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Proceedings Paper

Image processing techniques applied to rainfall estimation from radar reflectivity measurements
Author(s): John Lane; Francis Merceret; Takis Kasparis; W. Linwood Jones
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Paper Abstract

In this paper we consider the application of standard image processing algorithms to extract parameters from weather radar data that can be subsequently used in converting radar reflectivity Z to rainfall rate R. We also examine the possible advantage of using the total time rate of change of Z, Z, as a modification to the standard Z-R relationship. The rationale behind this approach is based on the observation that convective rainfall often produces drop size distributions (DSDs) which are significantly different from the DSDs produced by stratiform rainfall. This is somewhat similar in concept to previous rainfall regime classification strategy, using the spatial gradient of the horizontal reflectivity pattern to switch from a convective to a stratiform set of Z-R parameters. In this case, however, Z provides a continuous range of values rather than the binary values of the previous classification method. A time series of radar patterns are processed using standard image processing algorithms to segment the image into spatial areas corresponding to storm cells and multi-cell regions. These regions are tracked using thresholding and edge detection algorithms, as well as 2D cross-correlation, for determining the advection velocity. The average Z of each regions calculated and Z is then found by differences between consecutive frames. The final value of Z is used to modify the Z-R transformation for the corresponding region. Note that if Z is small, the resulting formula reduces to a standard form of the Z-R relationship. Similarly, the time rate of change of height at a constant dBZ level is considered as another approach to modifying the standard Z-R relation.

Paper Details

Date Published: 8 July 1998
PDF: 12 pages
Proc. SPIE 3389, Hybrid Image and Signal Processing VI, (8 July 1998); doi: 10.1117/12.316542
Show Author Affiliations
John Lane, Univ. of Central Florida (United States)
Francis Merceret, NASA Kennedy Space Ctr. (United States)
Takis Kasparis, Univ. of Central Florida (United States)
W. Linwood Jones, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 3389:
Hybrid Image and Signal Processing VI
David P. Casasent; Andrew G. Tescher, Editor(s)

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