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Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier

Miguel Angel Rico-Ramirez, Efren Gonzalez-Ramirez, Ian Cluckie, Dawei Han

Meteorological Applications, Volume: 16, Issue: 2, Pages: 227 - 236

Swansea University Author: Ian Cluckie

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DOI (Published version): 10.1002/met.112

Abstract

This paper presents a novel technique to monitor continuously the azimuthal pointing accuracy of a weather radar antenna. The technique consists of cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitatio...

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Published in: Meteorological Applications
ISSN: 1350-4827 1469-8080
Published: 2009
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URI: https://cronfa.swan.ac.uk/Record/cronfa10537
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first_indexed 2013-07-23T12:03:46Z
last_indexed 2018-02-09T04:39:23Z
id cronfa10537
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spelling 2013-11-21T14:21:34.5522762 v2 10537 2012-04-06 Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier d801af52a3cfb625308bd4301583064e Ian Cluckie Ian Cluckie true false 2012-04-06 FGSEN This paper presents a novel technique to monitor continuously the azimuthal pointing accuracy of a weather radar antenna. The technique consists of cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitation conditions. The azimuthal angle lag with the maximum cross-correlation indicates the adjustment needed in antenna pointing. The modelled ground clutter echoes were obtained using high-resolution digital elevation model (DEM) data whereas the measured ground clutter echoes can be obtained in real-time using a Bayes classifier, which identifies the clutter echoes in the presence of precipitation. The technique has been successfully tested in the Thurnham radar in Southeast England. This method can be used by data users as well as radar operators. It should complement the traditional methods based on sun measurements. Copyright © 2008 Royal Meteorological Society Journal Article Meteorological Applications 16 2 227 236 1350-4827 1469-8080 30 6 2009 2009-06-30 10.1002/met.112 This paper presents a novel technique to monitor continuously the azimuthal pointing accuracy of an operational weather radar antenna. The technique consists of cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitation conditions. The azimuthal angle lag with the maximum cross-correlation indicates the adjustment needed in antenna pointing. The modelled ground clutter echoes were obtained using a high-resolution digital elevation model (DEM) whereas the measured ground clutter echoes were obtained in real-time using a Bayes classifier that identifies the clutter echoes in the presence of precipitation. IF 1.32. COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2013-11-21T14:21:34.5522762 2012-04-06T18:21:27.5684122 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Miguel Angel Rico-Ramirez 1 Efren Gonzalez-Ramirez 2 Ian Cluckie 3 Dawei Han 4
title Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
spellingShingle Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
Ian Cluckie
title_short Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
title_full Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
title_fullStr Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
title_full_unstemmed Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
title_sort Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
author_id_str_mv d801af52a3cfb625308bd4301583064e
author_id_fullname_str_mv d801af52a3cfb625308bd4301583064e_***_Ian Cluckie
author Ian Cluckie
author2 Miguel Angel Rico-Ramirez
Efren Gonzalez-Ramirez
Ian Cluckie
Dawei Han
format Journal article
container_title Meteorological Applications
container_volume 16
container_issue 2
container_start_page 227
publishDate 2009
institution Swansea University
issn 1350-4827
1469-8080
doi_str_mv 10.1002/met.112
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 a novel technique to monitor continuously the azimuthal pointing accuracy of a weather radar antenna. The technique consists of cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitation conditions. The azimuthal angle lag with the maximum cross-correlation indicates the adjustment needed in antenna pointing. The modelled ground clutter echoes were obtained using high-resolution digital elevation model (DEM) data whereas the measured ground clutter echoes can be obtained in real-time using a Bayes classifier, which identifies the clutter echoes in the presence of precipitation. The technique has been successfully tested in the Thurnham radar in Southeast England. This method can be used by data users as well as radar operators. It should complement the traditional methods based on sun measurements. Copyright © 2008 Royal Meteorological Society
published_date 2009-06-30T03:11:56Z
_version_ 1763750042885160960
score 11.013148

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