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On Differentiating Ground Clutter and Insect Echoes from Doppler Weather Radars Using Archived Data : Volume 3, Issue 2 (16/04/2010)

By Rennie, S. J.

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Book Id: WPLBN0003999850
Format Type: PDF Article :
File Size: Pages 18
Reproduction Date: 2015

Title: On Differentiating Ground Clutter and Insect Echoes from Doppler Weather Radars Using Archived Data : Volume 3, Issue 2 (16/04/2010)  
Author: Rennie, S. J.
Volume: Vol. 3, Issue 2
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2010
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Illingworth, A. J., Dance, S. L., & Rennie, S. J. (2010). On Differentiating Ground Clutter and Insect Echoes from Doppler Weather Radars Using Archived Data : Volume 3, Issue 2 (16/04/2010). Retrieved from http://www.hawaiilibrary.net/


Description
Description: Department of Meteorlogy, University of Reading, Reading, UK. Normally wind measurements from Doppler radars rely on the presence of rain. During fine weather, insects become a potential radar target for wind measurement. However, it is difficult to separate ground clutter and insect echoes when spectral or polarimetric methods are not available. Archived reflectivity and velocity data from repeated scans provide alternative methods. The probability of detection (POD) method, which maps areas with a persistent signal as ground clutter, is ineffective when most scans also contain persistent insect echoes. We developed a clutter detection method which maps the standard deviation of velocity (SDV) over a large number of scans, and can differentiate insects and ground clutter close to the radar. Beyond the range of persistent insect echoes, the POD method more thoroughly removes ground clutter. A new, pseudo-probability clutter map was created by combining the POD and SDV maps. The new map optimised ground clutter detection without removing insect echoes.

Summary
On differentiating ground clutter and insect echoes from Doppler weather radars using archived data

Excerpt
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