Evaluation of Low-Level Jets in the Southern Baltic Sea: a Comparison Between Ship-Based Lidar Observational Data and Numerical Models
Wind energy science(2022)
Abstract
In contrast to fixed measuring devices, ship-based lidar systems provide highly reliable wind observations within extensive regions. Therefore, this kind of reference dataset provides a great potential for evaluating the performance of mesoscale numerical models in resembling mesoscale flow phenomena such as low-level jets – essential for an optimal development and operation of wind turbines. This paper presents a comparison between numerical output data from two state-of-the-art numerical datasets (ERA5 and NEWA) and the ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment. The comparison was performed along the route covered by the vessel, as well as in specific locations within this route, to better understand the capabilities and limitations of the numerical models to precisely resemble the occurrence and main properties of low-level jets (LLJs) in different locations. The findings of this study show that the non-stationary nature of ship-based lidar measurements allows evaluating the accuracy of the models when retrieving jets' characteristics and occurrence under different temporal and spatial effects. Numerical models underestimate the occurrence of LLJs, and they struggle to accurately describe their main characteristics, with a particularly large underestimation of the falloff. The found results are to be seen in relation to the characteristics of the observations, such as the data availability, the time–position relation of the selected vessel's route, or the profile height limitation, as well as the features of the jets, with a particular relevance of core height and falloff. Additionally, the results illustrate the temporal and spatial shift between the LLJ events detected by the measurements and the models and the potential benefit of considering such deviations when studying LLJs' climatology through numerical modes.
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