NDLI: Censoring Biological Echoes in Weather Radar Images

Content Provider	IEEE Xplore Digital Library
Author	Lakshmanan, V. Jian Zhang
Copyright Year	2009
Abstract	Weather radar data is susceptible to several artifacts due to anamalous propagation, ground clutter, electronic interference, sun angle, second-trip echoes and biological contaminants such as insects, bats and birds. Several methods of censoring radar reflectivity data have been devised and described in the literature. However, they all rely on analyzing the local texture and vertical profile of reflectivity fields. The local texture of reflectivity fields suffices to remove most artifacts, except for biological echoes. Biological echoes have proved difficult to remove because they can have the same returned power and vertical profile as stratiformrain or snow. In this paper, we describe a soft-computing technique based on clustering, segmentation and a two-stage neural network to censor all non-precipitating artifacts in weather radar reflectivity data. We demonstrate that the technique is capable of discrimination between light snow, stratiformrain and deep biological ”bloom”.
Starting Page	491
Ending Page	495
File Size	2905056
Page Count	5
File Format	PDF
ISBN	9780769537351
DOI	10.1109/FSKD.2009.640
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Meteorological radar Snow Echo interference Birds Clutter Sun neural network Reflectivity Rain Insects Radar imaging segmentation clustering weather radar
Content Type	Text
Resource Type	Article

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