Posters

Abstract

Understanding the site-specific wind conditions and climatology of winds is fundamental for the successful development of offshore wind farms. However, executing dedicated measurement campaigns is expensive, and highly reliable observations are still scarce. Hencce, alternative data sources and technologies are essential to foster further the development of offshore projects. Numerical weather prediction models are commonly used in regions where in situ measurements are unavailable, providing long-term wind observations with an extensive spatial and temporal coverage. However, while numerical models have demonstrated good performance in shallow-water regions compared to in-situ measurements, they often fail to describe the spatial and temporal variability of wind accurately, due the inaccurate parameterization of the model variables or their insufficient spatiotemporal resolution. Furthermore, the lack of in situ measurements in deeper offshore regions hinders the validation of these datasets, leading to increased uncertainties in derived wind statistics for such locations. Satellite remote sensing devices emerge as a promising alternative to traditional measurement systems. They have shown a great potential for characterizing ocean winds over large areas, capturing the horizontal wind variability with a temporal coverage of over 15 years. One of the most well-known satellite-based instruments used for wind energy purposes is the Advanced Scatterometer (ASCAT), which provides global ocean wind measurements with a resolution down to 12.5 km. However, the application of satellite measurements for wind energy purposes has been limited by three main factors. First, the limited temporal resolution restricts wind measurements to a few fixed times per day, limiting their capabilities to fully capture the diurnal wind speed variability. Second, satellite measurements are provided at 10 m above the sea surface, requiring the implementation of extrapolation methods to derive wind information at turbine operating heights. Lastly, the trustworthiness of satellite retrievals remains a knowledge gap, due to the lack of available in situ datasets for validation especially in deep water regions. To address these challenges, this study aims to assess the accuracy of ASCAT-derived wind speed profiles in the nearshore and offshore locations of the Northern Baltic Sea by conducting a comprehensive comparison against ship-based lidar measurements. Additionally, the numerical model output data from the ECMWF Reanalysis 5th generation (ERA5) is included in this analysis to evaluate and highlight the different wind profiles obtained through the application of the different datasets. To accomplish this comparison, we employ a long-term stability correction approach to derive wind profiles from the ASCAT 10 m measurements; and introduce a novel collocation strategy for comparing ASCAT-derived and ERA5 profiles against the ship-mounted lidar observations, which has not been previously reported. To the authors' knowledge, this study represents the first comprehensive comparison of ASCAT wind profiles extrapolated to wind turbine operational heights against non-stationary in situ measurements, covering a wide horizontal extent that extends from nearshore to offshore locations and therefore, providing valuable insights about the prospective applicability of ASCAT observations for wind characterization within varying spatial constraints.