Status: this preprint is currently under review for the journal WES.
Characterization and Bias-Correction of Low-Level Jets at FINO1 Using LiDAR Observations and Reanalysis Data
Hai Bui,Mostafa Bakhoday-Paskyabi,and Joachim Reuder
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.
Received: 20 May 2025 – Discussion started: 10 Jun 2025
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
The manuscript describes a novel method of characterizing low-level jets in lidar data and models, and then applies the method to a substantial dataset. The method is well designed and described, and has the several advantages described. Use of this method uncovers important differences between the observations and the models, which should be useful for model applications and improvement. The presentation is clear, complete, and concise. The only suggestion I have is that some more information about the goodness of fit might be helpful. In other words, how well do the log-jet profiles fit the data, and should criteria be applied to reject some data for insufficiently good fits, beyond the 20% falloff criterion?
Hai Bui,Mostafa Bakhoday-Paskyabi,and Joachim Reuder
Data sets
Supplementary Data and Code for "Characterization and Bias-Correction of Low-Level Jets at FINO1 Using LiDAR Observations and Reanalysis Data"Hai Bui https://zenodo.org/records/15470418
Hai Bui,Mostafa Bakhoday-Paskyabi,and Joachim Reuder
Viewed
Total article views: 203 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
137
40
26
203
17
19
HTML: 137
PDF: 40
XML: 26
Total: 203
BibTeX: 17
EndNote: 19
Views and downloads (calculated since 10 Jun 2025)
Cumulative views and downloads
(calculated since 10 Jun 2025)
Viewed (geographical distribution)
Total article views: 200 (including HTML, PDF, and XML)
Thereof 200 with geography defined
and 0 with unknown origin.
Strong low-altitude winds, known as low-level jets (LLJs), significantly impact offshore wind turbines. We analyzed LLJs at the FINO1 site using LiDAR observations and reanalysis data. Our results show that models tend to underestimate LLJ intensity. To address this, we introduced a new method to characterize wind profiles and applied a correction to 50 years of reanalysis data, yielding a more accurate long-term representation of these wind features.
Strong low-altitude winds, known as low-level jets (LLJs), significantly impact offshore wind...
