Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar

Rico‐Ramirez, Miguel A.; Cluckie, ID

doi:10.1109/tgrs.2008.916979

Cited by 107 publications

(64 citation statements)

References 30 publications

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“…These fields usually exhibit quite different probability distribution functions (PDFs) for echoes from precipitation, clutter, or anaprop. Parameters derived from the reflectivity gradient field have been used within differing probabilistic classification algorithms including fuzzy logic (Gourley et al 2007;Hubbert et al 2009;Kessinger et al 2004), neural networks (Grecu and Krajewski 2000;Krajewski and Vignal 2001;Lakshmanan et al 2007;Luke et al 2008), and Bayesian (Moszkowicz et al 1994;Rico-Ramirez and Cluckie 2008). Some of these have been developed using polarimetric variables; however, an advantage of each of these methods is that they can be applied to radar systems utilizing only reflectivity measurements at a single wavelength and polarization.…”

Section: B Postdata Acquisition Processingmentioning

confidence: 99%

“…The NBC is a supervised learning classification algorithm, which requires training datasets where it is known a priori if the returns originate from precipitation or anaprop (RicoRamirez and Cluckie 2008). The algorithm developed is similar to that presented by Rico-Ramirez and Cluckie (2008); however, we demonstrate and quantify its efficacy with the use of single-polarization data using only corrected reflectivity. Furthermore, in section 4, the NBC is applied to two cases of convective storms embedded in anaprop signals: in the first, it is shown that the NBC is skillful at distinguishing precipitation from anaprop, while in the second example we demonstrate that the NBC is effective when applied to data from two nearby radars with differing wavelengths and beamwidths from the radar on which it was trained.…”

Section: B Postdata Acquisition Processingmentioning

confidence: 99%

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Application of a Bayesian Classifier of Anomalous Propagation to Single-Polarization Radar Reflectivity Data

Peter

Seed

Steinle

2013

Journal of Atmospheric and Oceanic Technology

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A naïve Bayes classifier (NBC) was developed to distinguish precipitation echoes from anomalous propagation (anaprop). The NBC is an application of Bayes's theorem, which makes its classification decision based on the class with the maximum a posteriori probability. Several feature fields were input to the Bayes classifier: texture of reflectivity (TDBZ), a measure of the reflectivity fluctuations (SPIN), and vertical profile of reflectivity (VPDBZ). Prior conditional probability distribution functions (PDFs) of the feature fields were constructed from training sets for several meteorological scenarios and for anaprop. A Box-Cox transform was applied to transform these PDFs to approximate Gaussian distributions, which enabled efficient numerical computation as they could be specified completely by their mean and standard deviation. Combinations of the feature fields were tested on the training datasets to evaluate the best combination for discriminating anaprop and precipitation, which was found to be TDBZ and VPDBZ. The NBC was applied to a case of convective rain embedded in anaprop and found to be effective at distinguishing the echoes. Furthermore, despite having been trained with data from a single radar, the NBC was successful at distinguishing precipitation and anaprop from two nearby radars with differing wavelength and beamwidth characteristics. The NBC was extended to implement a strength of classification index that provides a metric to quantify the confidence with which data have been classified as precipitation and, consequently, a method to censor data for assimilation or quantitative precipitation estimation.

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Section: B Postdata Acquisition Processingmentioning

confidence: 99%

Section: B Postdata Acquisition Processingmentioning

confidence: 99%

Application of a Bayesian Classifier of Anomalous Propagation to Single-Polarization Radar Reflectivity Data

Peter

Seed

Steinle

2013

Journal of Atmospheric and Oceanic Technology

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“…The clutter classifier is also able to identify AP echoes (See Figures 9 and 10 in Rico-Ramirez and Cluckie, 2008). For events with AP, the ground clutter model will produce reflectivity values equal to zero at the locations of the AP echoes under standard propagation conditions, whereas the clutter classifier will identify all clutter pixels including those due to AP.…”

Section: Monitoring Of Antenna Pointingmentioning

confidence: 99%

“…The combination of the input measurements obtained with single-and dual-polarization weather radars have enabled automatic classification of ground clutter echoes as well as anomalous propagation (AP) echoes, which are originated when the radar beam hits the ground/sea surface due to changes in the atmospheric index of refraction. Examples of these classifiers are described in Grecu and Krajewski (1999); Schuur et al (2003); Berenguer et al (2006); Cho et al (2006); Gourley et al (2007); Rico-Ramirez and Cluckie (2008). Ground clutter echoes have been utilized in different applications such as to calibrate weather radars at attenuating wavelengths (Serrar et al, 2000) and to control radar signal stability (Sempere-Torres et al, 2001.…”

Section: Introductionmentioning

confidence: 99%

Real‐time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier

Rico‐Ramirez

Gonzalez-Ramirez

Cluckie

et al. 2008

Meteorological Applications

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This paper presents a novel technique to monitor continuously the azimuthal pointing accuracy of a weather radar antenna. The technique consists of cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitation conditions. The azimuthal angle lag with the maximum cross-correlation indicates the adjustment needed in antenna pointing. The modelled ground clutter echoes were obtained using high-resolution digital elevation model (DEM) data whereas the measured ground clutter echoes can be obtained in real-time using a Bayes classifier, which identifies the clutter echoes in the presence of precipitation. The technique has been successfully tested in the Thurnham radar in Southeast England. This method can be used by data users as well as radar operators. It should complement the traditional methods based on sun measurements.

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“…Fuzzy logic has been widely applied to quality control algorithms for radar data to remove ground clutter and anomalous propagation (AP) (e.g. Kessenger et al, 1999;Cho et al, 2006;Gourley et al, 2007;Rico-Ramirez and Cluckie, 2008), for hydrometeor classification using polarimetric measurements (e.g. Straka and Zrnic, 1993;Vivekanandan et al, 1999;Keenan, 2003;Lim et al, 2005;Park et al, 2009), for correction of bright bands (e.g.…”

mentioning

confidence: 99%

Radar‐based cell tracking with fuzzy logic approach

Jung

Lee

2015

Meteorological Applications

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This study presents a new algorithm Fuzzy logic Algorithm for Storm Tracking (FAST) for tracking convective cells in 3D Cartesian co-ordinates. A fuzzy logic approach was applied to track cells with objectively determined membership functions (MFs) and weights that were derived from a large set of convective cell tracks (2326 pairs of convective cells). Three feature parameters (cell motion speed, area change ratio and axis transformation ratio) were selected by Bayesian information criterion and combined through fuzzy logic. FAST was evaluated with three skill scores using 2561 (2456) pairs of convective cells with (without) mergers and splits. The performance was tested in comparison with Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) as a baseline method. For reference tracks without merger and split, the critical success index (CSI) of FAST reaches about 0.89. FAST shows better skill scores (CSI = 0.92 and 0.89 at speed limitations of 16.1 m s −1 and 25.0 m s −1 , respectively) than those of TITAN (CSI = 0.89 and 0.82 at the same speed limitations) and is independent of the number of convective cells and the speed limitation. When mergers and splits are considered in the reference, the tracking performance of FAST (FAST MS ) with a merger and split process is always better than that of TITAN (TITAN MS ) with a merger and split process and is independent of speed limitations. When FAST (=FAST noMS ) does not include a merger and split process, its tracking performance is better than that of TITAN MS at speed limitation of > 20 m s −1 .

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Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar

Cited by 107 publications

References 30 publications

Application of a Bayesian Classifier of Anomalous Propagation to Single-Polarization Radar Reflectivity Data

Application of a Bayesian Classifier of Anomalous Propagation to Single-Polarization Radar Reflectivity Data

Real‐time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier

Radar‐based cell tracking with fuzzy logic approach

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