An extensive framework for preparing dual-Doppler radar measurements for wake model validation: Application to the AWAKEN large-scale field experiment

Dual-Doppler radar systems have recently emerged as a next-generation method for wind measurement, yet their application to wind wake characterization remains limited by data quality challenges, including measurement artifacts and low data availability. This study presents a comprehensive post-processing framework for dual-Doppler radar data to comprehensively identify and discard measurements with unphysical flow patterns, which is demonstrated for the AWAKEN field campaign. The developed pipeline enables the first systematic application of radar-measured wind fields to investigate wakes that extend over 10 km using over 1 year of continuous observational data with engineering wake simulation at a two-wind-farm scale, a scale and duration only made possible by robust quality control procedures. Analysis of 82 516 individual radar scans over 12 months reveals that external factors such as precipitation, season, and diurnal trends critically influence radar data quality. Using the high-quality cases identified by the pipeline, we validate TurbOPark and Bastankhah (2016) engineering wake models against radar-derived wind fields and characterize wake evolution across different atmospheric stability regimes. Results demonstrate that atmospheric stability exerts substantial control on inter-farm wake interactions: stable conditions produce persistent velocity deficits (5%–6% at 52.5D downstream) with severe power losses in downstream turbines (up to 76%), while neutral conditions enable faster wake recovery and improved downstream performance. This work establishes a practical methodology for extracting reliable wind field observations from dual-Doppler radar systems and demonstrates their value for generating wake model validation datasets and improving wind farm performance prediction under diverse atmospheric conditions.