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.

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

2009 ◽  
Vol 26 (7) ◽  
pp. 1181-1197 ◽  
Author(s):  
J. C. Hubbert ◽  
M. Dixon ◽  
S. M. Ellis
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Weather Radar ◽  
Radar Data ◽  
Weather Data ◽  
Fuzzy Logic Algorithm ◽  
Zero Velocity ◽  
Ground Clutter ◽  
Phase Alignment ◽  
Real Time Identification

Abstract The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol).

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

2015 ◽  
Vol 8 (5) ◽  
pp. 5025-5063
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 one minute, allowing its use on future field campaigns.

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.

scholarly journals Using WSR-88D Polarimetric Data to Identify Bird-Contaminated Doppler Velocities

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Jiang ◽  
Qin Xu ◽  
Pengfei Zhang ◽  
Kang Nai ◽  
Liping Liu
Keyword(s):  
Fuzzy Logic ◽  
Real Data ◽  
Radar Data ◽  
Second Step ◽  
Doppler Velocity ◽  
Data Quality Control ◽  
Fuzzy Logic Method ◽  
Automated Technique ◽  
Migrating Birds ◽  
Logic Method

As an important part of Doppler velocity data quality control for radar data assimilation and other quantitative applications, an automated technique is developed to identify and remove contaminated velocities by birds, especially migrating birds. This technique builds upon the existing hydrometeor classification algorithm (HCA) for dual-polarimetric WSR-88D radars developed at the National Severe Storms Laboratory, and it performs two steps. In the first step, the fuzzy-logic method in the HCA is simplified and used to identify biological echoes (mainly from birds and insects). In the second step, another simple fuzzy logic method is developed to detect bird echoes among the biological echoes identified in the first step and thus remove bird-contaminated velocities. The membership functions used by the fuzzy logic method in the second step are extracted from normalized histograms of differential reflectivity and differential phase for birds and insects, respectively, while the normalized histograms are constructed by polarimetric data collected during the 2012 fall migrating season and sorted for bird and insects, respectively. The performance and effectiveness of the technique are demonstrated by real-data examples.

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

2020 ◽  
Vol 13 (2) ◽  
pp. 537-551
Author(s):  
Shuai Zhang ◽  
Xingyou Huang ◽  
Jinzhong Min ◽  
Zhigang Chu ◽  
Xiaoran Zhuang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Weak Signal ◽  
Post Processing ◽  
Algorithm Performance ◽  
Radar Echoes ◽  
Fuzzy Logic Method ◽  
Anomalous Propagation ◽  
Logic Method

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.

associations between aircraft measurements of turbulence and weather radar measurements1

1965 ◽  
Vol 46 (8) ◽  
pp. 443-447 ◽  
Author(s):  
Edwin Kessler ◽  
Jean T. Lee ◽  
Kenneth E. Wilk
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Time Dependent ◽  
Aircraft Measurements ◽  
Radar Echoes ◽  
Radar Echo

Aircraft have been guided with the aid of radar data to measure turbulence in thunderstorm areas. Although turbulence is frequently encountered in areas containing highly reflective and sharp-edged echoes, no unique correspondence has been discovered between single-echo parameters and collocated within-storm turbulence. A theory embracing some of the time-dependent relationships between fields of wind and precipitation suggests that the correspondence between instantaneous distributions of radar echoes and turbulence is statistical rather than precise. Statistical bases for study of radar echo-turbulence relationships are outlined.

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

2019 ◽  
Author(s):  
Shuai Zhang ◽  
Xingyou Huang ◽  
Jinzhong Min ◽  
Hengheng Zhang
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Weak Signal ◽  
Post Processing ◽  
Algorithm Performance ◽  
Radar Echoes ◽  
Fuzzy Logic Method ◽  
Anomalous Propagation ◽  
Logic Method

Abstract. In order to obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After the comprehensive analysis of the computational efficiency and radar system characteristics, a fuzzy logic method similar to the MetSignal algorithm is adopted, but its performance 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 far range to be misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is introduced 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 that the performance of the algorithm has been significantly improved, especially with respect to the classification of meteorological echoes in weak signal regions.

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