Evaluating Dual-Doppler Radar Wind Speed Performance Under Different Scanning Strategies and Atmospheric Conditions

With the rapid growth of wind farms worldwide, it is increasingly relevant to identify reliable measurement approaches for characterizing wind turbine wakes. Scanning wind lidars are commonly used for this purpose, but they are constrained by limited spatial coverage and can face performance challenges under certain atmospheric conditions (i.e., precipitation). In contrast, a dual-Doppler radar setup, in which two radars sample the same scanning volume, has emerged as a promising approach. It retrieves wind velocities over larger areas and can capture the spatial extent and evolution of turbine wakes, particularly during precipitation when radar returns are strongest. Recent field operations as part of the American WAKE ExperimeNt have demonstrated the value in using this dual-Doppler radar approach over a large domain encompassing several wind farms. However, there still remains uncertainty about its ability to resolve winds in different locations and under various radar configurations and atmospheric regimes. 

The 2025 Krummendeich field experiment in northern Germany provided an ideal testbed to address this gap. This onshore campaign took place at a wind farm consisting of only a few turbines to target finer, turbulence-based measurements. Along with the dual-doppler setup of the radars, the site was equipped with scanning lidars, met masts, laser disdrometers, a commercial vertical profiling lidar, and other instruments. By leveraging observations collected from Krummendeich, dual-Doppler radar wind measurements can be validated against datasets previously used extensively in wind-energy research, and a systematic evaluation of this novel dual-Doppler setup can provide new insights into how its performance responds to different external factors. As part of an ongoing effort, this study examines under what conditions the dual-Doppler radar approach does and does not supply optimal data availability for resolving turbine wakes. Preliminary results suggest that data coverage and quality increases with rainfall intensity, motivating a more in-depth analysis across precipitation regimes, atmospheric conditions, and scan configurations.