannot provide a better
estimate than Fourier-like techniques in an operational use. The second method
consists of an autoregressive estimation of the spectrum. Since the tests were
promising, this method was applied to reduce the radar data obtained during two
thunder-storms. The autoregressive method, which is very simple to implement,
improved the Doppler-frequency data reduction relative to the FFT spectrum
analysis. The third method exploits a MUSIC algorithm, one of the numerous
subspace-based methods, which is well adapted to estimate spectra composed of
pure lines. A statistical study of performances of this method is presented,
and points out the very good resolution of this estimator in comparison with
Fourier-like techniques. Application to actual data confirms the good qualities
of this estimator for reducing radar’s data.
Key words. Meteorology and atmospheric dynamics
(tropical meteorology)- Radio science (signal processing)- General (techniques
applicable in three or more fields)
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