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Abstract. Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. The wind statistics at 10 and 100 m above mean sea level (a.m.s.l.) height using an extrapolation procedure involving simulated long-term stability over oceans are presented for both SAR and ASCAT. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented. This has 3 km grid spacing with data every 30 min for 30 years from 1989 to 2018, while ASCAT has 12.5 km and SAR has 2 km grid spacing. Offshore mean wind speed maps at 100 m a.m.s.l. height from ASCAT, SAR, WRF and ERA5 at a European scale are compared. A case study on offshore winds near Crete compares SAR and WRF for flow from the north, west and all directions. The paper highlights the ability of the WRF model to simulate the overall European wind climatology and the near-coastal winds constrained by the resolution of the coastal topography in the WRF model simulations.

Abstract. The growing size of wind turbines leads to extremely high tip speeds when the blades are rotating. The blades are prone to leading edge erosion when raindrops hit the blades at such high speeds and blade damage will eventually affect the power production until repair or replacement of the blade is performed. Since these actions come with a high cost, it is relevant to estimate the blade lifetime for a given wind farm site prior to wind farm construction. Modelling tools for blade lifetime prediction require input time series of rainfall intensities and wind speeds in addition to a turbine-specific tip speed curve. In this paper, we investigate the suitability of satellite-based precipitation data from the Global Precipitation Measurement (GPM) Mission in the context of blade lifetime prediction. We first evaluate satellite-based rainfall intensities from the Integrated Multi-Satellite Retrievals for GPM (IMERG) final product against in situ observations at 18 weather stations located in Germany, Denmark, and Portugal. We then use the satellite and in situ rainfall intensities as input to a model for blade lifetime prediction together with the wind speeds measured at the stations. We find that blade lifetimes estimated with rainfall intensities from satellites and in situ observations are in good agreement despite the very different nature of the observation methods and the fact that IMERG products have a 30 minute temporal resolution whereas in situ stations deliver 10 minute accumulated rainfall intensities. Our results indicate that the wind speed has a large impact on the estimated blade lifetimes. Inland stations show significantly longer blade lifetimes than coastal stations, which are more exposed to high mean wind speeds. One station located in mountainous terrain shows large differences between rainfall intensities and blade lifetimes based on satellite and in situ observations. IMERG rainfall products are known to have a limited accuracy in mountainous terrain. Our analyses also confirm that IMERG overestimates light rainfall and underestimates heavy rainfall. Given that networks of in situ stations have large gaps over the oceans, there is a potential for utilizing rainfall products from satellites to estimate and map blade lifetimes. This is useful as more wind power is installed offshore including floating installations very far from the coast.

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

Abstract. Modeling leading-edge erosion has been a challenging task due to its multidisciplinary nature involving several variables such as weather conditions, blade coating properties, and operational characteristics. While the process of wind turbine blade erosion is often described by engineering models that rely on the well-known Springer model, there is a glaring need for modeling approaches supported by field data. This paper presents a data-driven framework for modeling erosion damage based on blade inspections from several wind farms in northern Europe and mesoscale numerical weather prediction (NWP) models. The outcome of the framework is a machine-learning-based model that can be used to predict and/or forecast leading-edge erosion damage based on weather data/simulations and user-specified wind turbine characteristics. The model is based on feedforward artificial neural networks utilizing ensemble learning for robust training and validation. The model output fits directly into the damage terminology used by industry and can therefore support site-specific planning and scheduling of repairs as well as budgeting of operation and maintenance costs.

Abstract. Impact fatigue caused by collision with rain droplets, hail stones and other airborne particles, also known as leading-edge erosion, is a severe problem for wind turbine blades. Each impact on the leading edge adds an increment to the accumulated damage in the material. After a number of impacts the leading-edge material will crack. This paper presents and supports the hypothesis that the vast majority of the damage accumulated in the leading edge is imposed at extreme precipitation condition events, which occur during a very small fraction of the turbine's operation life. By reducing the tip speed of the blades during these events, the service life of the leading edges significantly increases from a few years to the full expected lifetime of the wind turbine. This life extension may cost a negligible reduction in annual energy production (AEP) in the worst case, and in the best case a significant increase in AEP will be achieved.

Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other and in the lee of large land masses. Synthetic aperture radar (SAR) offers maps of the wind speed offshore with high resolution over large areas. These can be used to detect horizontal wind speed gradients close to shore and wind farm wake effects. SAR observations have become much more available with the free and open-access data from European satellite missions through Copernicus. Examples of applications and tools for using large archives of SAR wind maps to aid offshore site assessment are few. The Anholt wind farm operated by the utility company Ørsted is located in coastal waters and experiences strong spatial variations in the mean wind speed. Wind speeds derived from the Supervisory Control And Data Acquisition (SCADA) system are available at the turbine locations for comparison with winds retrieved from SAR. The correlation is good, both for free-stream and waked conditions. Spatial wind speed variations along the rows of wind turbines derived from SAR wind maps prior to the wind farm construction agree well with information gathered by the SCADA system and a numerical weather prediction model. Wind farm wakes are detected by comparisons between images before and after the wind farm construction. SAR wind maps clearly show wakes for long and constant fetches but the wake effect is less pronounced for short and varying fetches. Our results suggest that SAR wind maps can support offshore wind energy site assessment by introducing observations in the early phases of wind farm projects.

Abstract. Leading edge erosion (LEE) of wind turbine blades is caused by the impact of hydrometeors, which appear in solid or liquid phase. A reduction of the wind turbine blades’ tip speed during severe precipitation events can mitigate LEE. To apply such an erosion-safe mode, a precipitation nowcast is required. Theoretical considerations indicate that the time a raindrop needs to fall to the ground is sufficient to reduce the tip speed. Furthermore, it is described that a compact vertical pointing radar that measures rain in different heights with a sufficient high spatio-temporal resolution can nowcast rain for an erosion-safe mode.