This thesis investigates whether the scale-dependent bias observed in Sentinel-1 SAR CMOD5.N wind retrievals is primarily a mathematical consequence of nonlinear aggregation or evidence of physical scale-dependence in the backscatter--wind relationship. The study analyzes 2020–2021 Sentinel-1 Wave Mode imagettes at two incidence-angle geometries (WV1: ~23.5°; WV2: ~36.5°), aggregating retrieved fields from 100 m native resolution to scales of 200–2500 m under unstable atmospheric conditions. A second-order Taylor expansion of the CMOD5.N forward model is derived and reformulated into an operational prediction that uses only observable retrieval statistics — retrieved wind variance, incidence angle variance, and GMF curvature. A five-step diagnostic chain tests: whether retrieved variance follows atmospheric turbulence scaling (SQ1), whether the Taylor expansion predicts the empirical bias (SQ2), whether GMF curvature remains constant across scales (SQ3), whether bias depends on spectral structure beyond total variance (SQ4), and whether WV1–WV2 differences are consistent with incidence-angle geometry (SQ5). Results across 24 strata show that retrieved wind variance scales with domain size consistently with atmospheric boundary layer turbulence (β in [0.10, 0.20]). The wind-variance curvature term accounts for more than 98% of the Taylor prediction in every stratum. Spectral independence holds universally (mean partial correlations below 0.15), confirming that bias depends on total variance rather than on its spectral distribution. GMF curvature is constant across scales in all WV2 strata and in WV1 strata away from a curvature zero-crossing. Two WV2 strata (9–11 m/s crosswind) achieve the strongest agreement (r = 0.95), classified as pure mathematical aggregation (S1). The majority of strata fall into contaminated aggregation (S2): the mathematical mechanism is correct, but directional contamination from coarse auxiliary wind direction, residual noise, and second-order truncation error degrade prediction quality. WV1 crosswind strata at 9–11 m/s are classified as S3* — a curvature zero-crossing edge case that renders the Taylor prediction ill-conditioned without implying physical scale-dependence. No stratum exhibits evidence of physical scale-dependence (S3). The practical implication is that aggregation bias at SAR resolution can, in principle, be corrected analytically using local variance estimates and GMF curvature, without requiring reformulation of the geophysical model function. ... Read more

Offshore wind energy is expanding rapidly to meet global decarbonisation targets, and realising this transition, together with its socio-economic benefits, depends on large capital investments that are only committed when projects can demonstrate a reliable energy yield. Accurate energy yield assessments are therefore central: they secure competitive financing and limit financial risk. Yet standard power performance models typically assume idealised, neutral atmospheric conditions, while real offshore environments feature dynamic atmospheric boundary layers in which thermodynamic stability alters the wind profile and turbulence intensity. This mismatch introduces uncertainty into yield predictions under real-world stratification.

This research investigates how atmospheric stability influences the aerodynamic power performance of a modern offshore wind turbine, and evaluates the extent to which these thermodynamic effects can be decoupled from interconnected kinematic characteristics.

To isolate these aerodynamic impacts, the study processed continuous operational and meteorological data from a commercial wind farm located in the North Sea. The campaign used a forward-looking nacelle-mounted Light Detection and Ranging (Lidar) system to characterise the incoming wind field, alongside thermodynamic sensors to measure temperature, pressure, and humidity. The analysis was conducted within a wake-free sector, applying instrument availability and operational filters to ensure clean, undisturbed free-stream data. The dataset was then categorised into stable, neutral, and unstable regimes using kinematic proxies such as wind shear and Turbulence Intensity (TI), along with thermodynamic metrics, primarily the Bulk Richardson Number. Standardised empirical power curves were then constructed to quantify performance deviations between regimes.

The majority of the analysis relied on the Bulk Richardson Number classification. Because it directly incorporates thermodynamic information, it provided the most physically consistent basis for classification among the methods tested. Wind shear served as a viable low-cost alternative proxy, while Turbulence Intensity was less reliable due to its high volatility. In terms of aerodynamic performance, the data showed clear deviations in the partial-load operational region: unstable, convective conditions were consistently associated with overperformance, yielding an energy surplus of approximately +1.80% relative to the manufacturer's baseline, while stable, highly stratified conditions corresponded to a near-symmetric deficit of about -1.81%. In the rated power region, the turbine's active pitch control absorbed these stability-driven differences, bringing power anomalies close to zero. Seasonal variations in atmospheric stability, along with other aerodynamic phenomena, were also examined to provide a more complete evaluation of the turbine's performance across the year.

The research concludes that atmospheric stability significantly affects offshore wind turbine efficiency, and that standard neutral baseline power curves tend to overestimate energy production in stable offshore environments. The most plausible explanation is that unstable conditions promote strong convective mixing, producing a more uniform wind profile that improves kinetic energy extraction across the rotor swept area. To reduce financial risk, project developers should integrate stability-weighted probability distributions into pre-construction Energy Yield Assessments rather than relying on generic wind distributions. This methodology might also be suitable for resource assessment planning by deploying floating Lidar systems that capture local thermodynamic data. Future research should use vertically profiling Lidars to implement Rotor Equivalent Wind Speed (REWS) corrections and apply machine learning algorithms for continuous, multivariate stability classification. ... Read more

This study investigates the aerodynamic interaction between two neighbouring offshore wind farms operating in a Conventionally Neutral Boundary Layer (CNBL), where the atmospheric boundary layer (ABL) is neutrally stable, capped by a stable inversion layer and a less stable free atmosphere above. While previous studies have investigated: 1) wake effects between two neighbouring wind farms in a Truly Neutral Boundary Layer (TNBL) with neutral conditions throughout the ABL and above, and 2) the impact of a single wind farm on the flow field in a CNBL, there is a lack of research on the interaction between neighbouring wind farms in a CNBL. Improving our understanding of wind farm interactions in a realistic atmosphere is important for offshore wind farm planning and operation. In this study the performance of the two wind farms under two atmospheric conditions, a CNBL and a TNBL. While the TNBL assumes neutral conditions throughout the ABL and above, the CNBL represents a more complex and realistic modelling approach.

When the wind approaches a wind farm in a TNBL, the combined induction of the turbines creates a zone with increased pressure just before the start of the wind farm, redirecting the flow laterally and vertically. In a CNBL, the vertical component of the redirected wind interacts with a stable inversion layer aloft, which creates an enhanced high pressure area at the start of the wind farm, a phenomena commonly known as global blockage. At the end of the wind farm, the wind is directed down towards the low pressure region in the wake of the wind farm, lowering the inversion layer and creating a zone with even lower pressure. The combination of the two processes described, reduces the wind speed at the start of the wind farm and accelerates it towards the end.

In this study, we look at the interaction of two neighbouring wind farms at varying distances in both a TNBL and CNBL using the relatively fast Multi-Scale Coupled model (Stipa, Ajay, Allaerts, & Brinkerhoff, 2024) which uses a simplified mesoscale model to determine a background flow field which is then used to drive an engineering wake model. Each wind farm consists of a regularly aligned array of five turbines in the spanwise and ten turbines in the streamwise direction with a five rotor diameter (5D) spacing in both dimensions. The turbines are based on the 5 MW NREL reference (Jonkman et al., 2009) and simulations are run at a wind speed of 9 m/s at hub height, on the plateau of the thrust curve. For the CNBL simulations, we choose a Froude number of 1.0 for the inversion layer, resulting in maximum inversion layer displacement due to a phenomena called choking, and a Froude number of 0.10 for the free atmosphere.

Key findings:
1. For infinitely spaced wind farms in a CNBL, lower power output is seen compared to a TNBL due to stronger global blockage effects at the start of each wind farm despite a small speed-up towards the end of the farm.
2. For small wind farm separations (up to ±72D), the speed-up effect at the downstream end of the first wind farm enhances the power output of the second wind farm compared to a TNBL, whilst the blockage effect of the second wind farm negatively impacts the first wind farm.
3. The combined output of the two wind farms at small wind farm spacings is higher than when the farms are infinitely spaced, though lower than in a TNBL. ... Read more

Accurate prediction of wind farm power output under varying atmospheric conditions remains a critical challenge for offshore wind energy planning. This study links wake-model performance explicitly to atmospheric regime using an event-based, time-domain analysis. SCADA data, lidar measurements, and meteorological reanalyses are combined to evaluate two classes of wake models—simplified engineering models and a high-fidelity large-eddy simulation (LES) model—under two contrasting offshore regimes: a shallow, stably stratified boundary layer and a deep, convective one. Representative 24-hour cases from two offshore wind farms (Moray East in Scotland and Mermaid in Belgium) are analysed. The results show that under convective boundary-layer conditions, both engineering models and the LES reproduce farm power with high accuracy, supported by reliable inflow representation and strong turbulent mixing that promotes rapid wake recovery. In this regime, engineering models capture mean wake losses well but remain limited in resolving spatial structure and short-term variability, partly due to heterogeneous inflow that violates their assumption of horizontal homogeneity. Under stable, shallow boundary-layer conditions, performance degrades markedly. Engineering models systematically overestimate farm power and substantially underestimate wake losses, while the LES, although more accurate, also overpredicts due to inflow errors inherited from the mesoscale forcing. These errors originate from misrepresentation of boundary-layer height, wind-shear structure, and low-level jets. The LES additionally indicates weak upstream flow deceleration consistent with global blockage, though observational data were insufficient to confirm this conclusively. Overall, the findings demonstrate that wake-model skill is strongly regime dependent: neither engineering models nor LES provide uniformly reliable predictions across all atmospheric conditions. A regime-aware modelling approach—combining appropriate model selection, calibration, and inflow characterisation—can substantially reduce uncertainty in energy-yield estimation and improve confidence in offshore wind-farm development. ... Read more

This Master's thesis uses Supervisory Control and Data Acquisition (SCADA) and ERA5 data to explore the impact of non-uniform wind conditions on the performance of offshore wind farms. It introduces a novel methodology for detecting and characterising mesoscale atmospheric structures, such as low level jets, convective rolls and wind farm induced gravity waves, and for relating them to variations in power and energy output. Analysis of six wind farms reveals that low-level jets and convective rolls decrease energy production, as do gravity waves. However, convective rolls and gravity waves introduce spatially varying wind, which can increase production locally. The study also presents an innovative approach to determining inversion layer and free atmosphere heights, linking boundary layer dynamics with wind farm output. This research provides a data-driven, foundational understanding of how mesoscale atmospheric phenomena influence the performance of offshore wind farms and offers guidance for future modelling and monitoring approaches. ... Read more

In this thesis, the identification and generation of meteotsunamis on the Southern North Sea are investigated. Although meteotsunamis are well known in other areas, hardly any research has been done on them near the Dutch Coast. Here, nine years of sea level elevation data are evaluated using the classic approach and a subsequent wavelet analysis to identify a list of potential meteotsunamis. Six of those events have been selected, for which the atmospheric conditions were simulated by WRF. To definitively classify them as meteotsunamis, a pressure jump and atmospheric front responsible for the generation are identified. The six events are characterized by computation of their angle of incidence, wave period, and wave height, and are checked for the probability of Proudman resonance. The six selected events were all linked to a pressure jump and an atmospheric front, and thus classified as meteotsunamis. Two events propagated from the North-West and the other four from the South through the English Channel. The wave periods varied between 10 and 25 minutes, and the wave heights varied around 0.20 meters, with a maximum wave height of 0.63 meters. Three events were linked to a front propagating overseas, all of which had a high probability that Proudman resonance contributed to their amplification. These results form a foundation for future research on meteotsunamis on the Southern North Sea. ... Read more