Abstract
Power spectrum is the considerable aspect in the atmospheric radar data processing to estimate wind parameters. Due to the poor resolution and high sidelobe level problems of the existing algorithms, there is a requisite for the novel data-dependent approaches. A non-parametric and hyperparameter-free iterative adaptive approach (IAA) is presented for the power spectral density estimation. This approach is able to work with single snapshot and is obtained by minimizing the weighted least square fitting criterion. The IAA method provides the accurate amplitude and frequency estimation for the simulated data. The data for the above study is collected from Indian MST (mesosphere, stratosphere, and troposphere) radar. The power spectrum and Doppler frequency are estimated using IAA. In this paper, zonal (U), meridional (V), windspeed (W) are also calculated and validated using Global Positioning System Sonde data. The effectiveness of the spectral estimation performance showed by IAA is demonstrated and assessed.
Highlights
Indian MST radar provides information on wind data above 3.5 km with a resolution of 150 m
The three wind components U, V and W are determined by the Doppler beam swinging (DBS) method of the MST radar
The iterative adaptive approach (IAA) (Yardibi et al 2010) is put into use to estimate the spectrum of MST radar data collected from National Atmospheric Research Laboratory (NARL)
Summary
Indian MST radar provides information on wind data above 3.5 km with a resolution of 150 m. The three wind components U, V and W are determined by the Doppler beam swinging (DBS) method of the MST radar. Since the signals are highly corrupted with noise at higher altitudes, the ADP is unable to estimate the Doppler frequencies and the wind speed.
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