Toward a More Robust Filtering Algorithm for Radar-Derived Wind Speed Data

Given its large range and spatial footprint, radar remote sensing has been met with increased interest in the wind energy community. Paired radars can yield complete wind speed and direction measurements over this large spatial footprint, an invaluable asset for wind farm operators; this was also the main goal of the Texas Tech University X-band radars installed for the American Wake Experiment (AWAKEN). However, care must be taken in how these measurements are quality-controlled and understood. This study examines the data filtering algorithms used in AWAKEN, and how their refinement can depict of atmospheric flows around wind farms differently. The first filtering method used allowed some non-atmospheric measurements to remain while also expunging other, higher-quality measurements, leading to a high bias in radar-estimated wind speeds. Closer inspection of the algorithm and its output led to the discovery of biological tracers (e.g., birds, bats, and insects) influencing wind speed fields, and to the development of a new algorithm that more effectively removed low-quality and non-atmospheric data and preserved high-quality data. Comparisons between radar data processed with the new algorithm and a ground-based lidar in the radars’ overlapping scanning areas showed reduction in the previous high bias, demonstrating the sensitivity of radar measurements to processing techniques.