Characterization and Bias-Correction of Low-Level Jets at FINO1 Using LiDAR Observations and Reanalysis Data

Abstract. Low-level jets (LLJs) are wind maxima typically observed within a few hundred meters of the surface. They are often associated with high vertical wind shear, which significantly impacts the performance and loading of modern wind turbines. In this study, we characterize LLJs over the North Sea using one year of LiDAR observations from FINO1 (2015–2016) and compare them with reanalysis (ERA5) and hindcast (NORA3) data. We introduce a log-jet fitting method to represent each observed or modeled wind profile with five parameters, enabling a direct comparison between LiDAR and model data. Results show that strong LLJs are generally underestimated by reanalysis and hindcast products. A bias-correction procedure based on quantile mapping is then applied to a 50-year ERA5 dataset to improve the long-term representation of LLJs. K-means clustering further reveals distinct directional and stability-dependent LLJ patterns. The findings highlight the need for detailed modeling of near-surface wind structures and motivate future numerical simulations to clarify the underlying mechanisms that govern LLJ development and variability.