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The 18-year database of Envisat & Sentinel 1A-1B Synthetic Aperture Radar (SAR) provides worldwide surface wind measurements at a 1-km resolution. Through an innovative vertical extrapolation methodology published in 2022, these long-term, wide, and high-resolution observations can complement in-situ observations and mesoscale modelling for offshore wind resource assessment. The methodology is based on three steps: (i) derivation of the 10-min SAR surface winds from SAR sea surface roughness, and a site-independent machine learning algorithm based on a large buoy network to correct SAR winds due to biases inherent to the characteristics of the satellite sensors and wind retrieval methodology, (ii) extrapolation up to a few hundred meters based on a second machine learning algorithm trained with in-situ observations and physical parameters from a high-resolution mesoscale model related to e.g. atmospheric stability, and (iii) a final post-processing step to correct for low temporal sampling of the SAR database (one passage every two days for one satellite) and retrieve wind statistics. The resulting output is a 1-km resolution wind atlas in a large (> 3000 km2) offshore and/or coastal area where strong coastal gradients will be accounted for, or it can integrate directly the estimation of the extractible wind power. Here, an improvement of the extrapolation method is presented and applied to a French offshore area in South Brittany. The wind atlases obtained with SAR are found to display a much finer level of details and estimate more precisely the coastal gradient.

International audience; This study is a follow up on a previous one carried out within the frame of the French project SMARTEOLE, during which, a ground-based scanning LiDAR measurement campaign was conducted in the onshore wind farm of Sole du Moulin Vieux. That previous study focused on the wakes of two wind turbines that experienced different degrees of interaction depending on the incoming wind direction, through the processing of LiDAR measurements. The measurement duration (7 months) ensured the statistical convergence of the ensemble-averaged flow fields obtained after holding a categorisation process based on the wind speed at hub height, wind direction, and atmospheric stability, where only near-neutral stability conditions were considered. The present study focuses on integrating the operational data of the wind turbines through SCADA processing to complement the LiDAR wake field observations and to be used as an input for analytical wake models. First, the correlation between the atmospheric stability, deduced from MERRA-2 dataset, and the free-stream turbulence intensity, measured by the wind turbines’ anemometers, is studied for different wind speed ranges. It is observed that the turbulence intensity tends towards a consistent value as the atmospheric stability approaches near-neutral stability conditions, giving confidence into the applied strategy of data categorisation based on MERRA-2 outputs. The influence of the degree of wake interaction on the wake added turbulence, the velocity and power deficits between both turbines is assessed. Clear trends between the wake added turbulence and both the velocity and power deficits are detected. Consequently, two fitting laws are proposed. Then, different analytical wake models and wake superposition methods are fed with the operational data deduced from the processed SCADA data, and are used for predicting the evolution of the velocity deficit within the wake. Some statistical metrics are used for error quantification of the different engineering wake models compared to the scanning LiDAR measurements, used as reference, and Blondel and Cathelain produces the closest results to the field measurements.

A new method is proposed to provide estimates of the turbulence intensity (TI) from measurements of pulsed lidars (light detection and ranging) employing the Doppler beam swinging technique. This method relies on combining the variances of the line-of-sight (LOS) velocities collected by the five independent beams of the lidars and, as such, is referred to as the variance method. The variance method comes with an explicit removal of the Doppler noise (inherent to the instrument) to the variance of the LOS velocities. Turbulence metrics derived from the variance method are compared to that derived from a standard method, commonly used in the wind energy industry. Reference turbulence measurements are provided by a sonic anemometer mounted on a meteorological mast, installed nearby the lidars. Two configurations of the WindCube v2.1 lidars are proposed: the commercial configuration and a prototype configuration, sampling 4 times faster, thus allowing to capture the turbulent energy of smaller eddies. The standard method applied on wind measurements collected by both configurations shows mean errors in TI estimates of more than 50 %. The application of the variance method on measurements collected by the commercial and prototype configuration drops the mean error to 16.7 % and 13.2 % respectively.

With the flourishing of offshore wind projects there is a new socio-economic interest to better our knowledge and forecasting ability of winds within the coastal marine atmospheric boundary layer (MABL). Air-sea fluxes of enthalpy and momentum greatly influence the turbulent and mean winds in the MABL. Already at moderate but certainly at high winds, wave breaking is a key driver of air-sea fluxes and the sea spray generated by whitecaps is thought to be a crucial component when modelling air-sea interactions. Most studies so far have focused on the role of sea spray in enhancing tropical cyclone intensity. Here we investigate its impacts on the MABL under strong orographic wind forcing. A coupled model framework was developed within the scope of the CASSIOWPE project aiming at characterizing the physical environment in the Gulf of Lion (NW Mediterranean Sea) in the prospective of future floating wind farms development. It consists of the non-hydrostatic mesoscale atmospheric model of the French research community Meso-NH, the 3rd generation wave model WAVEWATCH III®, and the oceanic model CROCO. Sea-spray physics were incorporated into the Meso-NH’s surface model SURFEX. Added parametrizations will be detailed and a series of test cases will be presented to illustrate how sea spray alters the MABL under Mistral and Tramontane winds. Several sea-state dependent sea spray generation functions (SSGF) are considered in the present study. The variability in simulated fields linked to the choice of wave forcing or coupling will be showcased to evaluate their suitability in varying fetch conditions. Sea spray production remains to be adequately quantified. Existing measurement derived SSGFs span several orders of magnitude resulting in uncertainties in simulated fields which will be discussed.