Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.
The unwanted return signals from wind turbines can contaminate the weather-radar data that are used by forecasters and automatic algorithms to issue forecast and warnings for severe weather. Since wind turbines have moving components that generate return signals with non-zero Doppler velocity, traditional ground clutter filters are ineffective at removing wind turbine clutter (WTC). In this study, a WTC mitigation algorithm using the range-Doppler spectrum is developed and tested with simulated weather and WTC signals. Once the general locations of the WTC contamination are known, the proposed range-Doppler regression (RDR) algorithm exploits the spatial continuity of weather signals in the range domain to mitigate the WTC contamination while retaining as much weather signal as possible. In contrast to other proposed mitigation algorithms, the RDR algorithm is suited for real-time implementation on typical operational weather radars. Simulated data are used to optimise the parameters of the algorithm and evaluate its performance for stratiform-and convective-precipitation cases with different degrees of WTC contamination. Finally, a real data case is processed to illustrate the RDR algorithm's effectiveness. The results show that the RDR algorithm has the potential to effectively reduce the bias in spectral-moment estimates caused by WTC contamination in an operational environment.
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 nonzero 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. 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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