A Machine Learning Approach to Correct Turbulence Intensity measured by Floating Lidars

In this work we introduce a supervised Machine Learning (ML) model to correct the Turbulence Intensity (TI) measured by Floating LiDAR Systems (FLS) in offshore environment. The model was developed using data from 46 EOLOS-FLS200 validation campaigns (≈4.6 years) carried out at three reference sites in the North Sea.