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Open AccessArticle

Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method

1
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
2
Advanced Radar Research Center, University of Oklahoma, Norman, OK 73072, USA
3
School of Meteorology, University of Oklahoma, Norman, OK 73072, USA
4
National Severe Storms Laboratory, NOAA, Norman, OK 73072, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Werner Singer and Robert W. Talbot
Atmosphere 2016, 7(6), 83; https://doi.org/10.3390/atmos7060083
Received: 2 March 2016 / Revised: 23 May 2016 / Accepted: 7 June 2016 / Published: 15 June 2016
(This article belongs to the Special Issue Radar Meteorology)
A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection. View Full-Text
Keywords: dual-polarization and dual-scan (DPDS); weather radar; polarimetry; Bayesian classifications; clutter detection dual-polarization and dual-scan (DPDS); weather radar; polarimetry; Bayesian classifications; clutter detection
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MDPI and ACS Style

Golbon-Haghighi, M.-H.; Zhang, G.; Li, Y.; Doviak, R.J. Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method. Atmosphere 2016, 7, 83.

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