Bayesian echo classification for Australian single-polarization weather radar with application to assimilation of radial velocity observations

Rennie, S. J. and Curtis, M. and Peter, J. and Seed, A. W. and Steinle, P. J. and Wen, G. (2015) Bayesian echo classification for Australian single-polarization weather radar with application to assimilation of radial velocity observations. Journal of Atmospheric and Oceanic Technology, 32 (7). pp. 1341-1355. ISSN 0739-0572

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Abstract

The 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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Access to published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Institute for Agriculture and the Environment
Date Deposited: 10 Apr 2017 06:19
Last Modified: 28 Apr 2017 02:57
Uncontrolled Keywords: Australia; data quality control; radars/radar observations; Bayesian methods; classification
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040199 Atmospheric Sciences not elsewhere classified
04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
04 Earth Sciences > 0401 Atmospheric Sciences > 040106 Cloud Physics
Socio-Economic Objective: D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960299 Atmosphere and Weather not elsewhere classified
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960203 Weather
Identification Number or DOI: 10.1175/JTECH-D-14-00206.1
URI: http://eprints.usq.edu.au/id/eprint/30996

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