Abstract. This paper presents a method for estimating offshore extractable wind power at hub height using Sentinel-1 synthetic aperture radar (SAR) data and machine learning. The method was tested in two areas off the Dutch coast, where measurements from Doppler wind lidars installed at the sea surface were available and could be used as a reference. A first machine learning algorithm improved the accuracy of SAR sea surface wind speeds by using geometrical characteristics of the sensor and metadata. This algorithm was trained with wind data measured by a large network of weather buoys at 4 m above sea level. After correction, the bias in SAR wind speed at 4 m versus buoys was 0.02 m s−1, with a standard deviation of error of 0.74 m s−1. Corrected surface wind speeds were then extrapolated to hub height with a second machine learning algorithm, which used meteorological parameters extracted from a high-resolution numerical model. This algorithm was trained with lidar vertical wind profiles and was able to extrapolate sea surface wind speeds at various altitudes up to 200 m. Once wind speeds at hub height were obtained, the Weibull parameters of their distribution were estimated, taking into account the satellites' irregular temporal sampling. Finally, we assumed the presence of a 10 MW turbine and obtained extractable wind power with a 1 km spatial resolution by multiplying the Weibull distribution point by point by its power curve. Accuracy for extractable wind power versus lidars was ± 3 %. Wind power maps at hub height were presented and compared with the outputs of the numerical model. The maps based on SAR data had a much higher level of detail, especially regarding coastal wind gradient. We concluded that SAR data combined with machine learning can improve the estimation of extractable wind power at hub height and provide useful insights to optimize siting and risk management. The algorithms presented in this study are independent and can also be used in a more general context to correct SAR surface winds, extrapolate surface winds to higher altitudes, and produce instantaneous SAR wind fields at hub height.

Abstract. Accurate wind speed determination at the height of the rotor swept area is critical for resource assessments. ERA5 data combined with short-term measurements through the “measure, correlate, predict” (MCP) method are commonly used for offshore applications in this context. However, ERA5 poses limitations in capturing site-specific wind speed variability due to its low resolution. To address this, we developed random forest models extending near-surface wind speed up to 200 m, focusing on the Dutch part of the North Sea. Based on public 2-year floating lidar data collected at four locations, the 15 % testing subset shows that the random forest model trained on the remaining 85 % of site-specific wind profiles outperforms the MCP-corrected ERA5 wind profiles in accuracy, bias, and correlation. In the absence of rotor height measurements, a model trained within a 200 km region handles vertical extension effectively, albeit with increased bias. Our regionally trained random forest model exhibits superior accuracy in capturing wind speed variations and local effects, with an average deviation below 5 % compared to corrected ERA5 with a 20 % deviation from measurements. The 10 min random-forest-predicted wind speeds capture the mesoscale section of the power spectrum where ERA5 shows degradation. For stable conditions the root mean squared error and bias are 12 % and 29 % larger, respectively, compared to unstable conditions, which can be attributed to the decoupling effect at higher heights from the surface during stable stratification. Our study highlights the potential enhancement in wind resource assessment by means of machine learning methods, specifically random forest. Future research may explore extending the random forest methodology for higher heights, benefiting a new generation of offshore wind turbines, and investigating cluster wakes in the North Sea through a multinational network of floating lidars, contingent on data availability.

Abstract. This paper presents a method to calculate offshore wind power at turbine hub height from Sentinel-1 Synthetic Aperture Radar (SAR) data using machine learning. The method is tested in two 70 km × 70 km areas off the Dutch coast where Lidar measurements are available. Firstly, SAR winds at surface level are improved with a machine learning algorithm using geometrical characteristics of the sensor and parameters related to the atmospheric stability extracted from a high-resolution numerical model. The wind speed bias at 10 m above sea level is reduced from −0.42 m s−1 to 0.02 m s−1 and its standard deviation from 1.41 m s−1 to 0.98 m s−1. After improvement, SAR surface winds are extrapolated at higher altitudes with a separate machine learning algorithm trained with the wind profiles measured by the Lidars. We show that, if profiling Lidars are available in the area of study, these two steps can be combined into a single one, in which the machine learning algorithm is trained directly at turbine hub height. Once the wind speed at turbine hub height is obtained, the extractible wind power is calculated using the method of the moments and a Weibull distribution. The results are given assuming an 8 MW turbine typical power curve. The accuracy of the wind power derived from SAR data is in the range ±3–4 % when compared with Lidars. Then, wind power maps at 200 m are presented and compared with the raw outputs of the numerical model at the same altitude. The maps based on SAR data have a much better level of detail, in particular regarding the coastal gradient. The new revealed patterns show differences with the numerical of as much as 10 % in some locations. We conclude that SAR data combined with a high-resolution numerical model and machine learning techniques can improve the wind power estimation at turbine hub height, and thus provide useful insights for optimizing wind farm siting and risk management.

The main objective of this paper is to develop a predictive model of vertical wind speed profile. Response surface methodology (RSM) is used for this purpose. RSM is a set of statistical and mathematical techniques useful for the development, improvement and optimisation of processes. It is mainly used in industrial processes and is successfully applied in this paper to model the wind speed at the hub height of the wind turbine. An unconventional model is adopted due to the nature of the input parameters which cannot be controlled or modified. The model validation indicators, namely correlation coefficient ([Formula: see text]) and root mean square error (RMSE = 1.02), give excellent results when comparing predicted and measured wind speeds. For the same data, the RSM model gives a better RMSE compared to the conventional power law and the artificial neural network.

According to the evaluation of onshore wind energy resources, the hub height of the wind turbine is greater than the height of the wind tower. The influences of atmospheric stability and surface roughness changes are ignored, and the wind shear index is affected by ground topography and geomorphic factors. Moreover, the wind speed is difficult to predict accurately when increased to the hub height. Therefore, based on Monin Obukhov's similarity theory and the least squares calculation model of dynamic roughness, a fitting method based on atmospheric thermal stability under neutral conditions was established. Firstly the spatial distribution of wind shear index at different locations and its correlation with changes in atmospheric stability were evaluated. Secondly, based on the measured data of onshore wind farms with two different landforms, the proposed method was compared with the commonly used calculation scheme of wind speed at hub height using comprehensive shear extrapolation. The results show that the daily variation of wind shear index is correlated with that of atmospheric stability. The new calculation method of the extrapolated wind speed model can evaluate the vertical distribution of wind speed at target height more accurately.

In this study, we focused on the improvements in the simulation of sea surface wind over the complex coastal area. MM5 model being currently used to predict sea surface wind at Korea Meteorological Administration, was used to verify the accuracy to estimate the local wind field. A case study was performed on clear days with weak wind speed(4 m/s), chosen by the analysis of observations. The model simulations were conducted in the southeastern area of Korea during the selected periods, and observational data such as AWS, buoy and QuikSCAT were used to compare with the calculated wind components to investigate if simulated wind field could follow the tendency of the real atmospheric wind field. Results showed that current operational model, MM5, does not estimate accurately sea surface wind and the wind over the coastal area. The calculated wind speed was overestimated along the complex coastal regions but it was underestimated in islands and over the sea. The calculated diurnal changes of wind direction could not follow well the tendency of the observed wind, especially at nighttime. In order to exceed the limitations, data assimilation with high resolution data and more specificated geographical information is expected as a next best policy to estimate accurately the environment of local marine wind field.

sea by winds at the surface. Computers determine wind speeds based on the levels of microwave radiation detected, extrapolated from the winds at the aircraft's The 53rd Weather Reconnaissance Squadron, also known by its nickname, Hurricane Hunters, is a flying unit of the United States Air Force, and "the only Department of Defense organization still flying into tropical storms and hurricanes." Aligned under the 403rd Wing of the Air Force Reserve Command (AFRC) and based at Keesler Air Force Base, Mississippi, with ten aircraft, it flies into tropical c T… Once NHC determines that there is circulation within the disturbance, the mission becomes a sequentially numbered "fix" mission, conducted initially every six hours by rotating flights in cooperation with NOAA missions, and then at three-hour intervals as the storm moves within specified parameters. During the "fix" mission, the ARWO directs the aircraft to the true center or vortex of the storm by monitoring the radar presentation, temperature, pressure, and mapping the wind fields as the aircraft makes left-hand turns. Vortices determined by individual parameters including visual observation may…

balloon can be designed to float along an isobar for extended periods of time, providing a continuous record of wind speeds and other atmospheric data . Tethered