Wednesday, 26 January 2011: 2:15 PM
606 (Washington State Convention Center)
Wind turbines produce clutter signals that can bias estimates of the spectral moments and polarimetric variables of weather signals. These biases can propagate to and negatively influence the output of automatic algorithms, such as severe weather detection and quantitative precipitation estimates. Moreover, existing ground clutter filters are ineffective at removing wind turbine clutter (WTC) contamination because the moving components of the wind turbine produce clutter signals with non-zero Doppler frequency shifts. As the first step in any mitigation scheme, an automatic WTC detection algorithm is necessary and was recently developed by University of Oklahoma and National Severe Storm Laboratory scientists. The detection algorithm operates on a gate-by-gate basis and uses temporal and spectral features to flag contaminated data. After successfully detecting the presence of WTC, the goal is to devise signal processing algorithms that mitigate this contamination so that the weather signal can be recovered and used to estimate the spectral moments and polarimetric variables. However, WTC is inherently non-stationary due to the moving wind turbine blades, which makes frequency-domain-filtering based clutter mitigation methods ineffective. In this work, we propose a new signal processing technique to separate the WTC from the weather signal in the range-Doppler domain. This technique exploits the different spatial and spectral characteristics of WTC and weather signals. Real weather signals and WTC data are used to test the effectiveness of the mitigation scheme.
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