Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS

2009 ◽  
Vol 24 (3) ◽  
pp. 730-748 ◽  
Author(s):  
Hyang Suk Park ◽  
A. V. Ryzhkov ◽  
D. S. Zrnić ◽  
Kyung-Eak Kim
Keyword(s):  
Fuzzy Logic ◽  
Classification Scheme ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Classification Algorithm ◽  
Convective System ◽  
Polarimetric Radar ◽  
The Matrix ◽  
Mesoscale Convective ◽  
Radar Measurements

Abstract This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric variables and discerns 10 different classes of radar echoes. Furthermore, a methodology for the new fuzzy logic classification scheme is discussed and the results are illustrated using polarimetric radar data collected with the Norman, Oklahoma (KOUN), WSR-88D prototype radar during a mesoscale convective system event on 13 May 2005.

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

2007 ◽  
Vol 24 (8) ◽  
pp. 1439-1451 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Pierre Tabary ◽  
Jacques Parent du Chatelet
Keyword(s):  
Fuzzy Logic ◽  
Case Studies ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Fuzzy Logic Algorithm

Abstract A fuzzy logic algorithm has been developed for the purpose of segregating precipitating from nonprecipitating echoes using polarimetric radar observations at C band. Adequate polarimetric descriptions for each type of scatterer are required for the algorithm to be effective. An observations-based approach is presented in this study to derive membership functions and objectively weight them so that they apply directly to conditions experienced at the radar site and to the radar wavelength. Three case studies are examined and show that the algorithm successfully removes nonprecipitating echoes from rainfall accumulation maps.

scholarly journals Cloud Ice Crystal Classification Using a 95-GHz Polarimetric Radar

2004 ◽  
Vol 21 (11) ◽  
pp. 1679-1688 ◽  
Author(s):  
K. Aydin ◽  
J. Singh
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Classification Algorithm ◽  
Ice Crystals ◽  
Polarimetric Radar ◽  
Ice Crystal ◽  
Radar Measurements ◽  
Particle Images ◽  
Cloud Ice ◽  
Range Of Values

Abstract Two algorithms are presented for ice crystal classification using 95-GHz polarimetric radar observables and air temperature (T). Both are based on a fuzzy logic scheme. Ice crystals are classified as columnar crystals (CC), planar crystals (PC), mixtures of PC and small- to medium-sized aggregates and/or lightly to moderately rimed PC (PSAR), medium- to large-sized aggregates of PC, or densely rimed PC, or graupel-like snow or small lumpy graupel (PLARG), and graupel larger than about 2 mm (G). The 1D algorithm makes use of Zh, Zdr, LDRhv, and T, while the 2D algorithm incorporates the three radar observables in pairs, (Zdr, Zh), (LDRhv, Zh), and (Zdr, LDRhv), plus the temperature T. The range of values for each observable or pair of observables is derived from extensive modeling studies conducted earlier. The algorithms are tested using side-looking radar measurements from an aircraft, which was also equipped with particle probes producing simultaneous and nearly collocated shadow images of cloud ice crystals. The classification results from both algorithms agreed very well with the particle images. The two algorithms were in agreement by 89% in one case and 97% in the remaining three cases considered here. The most effective observable in the 1D algorithm was Zdr, and in the 2D algorithm the pair (Zdr, Zh). LDRhv had negligible effect in the 1D classification algorithm for the cases considered here. The temperature T was mainly effective in separating columnar crystals from the rest. The advantage of the 2D algorithm over the 1D algorithm was that it significantly reduced the dependence on T in two out of the four cases.

scholarly journals A Cluster-Based Method for Hydrometeor Classification Using Polarimetric Variables. Part I: Interpretation and Analysis

2015 ◽  
Vol 32 (7) ◽  
pp. 1320-1340 ◽  
Author(s):  
Guang Wen ◽  
Alain Protat ◽  
Peter T. May ◽  
Xuezhi Wang ◽  
William Moran
Keyword(s):  
Electromagnetic Scattering ◽  
Expectation Maximization ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Radar Data ◽  
Probability Density Functions ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Membership Functions ◽  
Polarimetric Radar

AbstractHydrometeor classification methods using polarimetric radar variables rely on probability density functions (PDFs) or membership functions derived empirically or by using electromagnetic scattering calculations. This paper describes an objective approach based on cluster analysis to deriving the PDFs. An iterative procedure with K-means clustering and expectation–maximization clustering based on Gaussian mixture models is developed to generate a series of prototypes for each hydrometeor type from several radar scans. The prototypes are then grouped together to produce a PDF for each hydrometeor type, which is modeled as a Gaussian mixture. The cluster-based method is applied to polarimetric radar data collected with the CP-2 S-band radar near Brisbane, Queensland, Australia. The results are illustrated and compared with theoretical classification boundaries in the literature. Some notable differences are found. Automated hydrometeor classification algorithms can be built using the PDFs of polarimetric variables associated with each hydrometeor type presented in this paper.

scholarly journals Reduction of Nonuniform Beamfilling Effects by Multiple Constraints: A Simulation Study

2015 ◽  
Vol 32 (11) ◽  
pp. 2114-2124 ◽  
Author(s):  
David A. Short ◽  
Robert Meneghini ◽  
Amber E. Emory ◽  
Mathew R. Schwaller
Keyword(s):  
Attenuation Correction ◽  
Vertical Structure ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Radar Signal ◽  
Precipitation Radar ◽  
Sensor Field ◽  
Spaceborne Radar ◽  
Ku Band ◽  
Reflectivity Factor

AbstractA spaceborne precipitation radar samples the vertical structure of precipitating hydrometeors from the top down. The viewing geometry and operating frequency result in certain limitations and opportunities. Among the limitations is attenuation of the radar signal that can cause the measured radar reflectivity factor to be substantially less than the desired quantity, the true radar reflectivity factor. Another error source is the spatial variability in precipitation rates that occurs at scales smaller than the sensor field of view (FOV), giving rise to the nonuniform beamfilling (NUBF) effect. The opportunities arise when the radar return from the surface can be used to obtain constraints on the path-integrated attenuation (PIA) for use in hybrid attenuation correction algorithms. The surface return can also provide some information on the degree of NUBF at off-nadir viewing angles. In this paper ground-based radar data are used to simulate spaceborne radar data at nadir and off-nadir viewing angles at Ku band and Ka band and to test attenuation correction algorithms in the presence of nonuniform beamfilling. The cross-FOV gradient in PIA is found to be an important characteristic for describing the performance of attenuation correction algorithms.

Storm Labeling in Three Dimensions (SL3D): A Volumetric Radar Echo and Dual-Polarization Updraft Classification Algorithm

2017 ◽  
Vol 145 (3) ◽  
pp. 1127-1145 ◽  
Author(s):  
Mariusz Starzec ◽  
Cameron R. Homeyer ◽  
Gretchen L. Mullendore
Keyword(s):  
Three Dimensional ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Three Dimensions ◽  
Classification Algorithms ◽  
Two Dimensional ◽  
Horizontal Polarization ◽  
Dimensional Classification ◽  
Traditional Algorithm ◽  
Radar Echo

This study presents a new storm classification method for objectively stratifying three-dimensional radar echo into five categories: convection, convective updraft, precipitating stratiform, nonprecipitating stratiform, and ice-only anvil. The Storm Labeling in Three Dimensions (SL3D) algorithm utilizes volumetric radar data to classify radar echo based on storm height, depth, and intensity in order to provide a new method for updraft classification and improve upon the limitations of traditional storm classification algorithms. Convective updrafts are identified by searching for three known polarimetric radar signatures: weak-echo regions (bounded and unbounded) in the radar reflectivity factor at horizontal polarization ([Formula: see text]), differential radar reflectivity ([Formula: see text]) columns, and specific differential phase ([Formula: see text]) columns. Additionally, leveraging the three-dimensional information allows SL3D to improve upon missed identifications of weak convection and intense stratiform rain in traditional two-dimensional classification schemes. This study presents the results of applying the SL3D algorithm to several cases of high-resolution three-dimensional composites of NEXRAD WSR-88D data in the contiguous United States. Comparisons with a traditional algorithm that uses two-dimensional maps of [Formula: see text] are also shown to illustrate the differences of the SL3D algorithm.

scholarly journals Fuzzy Logic Algorithms to Identify Birds, Precipitation, and Ground Clutter in S-Band Radar Data Using Polarimetric and Nonpolarimetric Variables

2019 ◽  
Vol 36 (12) ◽  
pp. 2401-2414 ◽  
Author(s):  
Basivi Radhakrishna ◽  
Frédéric Fabry ◽  
Alamelu Kilambi
Keyword(s):  
Fuzzy Logic ◽  
Target Identification ◽  
Radar Data ◽  
Radar Observations ◽  
Fuzzy Logic Algorithm ◽  
Ground Clutter ◽  
Independent Events ◽  
Radar Echoes ◽  
Radar Echo ◽  
Migrating Birds

AbstractThe statistical properties of the radar echoes from biological, precipitation, and ground targets observed with the McGill S-band dual-polarization radar have been used to devise a polarimetric and a nonpolarimetric fuzzy logic algorithm for pixel-by-pixel target identification. Radar observations of migrating birds show distinctly different polarimetric features during their relative approach and departure from the radar site illustrating the dependency of radar parameters on the canting angle and scattering cross section. The devised algorithms have been tested with two independent events, each consisting of 2 h of radar observations with a 5-min temporal resolution. One event consisted of precipitation without birds while the other contained only birds. The misclassifications were 10.12% and 9.6%, respectively, for the two cases for the nonpolarimetric algorithm, and 1.99% and 0.92% for the polarimetric algorithm. The results indicate that even though nonpolarimetric radar membership functions may be considered adequate for separating radar echo returns from birds, precipitation, and ground targets, they are not sufficiently skilled if a greater accuracy is required. Target identification without polarimetric variables especially fails in the region of zero isodop and in precipitation with an echo top below 4 km.

scholarly journals Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments

2015 ◽  
Vol 8 (10) ◽  
pp. 3985-4000 ◽  
Author(s):  
D. R. L. Dufton ◽  
C. G. Collier
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Convective Precipitation ◽  
Final Decision ◽  
Membership Functions ◽  
Fuzzy Classifier ◽  
Differential Phase Shift ◽  
Ground Clutter ◽  
Radar Echoes ◽  
Quantitative Precipitation Estimates

Abstract. The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25 % of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coefficient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium-duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echo features, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than 1 min, allowing its use on future field campaigns.

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