Wednesday, 26 January 2011
Radar antenna intercepts thermal radiation from various sources including ground, precipitation, sun, sky, or man-made radiators. Consequently, depending on where the antenna is pointing, the receiver noise power will vary routinely. This creates possibility for incorrect noise power measurements which may lead to reduction of coverage in cases where noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated. Moreover, when an erroneous noise power is used at low signal-to-noise ratios, estimators usually produce biased meteorological variables such as in the case of reflectivity and spectrum width. Therefore, to obtain the best quality of radar products it is desirable to estimate receiver noise for each radial and account for it in the computation of the radar variables. Currently, most weather radars estimate system noise power by either offline measurements or through periodic automatic calibrations as in the NEXRAD network. Ideally, noise power estimates should be computed for every sampling volume, for example, by using spectral noise estimation methods. However, techniques such as those proposed by Hildebrand and Sekhon (1974), and Urkowitz and Nespor (1992, 1994) introduce significant bias in noise power estimates for radar volumes with weather signals that have wide spectrum or if using a small number of samples. Hence, a need arises for a more precise and continuous system noise power calibration that is robust and feasible for real-time implementation on weather radars. In this paper, we propose an effective method to estimate the system noise power dynamically from the in-phase and quadrature data for every antenna position (radial). The technique uses a novel criterion to detect radar volumes that do not contain significant weather signals from which to estimate the system noise power. This technique is evaluated using time-series data collected with the National Weather Radar Testbed Phased-Array Radar (NWRT) and the research WSR-88D KOUN radar, both located in Norman, OK. Results show that the proposed technique produces noise power estimates that are closely matched to the ones obtained from manually identified, signal-free radar volumes at far ranges from the radar; thus, providing empirical validation. A real-time implementation of this technique is expected to significantly improve the data quality of operational weather radars which often rely on accurate noise power estimates.
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