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.

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.

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.

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 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.

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.

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 Characterization of a Mediterranean flash flood event using rain gauges, radar, GIS and lightning data

2008 ◽  
Vol 17 ◽  
pp. 35-41 ◽  
Author(s):  
M. Barnolas ◽  
A. Atencia ◽  
M. C. Llasat ◽  
T. Rigo
Keyword(s):  
Flash Flood ◽  
Radar Data ◽  
Convective Precipitation ◽  
Flood Event ◽  
Flood Events ◽  
Rain Gauges ◽  
Radar Echoes ◽  
Spatial Coverage ◽  
Ephemeral Rivers ◽  
Accumulated Rainfall

Abstract. Flash flood events are very common in Catalonia, generating a high impact on society, including losses in life almost every year. They are produced by the overflowing of ephemeral rivers in narrow and steep basins close to the sea. This kind of floods is associated with convective events producing high rainfall intensities. The aim of the present study is to analyse the 12–14 September 2006 flash flood event within the framework of the characteristics of flood events in the Internal Basins of Catalonia (IBC). To achieve this purpose all flood events occurred between 1996 and 2005 have been analysed. Rainfall and radar data have been introduced into a GIS, and a classification of the events has been done. A distinction of episodes has been made considering the spatial coverage of accumulated rainfall in 24 h, and the degree of the convective precipitation registered. The study case can be considered as a highly convective one, with rainfalls covering all the IBC on the 13th of September. In that day 215.9 mm/24 h were recorded with maximum intensities above 130 mm/h. A complete meteorological study of this event is also presented. In addition, as this is an episode with a high lightning activity it has been chosen to be studied into the framework of the FLASH project. In this way, a comparison between this information and raingauge data has been developed. All with the goal in mind of finding a relation between lightning density, radar echoes and amounts of precipitation. Furthermore, these studies improve our knowledge about thunderstorms systems.

scholarly journals Identification and Removal of Ground Echoes and Anomalous Propagation Using the Characteristics of Radar Echoes

2006 ◽  
Vol 23 (9) ◽  
pp. 1206-1222 ◽  
Author(s):  
Yo-Han Cho ◽  
Gyu Won Lee ◽  
Kyung-Eak Kim ◽  
Isztar Zawadzki
Keyword(s):  
Fuzzy Logic ◽  
Membership Functions ◽  
Echo Intensity ◽  
Radar Echoes ◽  
Lower Elevation ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Overall Performance ◽  
Anomalous Propagation ◽  
Ground Echoes

Abstract This paper explores the removal of normal ground echoes (GREs) and anomalous propagation (AP) in ground-based radars using a fuzzy logic approach. Membership functions and their weights are derived from the characteristics of radar echoes as a function of radar reflectivity. The dependence on echo intensity is shown to significantly improve the proper identification of GRE/AP. In addition, the proposed method has a better performance at lower elevation angles. The overall performance is comparable with that from a polarimetric approach and can thus be easily implemented in operational radars.

scholarly journals Hydrometeor classification from dual-polarized weather radar: extending fuzzy logic from S-band to C-band data

2006 ◽  
Vol 7 ◽  
pp. 109-114 ◽  
Author(s):  
F. S. Marzano ◽  
D. Scaranari ◽  
M. Celano ◽  
P. P. Alberoni ◽  
G. Vulpiani ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Fuzzy Classification ◽  
Membership Functions ◽  
Simulation Data ◽  
Model Based ◽  
Classification Technique ◽  
Best Fitting ◽  
T Matrix ◽  
Differential Reflectivity

Abstract. A model-based fuzzy classification method for C-band polarimetric radar data, named Fuzzy Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC), is presented. Membership functions are designed for best fitting simulation data at C-band, and they are derived for ten different hydrometeor classes by means of a scattering model, based on T-Matrix numerical method. The fuzzy logic classification technique uses a reduced set of polarimetric observables, i.e. copolar reflectivity and differential reflectivity, and it is finally applied to data coming from radar sites located in Gattatico and S. Pietro Capofiume in North Italy. The final purpose is to show qualitative accuracy improvements with respect to the use of a set of ten bidimensional MBFs, previously adopted and well suited to S-band data but not to C-band data.

Measurements of Heavy Convective Rainfall in the Presence of Hail in Flood-Prone Areas Using an X-Band Polarimetric Radar

2013 ◽  
Vol 52 (2) ◽  
pp. 395-407 ◽  
Author(s):  
Sergey Y. Matrosov ◽  
Robert Cifelli ◽  
David Gochis
Keyword(s):  
Liquid Fraction ◽  
Radar Data ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Differential Phase Shift ◽  
Rain Gauges ◽  
Area Of Interest ◽  
X Band ◽  
Accumulated Rainfall ◽  
Convective Cells

AbstractThe utility of X-band polarimetric radar to provide rainfall estimations with high spatial and temporal resolution in heavy convective precipitation in the presence of hail is explored. A case study involving observations of strong convective cells with a transportable polarimetric X-band radar near Boulder, Colorado, is presented. These cells produced rain–hail mixtures with a significant liquid fraction, causing local flash floods and debris flow in an environmentally sensitive burn area that had been previously affected by wildfire. It is demonstrated that the specific differential phase shift (KDP)–based rainfall estimator provided liquid accumulations that were in relatively good agreement with a network of high-density rain gauges and experimental disdrometers. This estimator was also able to capture the significant variability of accumulated rainfall in a relatively small area of interest, and the corresponding results were not significantly affected by hail. Hail presence, however, was a likely reason for significant overestimation of rainfall retrievals for X-band radar approaches that are based on radar-reflectivity Ze measurements that have been corrected for attenuation in rain. Even greater overestimations were observed with the S-band radar of the weather-service network. In part because of larger range distances, these radar data could not correctly reproduce the spatial variability of rainfall in the burn area.

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