scholarly journals Scan-to-Scan Correlation of Weather Radar Signals to Identify Ground Clutter

2013 ◽  
Vol 10 (4) ◽  
pp. 855-859 ◽  
Author(s):  
Yinguang Li ◽  
Guifu Zhang ◽  
Richard Doviak ◽  
Darcy Saxion
Keyword(s):  
Spatial Resolution ◽  
Correlation Time ◽  
Correlation Method ◽  
Weather Radar ◽  
Detection Algorithm ◽  
Weather Radars ◽  
Ground Clutter ◽  
Radar Echoes ◽  
Radar Signals

The scan-to-scan correlation method to discriminate weather signals from ground clutter, described in this letter, takes advantage of the fact that the correlation time of radar echoes from hydrometeors is typically much shorter than that from ground objects. In this letter, the scan-to-scan correlation method is applied to data from the WSR-88D, and its results are compared with those produced by the WSR-88D's ground clutter detector. A subjective comparison with an operational clutter detection algorithm used on the network of weather radars shows that the scan-to-scan correlation method produces a similar clutter field but presents clutter locations with higher spatial resolution.

scholarly journals Automatic Detection of Wind Turbine Clutter for Weather Radars

2010 ◽  
Vol 27 (11) ◽  
pp. 1868-1880 ◽  
Author(s):  
Kenta Hood ◽  
Sebastián Torres ◽  
Robert Palmer
Keyword(s):  
Wind Turbine ◽  
Weather Radar ◽  
Real Data ◽  
Radar Data ◽  
Detection Algorithm ◽  
Weather Data ◽  
Atmospheric Conditions ◽  
Fuzzy Logic Algorithm ◽  
Weather Radars ◽  
Anomalous Propagation

Abstract Wind turbines cause contamination of weather radar signals that is often detrimental and difficult to distinguish from cloud returns. Because the turbines are always at the same location, it would seem simple to identify where wind turbine clutter (WTC) contaminates the weather radar data. However, under certain atmospheric conditions, anomalous propagation of the radar beam can occur such that WTC corrupts weather data on constantly evolving locations, or WTC can be relatively weak such that contamination on predetermined locations does not occur. Because of the deficiency of using turbine locations as a proxy for WTC, an effective detection algorithm is proposed to perform automatic flagging of contaminated weather radar data, which can then be censored or filtered. Thus, harmful effects can be reduced that may propagate to automatic algorithms or may hamper the forecaster’s ability to issue timely warnings. In this work, temporal and spectral features related to WTC signatures are combined in a fuzzy logic algorithm to classify the radar return as being contaminated by WTC or not. The performance of the algorithm is quantified using simulations and the algorithm is applied to a real data case from the radar facility in Dodge City, Kansas (KDDC). The results illustrate that WTC contamination can be detected automatically, thereby improving the quality control of weather radar data.

scholarly journals Detection of Ground Clutter for Dual-Polarization Weather Radar Using a Novel 3D Discriminant Function

2019 ◽  
Vol 36 (7) ◽  
pp. 1285-1296 ◽  
Author(s):  
Mohammad-Hossein Golbon-Haghighi ◽  
Guifu Zhang
Keyword(s):  
Discriminant Function ◽  
Weather Radar ◽  
Gaussian Mixture ◽  
Radar Data ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Optimal Decision ◽  
Model Parameters ◽  
Performance Improvements ◽  
Ground Clutter

AbstractA novel 3D discriminant function is introduced as part of a ground clutter detection algorithm for improving weather radar observations. The 3D discriminant function utilizes the phase fluctuations of the received signals for horizontal and vertical polarizations and the dual-scan cross-correlation coefficient. An optimal decision based on the 3D discriminant function is made using a simple Bayesian classifier to distinguish clutter from weather signals. For convenience of use, a multivariate Gaussian mixture model is used to represent the probability density functions of discriminant functions. The model parameters are estimated based on the maximum likelihood using the expectation–maximization (ML-EM) method. The performance improvements are demonstrated by applying the proposed detection algorithm to radar data collected by the polarimetric Norman, Oklahoma (KOUN), weather radar. This algorithm is compared to other clutter detection algorithms and the results indicate that, using the proposed detection algorithm, a better probability of detection can be achieved.

scholarly journals Multi-PRI Signal Processing for the Terminal Doppler Weather Radar. Part I: Clutter Filtering

2005 ◽  
Vol 22 (5) ◽  
pp. 575-582 ◽  
Author(s):  
John Y. N. Cho ◽  
Edward S. Chornoboy
Keyword(s):  
Finite Impulse Response ◽  
Signal Transmission ◽  
Weather Radar ◽  
Spectral Width ◽  
Doppler Weather Radar ◽  
Strong Suppression ◽  
Weather Radars ◽  
Ground Clutter ◽  
Anomalous Propagation ◽  
Repetition Interval

Abstract Multiple pulse repetition interval (multi-PRI) transmission is part of an adaptive signal transmission and processing algorithm being developed to aggressively combat range–velocity ambiguity in weather radars. In the past, operational use of multi-PRI pulse trains has been hampered due to the difficulty in clutter filtering. This paper presents finite impulse response clutter filter designs for multi-PRI signals with excellent magnitude and phase responses. These filters provide strong suppression for use on low-elevation scans and yield low biases of velocity estimates so that accurate velocity dealiasing is possible. Specifically, the filters are designed for use in the Terminal Doppler Weather Radar (TDWR) and are shown to meet base data bias requirements equivalent to the Federal Aviation Administration’s specifications for the current TDWR clutter filters. Also an adaptive filter selection algorithm is proposed that bases its decision on clutter power estimated during an initial long-PRI surveillance scan. Simulations show that this adaptive algorithm yields satisfactory biases for reflectivity, velocity, and spectral width. Implementation of such a scheme would enable automatic elimination of anomalous propagation signals and constant adjustment to evolving ground clutter conditions, an improvement over the current TDWR clutter filtering system.

Modern Weather Surveillance Radars: Hygienic Aspects of Monitoring Electromagnetic Fields at Operators’ Workplaces and in the Environment

2020 ◽  
pp. 34-40
Author(s):  
VN Nikitina ◽  
GG Lyashko ◽  
NI Kalinina ◽  
EN Dubrovskaya ◽  
VP Plekhanov
Keyword(s):  
Electromagnetic Fields ◽  
Weather Radar ◽  
Measuring Instruments ◽  
Residential Areas ◽  
Emf Measurements ◽  
Operating Modes ◽  
Weather Radars ◽  
Radio Transmitters ◽  
Permissible Levels ◽  
Radar Antennas

Summary. Introduction: Location of weather surveillance radars near settlements, in residential areas and on airport premises makes it important to ensure safe levels of electromagnetic fields (EMF) when operating these radio transmitters. EMF maximum permissible levels for weather radars developed in the 1980s are outdated. Our objective was to analyze modern weather surveillance radars to develop proposals for improvement of radar-generated radiofrequency field monitoring. Materials and methods: We studied trends in meteorological radiolocation and technical characteristics of modern weather radars for atmospheric sensing and weather alerts, analyzed regulations for EMF measurements and hygienic assessment, and measured radiofrequency fields produced by weather radar antennas in open areas and at workplaces of operators. Results: We established that modern types of weather radars used in upper-air sensing systems and storm warning networks differ significantly in terms of technical characteristics and operating modes from previous generations. Developed in the 1980s, current hygienic standards for human exposures to radiofrequency fields from weather radar antennas are obsolete. Conclusions: It is essential to develop an up-to-date regulatory and method document specifying estimation and instrumental monitoring of EMF levels generated by weather radars and measuring instruments for monitoring of pulse-modulated electromagnetic radiation.

scholarly journals Bayesian Echo Classification for Australian Single-Polarization Weather Radar with Application to Assimilation of Radial Velocity Observations

2015 ◽  
Vol 32 (7) ◽  
pp. 1341-1355 ◽  
Author(s):  
S. J. Rennie ◽  
M. Curtis ◽  
J. Peter ◽  
A. W. Seed ◽  
P. J. Steinle ◽  
...  
Keyword(s):  
Weather Radar ◽  
Training Data ◽  
Training Dataset ◽  
Bayes Classifier ◽  
Threshold Method ◽  
Ground Clutter ◽  
Radar Network ◽  
Routine Monitoring ◽  
Single Polarization ◽  
Anomalous Propagation

AbstractThe Australian Bureau of Meteorology’s operational weather radar network comprises a heterogeneous radar collection covering diverse geography and climate. A naïve Bayes classifier has been developed to identify a range of common echo types observed with these radars. The success of the classifier has been evaluated against its training dataset and by routine monitoring. The training data indicate that more than 90% of precipitation may be identified correctly. The echo types most difficult to distinguish from rainfall are smoke, chaff, and anomalous propagation ground and sea clutter. Their impact depends on their climatological frequency. Small quantities of frequently misclassified persistent echo (like permanent ground clutter or insects) can also cause quality control issues. The Bayes classifier is demonstrated to perform better than a simple threshold method, particularly for reducing misclassification of clutter as precipitation. However, the result depends on finding a balance between excluding precipitation and including erroneous echo. Unlike many single-polarization classifiers that are only intended to extract precipitation echo, the Bayes classifier also discriminates types of nonprecipitation echo. Therefore, the classifier provides the means to utilize clear air echo for applications like data assimilation, and the class information will permit separate data handling of different echo types.

scholarly journals Gaussian Model Adaptive Processing in Time Domain (GMAP-TD) for Weather Radars

2013 ◽  
Vol 30 (11) ◽  
pp. 2571-2584 ◽  
Author(s):  
Cuong M. Nguyen ◽  
V. Chandrasekar
Keyword(s):  
Time Domain ◽  
Gaussian Model ◽  
Radar Data ◽  
Adaptive Processing ◽  
Colorado State University ◽  
Weather Radars ◽  
Ground Clutter ◽  
Moment Estimation ◽  
Interpolation Procedure ◽  
The Time Domain

Abstract The Gaussian model adaptive processing in the time domain (GMAP-TD) method for ground clutter suppression and signal spectral moment estimation for weather radars is presented. The technique transforms the clutter component of a weather radar return signal to noise. Additionally, an interpolation procedure has been developed to recover the portion of weather echoes that overlap clutter. It is shown that GMAP-TD improves the performance over the GMAP algorithm that operates in the frequency domain using both signal simulations and experimental observations. Furthermore, GMAP-TD can be directly extended for use with a staggered pulse repetition time (PRT) waveform. A detailed evaluation of GMAP-TD performance and comparison against the GMAP are done using simulated radar data and observations from the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar using uniform and staggered PRT waveform schemes.

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