Peter, Justin R. and Seed, Alan and Steinle, Peter J. (2013) Application of a Bayesian classifier of anomalous propagation to single-polarization radar reflectivity data. Journal of Atmospheric and Oceanic Technology, 30 (9). pp. 1985-2005. ISSN 0739-0572
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Abstract
A naïve Bayes classifier (NBC) was developed to distinguish precipitation echoes from anomalous propagation (anaprop). The NBC is an application of Bayes's theorem, which makes its classification decision based on the class with the maximum a posteriori probability. Several feature fields were input to the Bayes classifier: texture of reflectivity (TDBZ), a measure of the reflectivity fluctuations (SPIN), and vertical profile of reflectivity (VPDBZ). Prior conditional probability distribution functions (PDFs) of the feature fields were constructed from training sets for several meteorological scenarios and for anaprop. A Box–Cox transform was applied to transform these PDFs to approximate Gaussian distributions, which enabled efficient numerical computation as they could be specified completely by their mean and standard deviation. Combinations of the feature fields were tested on the training datasets to evaluate the best combination for discriminating anaprop and precipitation, which was found to be TDBZ and VPDBZ. The NBC was applied to a case of convective rain embedded in anaprop and found to be effective at distinguishing the echoes. Furthermore, despite having been trained with data from a single radar, the NBC was successful at distinguishing precipitation and anaprop from two nearby radars with differing wavelength and beamwidth characteristics. The NBC was extended to implement a strength of classification index that provides a metric to quantify the confidence with which data have been classified as precipitation and, consequently, a method to censor data for assimilation or quantitative precipitation estimation.
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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/School / Institute/Centre: | Historic - Institute for Agriculture and the Environment |
| Faculty/School / Institute/Centre: | Historic - Institute for Agriculture and the Environment |
| Date Deposited: | 11 Apr 2017 04:22 |
| Last Modified: | 28 Apr 2017 02:59 |
| Uncontrolled Keywords: | data quality control; radars/radar observations; Bayesian methods |
| Fields of Research (2008): | 04 Earth Sciences > 0401 Atmospheric Sciences > 040199 Atmospheric Sciences not elsewhere classified 04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences 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: | https://doi.org/10.1175/JTECH-D-12-00082.1 |
| URI: | http://eprints.usq.edu.au/id/eprint/30998 |
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