Posters

Abstract

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. In the event of the auxiliary anemometer failing, the wind direction measured by the Lidar becomes unreliable. This scenario can occur in the harsh offshore conditions that Floating Lidar Systems (FLS) face, and it may take some time before a maintenance visit can be conducted to replace the faulty component. Consequently, the system may suffer from loss of valuable wind direction data. In this study, we propose a method to resolve the ambiguity in wind direction data when anemometer measurements are not available. To achieve this, we suggest using windmodelled data, specifically the Vortex-LES model. This model can provide wind time series with a 10-minute resolution and a grid size of 100m, for a region of 2.5 x 2.5 km covering heights ranging from 50 to 200m. To match the high-frequency resolution of Lidar data, the Vortex data will be downscaled to 1-second intervals. One advantage of using Vortex data is the ability to perform disambiguation using auxiliary data at each height, rather than relying solely on anemometer data at the surface level. The high resolution of the Vortex model and the relatively low precision required for wind direction disambiguation (+- 90 degrees) make this method highly effective and promising for resolving wind direction ambiguity. To assess the effectiveness of this method, we present a case study using the public Floating Lidar System (FLS) campaign of Nyserda in New York, performed by the FLS200. The FLS200, developed by EOLOS Floating Lidar Solutions, is equipped with a CW Lidar from ZX Lidars and has attained stage 3 commercial maturity. In this study, we demonstrate the disambiguation of one year's worth of high-frequency Lidar measurements using Vortex modelled data. To assess the accuracy of the method, we compare the results with the Lidar's own disambiguation process, which utilizes its integrated anemometer. The application of the Vortex-LES model for disambiguating wind direction in FLS200 Lidar measurements demonstrates good consistency with the Lidar's own disambiguation process. In summary, this method effectively resolves wind direction ambiguity in offshore Lidar measurements, making it a valuable solution for overcoming the challenge of losing Lidar auxiliary anemometer data during offshore wind resource assessment campaigns.