Journal ArticleDOI
Spectrum-Time Estimation and Processing (STEP) for Improving Weather Radar Data Quality
TLDR
Results show that STEP algorithm can effectively improve quality of polarimetric weather data in the presence of ground clutter and noise.Abstract:
This paper introduces the Spectrum-Time Estimation and Processing (STEP) algorithm developed in the Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU). The STEP processing framework integrates three novel algorithms recently developed in ARRC: spectrum clutter identification, bi-Gaussian clutter filtering, and multi-lag moment estimation. The three modules of STEP algorithm fulfill three functions: clutter identification, clutter filtering and noise reduction, respectively. The performance of STEP has been evaluated using simulated data as well as real data collected by the C-band polarimetric research radar OU-Polarimetric Radar for Innovations in Meteorology and Engineering. Results show that STEP algorithm can effectively improve quality of polarimetric weather data in the presence of ground clutter and noise.read more
Citations
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Identification and removal of non-meteorological echoes in dual-polarization radar data based on a fuzzy logic algorithm
TL;DR: In this article, a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.
Journal ArticleDOI
Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method
TL;DR: In this paper, a novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations, which is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods.
Journal ArticleDOI
Ground Clutter Detection Using the Statistical Properties of Signals Received With a Polarimetric Radar
TL;DR: A test statistic, obtained from the generalized likelihood ratio test (GLRT), and a simple Bayesian classifier (SBC), with inputs from the mean and covariance of the received signals, are developed to detect ground clutter in the presence of weather signals.
Journal ArticleDOI
Narrow-Band Clutter Mitigation in Spectral Polarimetric Weather Radar
TL;DR: A new clutter suppression method, named the moving double spectral linear depolarization ratio (MDsLDR) filter, is put forward to mitigate narrow-band clutter in weather radars to remove ground clutter, artifacts, and noise.
X-band Dual Polarization Phased-Array Radar for Meteorological Applications
TL;DR: X-BAND DUAL POLARIZATION PHASED-ARRAY RADAR for METEOROLOGICAL Applications as mentioned in this paper was used in the development of the X-BAN.
References
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Book
Characterization of ceramics
TL;DR: This article reviewed the principles of Doppler radar and emphasized the quantitative measurement of meteorological parameters, and illustrated the relation of radar data and images to atmospheric phenomena such as tornadoes, microbursts, waves, turbulence, density currents, hurricanes, and lightning.
Journal ArticleDOI
The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS
TL;DR: This version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic.
Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation
A. D. Siggia,R. E. Passarelli +1 more
TL;DR: GMAP is a frequency domain approach that uses a Gaussian clutter model to remove ground clutter over a variable number of spectral components that is dependent on the assumed clutter width, signal power, Nyquist interval and number of samples to achieve the highest possible spectrum resolution.
Journal ArticleDOI
Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering
TL;DR: 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.
Journal ArticleDOI
Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
TL;DR: A study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers, but when optimal weighting was applied, the fuzzyclassifier gave one of the best performances.
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