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30 April 2003 DFT-based spectral moments estimators for spaceborne Doppler precipitation radars
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In this paper an in-depth analysis on the performance of the Fourier analysis in estimating the first moment of Doppler spectra of rain signals from a spaceborne radar is presented. Spectral moments estimators based on Fourier analysis (DFT-SME) have been widely used by Doppler weather radars in measuring rainfall velocity and they have been found to be almost optimal for narrow normalized spectral widths (wN). They are also more computationally efficient than the Maximum Likelihood estimators. However, the existing analytical approaches for evaluating the DFT-SME performance have mostly been focused on a limited range of small wN (e.g., wN< 0.1) that are typical of ground based and airborne Doppler weather radars. With the rapid advances in spaceborne radar technologies, the flying of a Doppler precipitation radar in space to acquire global data sets of vertical rainfall velocity has become a real possibility. The objective of this work is to develop a generalized analytical approach by extending it to cover larger values of wN (e.g., wN ~ 0.2) in spaceborne radar applications. In particular, a method has been developed to properly treat the aliasing effects, which have become a significant error source in spaceborne applications. Several DFT-SME algorithms (differing in the adopted strategy for noise handling and the initial estimate of the mean Doppler velocity) have been analyzed with this generalized approach. The analytical results are in excellent agreement with those obtained through simulation. Such encouraging results suggest that the proposed approach is a reliable technique for fast and accurate prediction of DFT-SME performance for spaceborne Doppler weather radars.
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Simone Tanelli, Eastwood Im, Luca Facheris, and Eric A. Smith "DFT-based spectral moments estimators for spaceborne Doppler precipitation radars", Proc. SPIE 4894, Microwave Remote Sensing of the Atmosphere and Environment III, (30 April 2003);


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