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... reanalyses are validated against independent 10m- wind and sea level pressure observations at the three offshore research platforms (FINO) and at the station Helgoland (German Naval Observatory 1883-1991, DWD Climate Data Centre 1987-present) (see locations in Fig. 1). The selected case studies were the severe storms of 1906, 1962, 1973, 2006, 2007 and 2013. Additional work consisted in comparing the spatial patterns of the 10m-horizontal wind and the sea surface pressure during the storm ...
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... In the literature, multiple comparisons between in-situ measurements and IFS model wind speed close the surface have concluded that for strong wind conditions the IFS model wind speeds are smaller than measured values and smaller than other reanalysis datasets (global, or regional), see for instance (Fery et al., 2018) ...
Offshore Wind power plants have become an important element of the European electrical grid. Studies of metocean site conditions (wind, sea state, currents, water levels) form a key input to the design of these large infrastructure projects. Such studies heavily rely on reanalysis datasets which provide decades-long model time series over large areas. In this article, we address a known deficiency of one these reanalysis datasets, ERA5, namely that it underestimates strong wind speeds offshore. For doing so, comparisons are made against CFSR/CFSv2 reanalyses as well as high quality wind energy specific in-situ measurements from floating LiDAR systems. The ERA5 surface drag formulation and its sea state dependency are analysed in detail, the conditions of the bias identified, and a correction method is suggested. The article concludes with proposing practical and simple ways to incorporate publicly available, high-quality wind energy measurement datasets in air-sea interaction studies alongside legacy measurements such as met buoys.
Offshore wind power plants have become an important element of the European electrical grid. Studies of metocean site conditions (wind, sea state, currents, water levels) form a key input to the design of these large infrastructure projects. Such studies rely heavily on reanalysis datasets which provide decades-long model time series over large areas. In turn, these time series are used for assessing wind, water levels and wave conditions and are thereby key inputs to design activities such as calculations of fatigue loads and extreme loads and platform elevations. In this article, we address a known deficiency of one these reanalysis datasets, ERA5, namely that it underestimates strong wind speeds offshore. If left uncorrected, this poses a design risk (high and extreme wind, waves and water level conditions are underestimated). Firstly, comparisons are made against CFSR/CFSv2 reanalyses as well as high-quality wind-energy-specific in situ measurements from floating lidar systems. Then, the ERA5 surface drag formulation and its sea state dependency are analysed in detail, the conditions of the bias identified, and a correction method is suggested. The article concludes with proposing practical and simple ways to incorporate publicly available, high-quality wind energy measurement datasets in air–sea interaction studies alongside legacy measurements such as met buoys.
