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.

scholarly journals Multi-PRI Signal Processing for the Terminal Doppler Weather Radar. Part II: Range–Velocity Ambiguity Mitigation

2005 ◽  
Vol 22 (10) ◽  
pp. 1507-1519 ◽  
Author(s):  
John Y. N. Cho
Keyword(s):  
Signal Transmission ◽  
Weather Radar ◽  
Radar Data ◽  
Doppler Weather Radar ◽  
Processing Scheme ◽  
Phase Code ◽  
Repetition Interval ◽  
Moments Estimates ◽  
Radar Part ◽  
Adaptive Signal

Abstract Multiple pulse-repetition interval (multi-PRI) transmission is part of an adaptive signal transmission and processing algorithm being developed to combat range–velocity (RV) ambiguity for the Terminal Doppler Weather Radar (TDWR). In Part I of this two-part paper, an adaptive clutter filtering procedure that yields low biases in the moments estimates was presented. In this part, algorithms for simultaneously providing range-overlay protection and velocity dealiasing using multi-PRI signal transmission and processing are presented. The effectiveness of the multi-PRI RV ambiguity mitigation scheme is demonstrated using simulated and real weather radar data, with excellent results. Combined with the adaptive clutter filter, this technique will be used within the larger context of an adaptive signal transmission and processing scheme in which phase-code processing will be a complementary alternative.

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.

Ground Clutter Mitigation and Insect Signature Detection for Polarimetric C-Band Doppler Weather Radar

2021 ◽  
Author(s):  
Fidele Maniraguha ◽  
Anthony Vodacek ◽  
Emmanuel Ndashimye ◽  
Gerard Rushingabigwi
Keyword(s):  
Weather Radar ◽  
Doppler Weather Radar ◽  
Ground Clutter ◽  
Signature Detection ◽  
Clutter Mitigation

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.

Lattice-filter-based ground clutter canceller for pulse Doppler weather radar

2014 ◽  
Author(s):  
Dmytro S. Rachkov ◽  
David I. Lekhovytskiy ◽  
Andrii V. Semeniaka ◽  
Viacheslav P. Riabukha ◽  
Dmytro V. Atamanskiy
Keyword(s):  
Weather Radar ◽  
Doppler Weather Radar ◽  
Ground Clutter ◽  
Lattice Filter ◽  
Pulse Doppler

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 Of Fire and Smoke Plumes, Polarimetric Characteristics

2020 ◽  
Author(s):  
Dusan Zrnic ◽  
Pengfei Zhang ◽  
Valery Melnikov ◽  
Djordje Mirkovic
Keyword(s):  
Urban Area ◽  
Forest Fire ◽  
Computational Electromagnetics ◽  
Weather Radar ◽  
Doppler Weather Radar ◽  
National Weather Service ◽  
Weather Radars ◽  
Wild Fire ◽  
Smoke Plumes ◽  
The Usa

Weather surveillance radars routinely detect smoke of various origin. Of particular significance to the meteorological community are wildfires in forests and/or prairies. For example, one responsibility of the National Weather Service in the USA is to forecast fire outlooks as well as to monitor wild fire evolution. Polarimetric variables have enabled relatively easy recognitions of smoke plumes in data fields of weather radars. Presented here are the fields of these variables from smoke plumes caused by grass fire, brush fire, and forest fire. Histograms of polarimetric data from plumes contrast these three cases. Most of the data are from the polarimetric Weather Surveillance Radar 1988 Doppler (WSR-88D aka Nexrad, 10 cm wavelength) hence the wavelength does not influence these comparisons. Nevertheless, in one case simultaneous observations of a plume by the operational Terminal Doppler Weather Radar (TDWR, 5 cm wavelength) and a WSR-88D is used to infer backscattering characteristic and hence sizes of dominant contributors to the returns. In addition, comparisons with observations by other investigators of plumes from urban area but at a 5 cm wavelength are made. To interpret some measurements Computational Electromagnetics (CEM) tools are applied.

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