A Fuzzy Logic Algorithm for the Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar Observations

Gourley, Jonathan J.; Tabary, Pierre; Châtelet, Jacques Parent Du

doi:10.1175/jtech2035.1

Cited by 147 publications

(137 citation statements)

References 21 publications

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“…A large weight can be assigned to the feature parameter with relatively distinctive characteristics (less overlapping area) between matched and mismatched cell tracks. In this study, the weights for the three MFs have been objectively determined by an overlapping area between the NFDs of matched and mismatched cell tracks with Equation (8) (Cho et al, 2006;Gourley et al, 2007;Kuk et al, 2012):…”

Section: Membership Functions and Weightsmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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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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Section: Membership Functions and Weightsmentioning

confidence: 99%

mentioning

confidence: 99%

Radar‐based cell tracking with fuzzy logic approach

Jung

Lee

2015

Meteorological Applications

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“…• Pre-classification of each polar pixel as clear air (CA), ground clutter (GC) or precipitation (PR) according to the results of a fuzzy-logic algorithm using the Z dr texture, σ z and ρ hv as described in Gourley et al (2007a).…”

Section: Polarimetric Variables Processingmentioning

confidence: 99%

“…Since 2004, continuous effort has been made to address the issues posed by the operational exploitation of polarimetry. Work has been done to design and implement robust and efficient procedures to monitor the data quality , remove artefacts (Gourley et al, 2007a), correct for precipitationinduced attenuation (Gourley et al, 2007b), identify the bright band , classify the hydrometeors (Vulpiani et al, 2008), and assess the impact of groundclutter contamination on the measurements (Friedrich et al, 2009). This has led to the development of a preliminary operational polarimetric pre-processing chain which was introduced in July 2010 in shadow mode for evaluation alongside the legacy processing chain.…”

Section: Introductionmentioning

confidence: 99%

Long‐term monitoring of French polarimetric radar data quality and evaluation of several polarimetric quantitative precipitation estimators in ideal conditions for operational implementation at C‐band

Ventura

Boumahmoud

Fradon

et al. 2012

Quart J Royal Meteoro Soc

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This article presents the evaluation of several polarimetric quantitative precipitation estimation (Pol-QPE) algorithm candidates for operational implementation in the Météo-France polarimetric weather radars (nine C-band and one S-band out of a total of 24 radars in 2011). Since the performance of the algorithms depends on both the quality of the polarimetric variables and the applied polarimetric processing, the article first describes the current polarimetric processing chain and then provides a comprehensive analysis of the quality of the polarimetric variables.Three types of QPE algorithms have been studied at C-band in ideal conditions (short ranges, no partial beam blocking, no vertical profile of reflectivity effects): (i) algorithms based on simple Z-R relationships with and without attenuation correction using the differential phase dp , (ii) an algorithm based on reflectivity (Z h ) and differential reflectivity (Z dr ), and (iii) algorithms based on specific differential phase (K dp ).Evaluation against rain-gauges, performed hourly, confirms the superiority of polarimetric algorithms as repeatedly reported in the literature. It also shows that the benefits brought by algorithms based on power variables, Z h and Z dr , are critically dependent upon the capability to correctly calibrate them. Long-term monitoring shows that, with the current radar systems and operational calibration procedures, a stability of ±0.4 dB is achieved on Z dr . In that context, QPE algorithms based on K dp , which are considered to be immune to partial beam blocking, calibration, attenuation, etc., are preferred for operational use until improvements are made to the calibration of Z dr . To this end, a hybrid algorithm based on attenuationcorrected Z h for low rain rates and K dp for higher rain rates has been designed and tested Copyright c 2012 Royal Meteorological Society Key Words: polarimetry; polarimetric processing; radar calibration; radar-raingauge comparisons

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“…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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A Fuzzy Logic Algorithm for the Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar Observations

Cited by 147 publications

References 21 publications

Radar‐based cell tracking with fuzzy logic approach

Radar‐based cell tracking with fuzzy logic approach

Long‐term monitoring of French polarimetric radar data quality and evaluation of several polarimetric quantitative precipitation estimators in ideal conditions for operational implementation at C‐band

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

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