Publications

The influence of atmospheric stability on floating-lidar-observed turbulence intensity and its correction via machine learning

January 2025

The Floating Lidar Turbulence Intensity Debate: What Is the Industry Willing to Accept to Confront It?

July 2024

CW LiDAR wind direction disambiguation using “VORTEX” modelled data: an offshore case study with EOLOS FLS200

June 2024

A continuous wave (CW) Lidar alone cannot determine the sense of wind direction due to its homodyne behaviour. To resolve this 180º ambiguity, the Lidar requires supplementary data obtained from a sonic anemometer installed in close proximity, which allows the Lidar to discern the true direction of the wind at the high frequency level.

Assessing the power-law wind-profile model uncertainty via floating- and fixed-lidar observations

June 2024

The offshore environment has been looked at by the wind energy industry as an attractive place to install wind farms due to its abundant wind resources. Due to the challenges related to observational data gathering in offshore, the industry often employs wind-profile models as an alternative to assess the wind resource.

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

May 2024

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.

FLS200 Uncertainty Budget

March 2024

FLS200 as a Golden Floating Lidar: A verification campaign case study

January 2024

Floating LiDAR error at high heights and wind speeds: A detection and correction strategy

June 2023

Multi-dimensional data-based correction algorithm for Turbulence Intensity measured by Floating Lidars

June 2023