An automatic identification of clutter and anomalous propagation in polarization-diversity weather radar data using neural networks

Silveira, Reinaldo; Holt, A.R.

doi:10.1109/36.942556

Cited by 28 publications

(13 citation statements)

References 15 publications

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“…This is the case for the algorithm, proposed by Grecu and Krajewski (2000), whose performance was studied over a large dataset by Krajewski and Vignal (2001). Similarly, da Silveira and Holt (2001) proposed an alternative neural network algorithm in which they only used two features obtained from measurements taken by a polarization diversity radar.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans

Berenguer

Sempere‐Torres

Corral

et al. 2006

Journal of Atmospheric and Oceanic Technology

118

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Because echoes caused by nonmeteorological targets significantly affect radar scans, contaminated bins must be identified and eliminated before precipitation can be quantitatively estimated from radar measurements.Under mean propagation conditions, clutter echoes (mainly caused by targets such as mountains or large buildings) can be found in almost fixed locations. However, in anomalous propagation conditions, new clutter echoes may appear (sometimes over the sea), and they may be difficult to distinguish from precipitation returns. Therefore, an automatic algorithm is needed to identify clutter on radar scans, especially for operational uses of radar information (such as real-time hydrology).In this study, a new algorithm is presented based on fuzzy logic, using volumetric data. It uses some statistics to highlight clutter characteristics (namely, shallow vertical extent, high spatial variability, and low radial velocities) to output a value that quantifies the possibility of each bin being affected by clutter (in order to remove those in which this factor exceeds a certain threshold).The performance of this algorithm was compared against that of simply removing mean clutter echoes. Satisfactory results were obtained from an exhaustive evaluation of this algorithm, especially in those cases in which anomalous propagation played an important role.

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Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans

Berenguer

Sempere‐Torres

Corral

et al. 2006

Journal of Atmospheric and Oceanic Technology

118

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“…We applied a confusion matrix to verify the accuracy, as shown in Equation (8). In this equation, TP stands for true positive, TN for true negative, FP for false positive and FN for false negative.…”

Section: Resultsmentioning

confidence: 99%

“…For many years, researchers have studied the detection of anomalous propagation echoes from radar data. The techniques can be classified as follows: fuzzy logic techniques [5][6][7]; artificial neural networks [8][9][10]; Bayesian classifiers [11]; case studies [12]; statistical approaches [13]; etc.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Classification for Anomalous Propagation Echo Detection with Clustering-Based Subset-Selection Method

Lee

Kim

2017

Atmosphere

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Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated atmospheric conditions. Anomalous propagation echoes should be removed because they make weather forecasting difficult. In this paper, we suggest an ensemble classification method based on an artificial neural network and a clustering-based subset-selection method. This method allows us to implement an efficient classification method when a feature space has complicated distributions. By separating the input data into atomic and non-atomic clusters, each derived cluster will receive its own base classifier. In the experiments, we compared our method with a standalone artificial neural network classifier. The suggested ensemble classifier showed 84.14% performance, which was about 2% higher than that of the k-means clustering-based ensemble classifier and about 4% higher than the standalone artificial neural network classifier.

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“…The Sierra de Botucatu is about 100 km southeast from the radar. The 3.5 km CAPPI was found to be the most suitable, covering elevations that are sufficiently high to avoid ground clutter (see Silveira and Holt 2001, for definition and examples) as well as beam blockages, but below the melting layer, at a range of 120 km.…”

Section: Introductionmentioning

confidence: 99%