Looking for an Offshore Low-Level Jet Champion among Recent Reanalyses: A Tight Race over the Baltic Sea

Abstract

With an increasing interest in offshore wind energy, focus has been directed towards large semi-enclosed basins such as the Baltic Sea as potential sites to set up wind turbines. The meteorology of this inland sea in particular is strongly affected by the surrounding land, creating mesoscale conditions that are important to take into consideration when planning for new wind farms. This paper presents a comparison between data from four state-of-the-art reanalyses (MERRA2, ERA5, UERRA, NEWA) and observations from LiDAR. The comparison is made for four sites in the Baltic Sea with wind profiles up to 300 m. The findings provide insight into the accuracy of reanalyses for wind resource assessment. In general, the reanalyses underestimate the average wind speed. The average shear is too low in NEWA, while ERA5 and UERRA predominantly overestimate the shear. MERRA2 suffers from insufficient vertical resolution, which limits its usefulness in evaluating the wind profile. It is also shown that low-level jets, a very frequent mesoscale phenomenon in the Baltic Sea during late spring, can appear in a wide range of wind speeds. The observed frequency of low-level jets is best captured by UERRA. In terms of general wind characteristics, ERA5, UERRA, and NEWA are similar, and the best choice depends on the application.

Extreme Wind and Waves in U.S. East Coast Offshore Wind Energy Lease Areas

“…The first employs a generalized Pareto distribution (GPD) to describe exceedances of a fixed threshold, while the second uses Generalized Extreme Value (GEV) distributions typically fitted to annual maximum values. The GEV cumulative density distribution of property x, with location parameter μ, scale parameter β, and shape parameter κ is given by [51]:…”

“…Maximum likelihood estimation methods (with iteration) are used to derive the 3 parameters in Equation (1) from samples of Umax. [51] Methods for deriving the distribution parameters of Equations (1) and (2) employed herein are summarized in Table 3. A primary challenge in deriving estimate value estimates is selection of an appropriate probability distribution to describe the extremes.…”

“…In the absence of a mixed climate for extreme wind speeds [52,53], this relationship is linear. To assess the sensitivity of the U50 estimates to the precise data record used, 95% uncertainty intervals are determined using bootstrapping with 1000 iterations for the GG, GMM, and GML methods [51].…”

How do NEWA and ERA5 compare for assessing offshore wind resources and wind farm siting conditions?

“…Various numerical datasets, including NEWA and ERA5, were compared in [7] at eight sites in France, finding underestimation of wind speeds by ERA5 in mountainous areas, which was also found by [3], who analyzed ERA5 for various sites worldwide with different topographic and meteorological conditions. Four Lidar measurements in the Baltic Sea were compared to numerical data in [8], and an underestimation of the mean wind speed was found for all datasets with ERA5 showing the lowest errors. The authors in [4] verified and compared the NEWA data with ERA5 regarding wind speed bias and power bias at various sites in Europe and different surrounding terrain, outlining the advantage of NEWA at predicting the mean wind speed.…”

Baltic Offshore Wind Energy Development—Poland’s Public Policy Tools Analysis and the Geostrategic Implications

“…The second most promising European region in terms of potential and planned offshore wind power capacity is the Baltic Sea. It has a high potential to scale up the deployment of offshore wind power due to a relatively short distance from the coast, which lowers the cost for grid connections; the wind power potential, which in this semi-enclosed basin proved to be underestimated [23][24][25]; and also regional cooperation and national policies aimed at greater development of the Baltic wind energy. Apart from Russia, all the littoral states are EU members and all of them plan to increase the use of offshore wind power by 2030.…”