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Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar

M.A Rico-Ramirez, I.D Cluckie, Ian Cluckie

IEEE Transactions on Geoscience and Remote Sensing, Volume: 46, Issue: 7, Pages: 1892 - 1904

Swansea University Author: Ian Cluckie

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DOI (Published version): 10.1109/TGRS.2008.916979

Abstract

This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitat...

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Published in: IEEE Transactions on Geoscience and Remote Sensing
ISSN: 0196-2892 1558-0644
Published: 2008
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URI: https://cronfa.swan.ac.uk/Record/cronfa10535
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2013-11-21T14:19:25.9750734</datestamp><bib-version>v2</bib-version><id>10535</id><entry>2012-04-06</entry><title>Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar</title><swanseaauthors><author><sid>d801af52a3cfb625308bd4301583064e</sid><firstname>Ian</firstname><surname>Cluckie</surname><name>Ian Cluckie</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2012-04-06</date><deptcode>FGSEN</deptcode><abstract>This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual- polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available. Copyright &#xA9; 2008 IEEE Transactions on Geoscience and Remote Sensing.</abstract><type>Journal Article</type><journal>IEEE Transactions on Geoscience and Remote Sensing</journal><volume>46</volume><journalNumber>7</journalNumber><paginationStart>1892</paginationStart><paginationEnd>1904</paginationEnd><publisher/><issnPrint>0196-2892</issnPrint><issnElectronic>1558-0644</issnElectronic><keywords>Weather Radar, Rainfall Measurement, Remote Sensing, Ground Clutter, Anomalous Propagation, Duel-Polarization</keywords><publishedDay>31</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2008</publishedYear><publishedDate>2008-07-31</publishedDate><doi>10.1109/TGRS.2008.916979</doi><url/><notes>Funded by &#xA3;20M+ national EPSRC Flood Risk Management Research Consortium (FRMRC) that was chaired by Cluckie. The data was obtained from the Dual-Polarization Weather Radar commissioned at Thurnham in Kent that was the first radar of its type in the EU and heavily influenced by Cluckie as the principal advisor to the Environment Agency/Meteorological Office. The real-time processing algorithms used by the Met. Office were developed and implemented in Cluckie's research group. The paper focuses on the difficult problems of removing Ground Clutter and Anomalous Propagation along the radar beam in real-time. IF 3.47.</notes><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2013-11-21T14:19:25.9750734</lastEdited><Created>2012-04-06T18:03:46.1253106</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>M.A</firstname><surname>Rico-Ramirez</surname><order>1</order></author><author><firstname>I.D</firstname><surname>Cluckie</surname><order>2</order></author><author><firstname>Ian</firstname><surname>Cluckie</surname><order>3</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2013-11-21T14:19:25.9750734 v2 10535 2012-04-06 Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar d801af52a3cfb625308bd4301583064e Ian Cluckie Ian Cluckie true false 2012-04-06 FGSEN This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual- polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available. Copyright © 2008 IEEE Transactions on Geoscience and Remote Sensing. Journal Article IEEE Transactions on Geoscience and Remote Sensing 46 7 1892 1904 0196-2892 1558-0644 Weather Radar, Rainfall Measurement, Remote Sensing, Ground Clutter, Anomalous Propagation, Duel-Polarization 31 7 2008 2008-07-31 10.1109/TGRS.2008.916979 Funded by £20M+ national EPSRC Flood Risk Management Research Consortium (FRMRC) that was chaired by Cluckie. The data was obtained from the Dual-Polarization Weather Radar commissioned at Thurnham in Kent that was the first radar of its type in the EU and heavily influenced by Cluckie as the principal advisor to the Environment Agency/Meteorological Office. The real-time processing algorithms used by the Met. Office were developed and implemented in Cluckie's research group. The paper focuses on the difficult problems of removing Ground Clutter and Anomalous Propagation along the radar beam in real-time. IF 3.47. COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2013-11-21T14:19:25.9750734 2012-04-06T18:03:46.1253106 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised M.A Rico-Ramirez 1 I.D Cluckie 2 Ian Cluckie 3
title Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
spellingShingle Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
Ian Cluckie
title_short Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
title_full Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
title_fullStr Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
title_full_unstemmed Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
title_sort Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar
author_id_str_mv d801af52a3cfb625308bd4301583064e
author_id_fullname_str_mv d801af52a3cfb625308bd4301583064e_***_Ian Cluckie
author Ian Cluckie
author2 M.A Rico-Ramirez
I.D Cluckie
Ian Cluckie
format Journal article
container_title IEEE Transactions on Geoscience and Remote Sensing
container_volume 46
container_issue 7
container_start_page 1892
publishDate 2008
institution Swansea University
issn 0196-2892
1558-0644
doi_str_mv 10.1109/TGRS.2008.916979
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 0
active_str 0
description This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual- polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available. Copyright © 2008 IEEE Transactions on Geoscience and Remote Sensing.
published_date 2008-07-31T03:11:56Z
_version_ 1763750042634551296
score 11.037581

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