Ground Clutter Mitigation for Weather Radars Using the Autocorrelation Spectral Density

Torres, Sebastián; Warde, David A.

doi:10.1175/jtech-d-13-00117.1

Cited by 38 publications

(24 citation statements)

References 19 publications

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“…Therefore, real-time ML identification algorithms Zhang et al, 2008) or atmospheric environmental data have been considered as additions to the MetSignal_noise algorithm in the following study to select a better height threshold. Moreover, the advanced clutter suppression algorithm based on signal processing (Hubbert et al, 2009a, b;Torres and Warde, 2014) should be considered for introduction into NUIST-CDP. This is because when the ML appears at a lower altitude (frequently occuring in winter precipitation events), it will be fully mixed with the ground clutter.…”

Section: Discussionmentioning

confidence: 99%

Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

et al. 2020

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Abstract. To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions.

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Section: Discussionmentioning

confidence: 99%

Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

et al. 2020

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“…1(b)]. Spectral analysis of this long time series can be made to remove ground clutter [9] or separate signals overlaid in range [6], [10]. Usually, in such applications, a suitable window function is used, and it determines the spectral resolution and the level of sidelobes.…”

Section: A Combining Time Series From Adjacent Radialsmentioning

confidence: 99%

“…The method for clutter removal is a version of the Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) procedure implemented from description in the literature [9]. The CLEAN-AP procedure has been accepted by the National Weather Service to be part of some scans on the WSR-88D in about 2017.…”

Section: B Removal Of Ground Cluttermentioning

confidence: 99%

Spectral Processing for Step Scanning Phased-Array Radars

Borowska

Zhang

Zrnić

2016

IEEE Trans. Geosci. Remote Sensing

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On phased-array radars, scanning is done by stepping the beam from one direction to the next direction and dwelling long enough at each direction to achieve acceptable errors of estimates. Combining data from the three directions is suggested to obtain superresolution similar to that available on the national network of weather radar (Weather Surveillance Radar-1988 Doppler or WSR-88D). Spectral analysis of such data is addressed, and it is demonstrated that the Doppler spectra of simply concatenated time series have very strong sidebands due to the discontinuity of the signals from the three beam positions. This artifact degrades the performance of the spectral clutter filters and other methods that rely on spectral processing to enhance the weather signal. Special adjustments of the signals at each range location before concatenating (splicing) are proposed to mitigate the effects of discontinuities in time and thus improve clutter filtering. The adjustment is such that the total information contained in the signal can be preserved in subsequent processing. Spectral quality of the concatenated signals is quantified via results from simulations. Samples of spectra obtained with the National Weather Radar Testbed are presented to substantiate the predictions. A ground clutter detector/filter accepted by the National Weather Service is applied to the conditioned time series data, and the ensuing fields of reflectivity factor and Doppler velocity are compared to the fields from which clutter had not been removed.

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“…Weather echoes with small radial velocity are particularly sensitive to this method. To deal with this issue, adaptive strategies that reconstruct the removed spectral components have been proposed [10–12 ]. The Gaussian model adaptive processing algorithm (GMAP) [10 ] works in the spectral domain, processing the estimated signal PSD.…”

Section: Introductionmentioning

confidence: 99%