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Proceedings Paper

Tracking radar echoes by multiscale correlation: a nowcasting weather radar application
Author(s): Simone Tanelli; Luca Facheris; Fabrizio Cuccoli; Dino Giuli
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Paper Abstract

An algorithm for storm tracking through weather radar data is presented. It relies on the crosscorrelation principle as in TREC (Tracking Radar Echoes by Correlation) and derived algorithms. The basic idea is to subdivide the radar maps in Cartesian format in a grid of square boxes and to exploit the so called local translation hypothesis. The motion vector is estimated as the space shift such that corresponding boxes at different times exhibit the maximum correlation coefficient. The discussed technique adopts a multiscale, multiresolution, and partially overlapped box grid which adapts to the radar reflectivity pattern. Multiresolution decomposition is performed through 2D wavelet based filtering. Correlation coefficients are calculated taking into account unreliable data (e.g. due to ground clutter or beam shielding) in order to avoid strong undesired motion estimation biases due to the presence of such stationary features. Data are gathered through a C-band multipolarimetric doppler weather radar. Results show that the technique overcomes some problems highlighted by researchers in previous related studies. Comparison with radial velocity maps shows good correlation values; although they may vary depending on the specific event and on the orographic complexity of the considered area, estimated motion fields are consistent with the shift of the pattern determined through simple visual inspection.

Paper Details

Date Published: 14 December 1999
PDF: 11 pages
Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); doi: 10.1117/12.373261
Show Author Affiliations
Simone Tanelli, Univ. of Florence (United States)
Luca Facheris, Univ. of Florence (Italy)
Fabrizio Cuccoli, Univ. of Florence (Italy)
Dino Giuli, Univ. of Florence (Italy)

Published in SPIE Proceedings Vol. 3871:
Image and Signal Processing for Remote Sensing V
Sebastiano Bruno Serpico, Editor(s)

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