The impact of inter-farm wakes is a growing issue as offshore wind is scaled up to meet renewable energy needs. High-fidelity simulations which capture such wake effects under potential future build-out scenarios are required to enable regional-scale planning which can mitigate wake impacts. Here, we present a machine learning-based workflow for estimating power losses due to inter-farm wake effects, suited to efficient analysis and optimal planning of future build-out. We apply this tool to the assessment of planned build-out in the North Sea. We estimate that percentage power losses due to inter-farm wakes will more than double compared with their current level, reaching 2.4%, and that increased losses in summer will exacerbate natural seasonal variability in resource. Our tool also facilitates sensitivity analysis and optimisation of wind farm fleets with respect to a variety of design choices. In this work we optimise total fleet power output with respect to small adjustments in future farm locations, finding that wake-induced losses can be reduced by one third via careful spatial planning, corresponding to annual economic gains of £160m compared with current plans.

Offshore wind is undergoing rapid expansion in many locations around the world, including the North Sea which is central to UK and EU renewable energy strategies. Turbine wakes, both within and between farms, impact negatively on wind power, particularly offshore. This study investigates the evolving impact of wakes on wind power output in the North Sea, based on a build-out trajectory to 2050. The results indicate that intra-farm wake effects will decrease in future, while inter-farm interactions over up to 125 km will increase, resulting in a small net increase in losses. While over 95% of wake-induced losses in the current North Sea fleet occur due to intra-farm wakes, this reduces to around 70% by 2050, highlighting the growing complexity of regional-scale wind farm planning and design. Our results also indicate a wind ‘theft’ effect, where a given farm produces gradually less power over its lifetime due to continued installation of neighbouring farms. For our case study, inter-farm losses experienced by existing farms increase 6.0% by 2050. Finally, we develop a scenario-specific regression model relating wake losses to installed capacity density, providing a practical wake correction model for use within power systems models, which typically neglect wake effects.

Reanalysis datasets have become indispensable tools for wind resource assessment and wind power simulation, offering long-term and spatially continuous wind fields across large regions. However, they inherently contain systematic wind speed biases arising from various factors, including simplified physical parameterizations, observational uncertainties, and limited spatial resolution. Among these, low spatial resolution poses a particular challenge for capturing local variability accurately. Whereas prevailing industry practice generally relies on either no bias correction or coarse, nationally uniform adjustments, we extend and thoroughly analyse a recently proposed spatially resolved, cluster-based bias correction framework. This approach is designed to better account for local heterogeneity and is applied to 319 wind farms across the United Kingdom to evaluate its effectiveness. Results show that this method reduced monthly wind power simulation errors by more than 32% compared to the uncorrected ERA5 reanalysis dataset. The method is further applied to the MERRA-2 dataset for comparative evaluation, demonstrating its effectiveness and robustness for different reanalysis products. In contrast to prior studies, which rarely quantify the influence of topography on reanalysis biases, this research presents a detailed spatial mapping of bias correction factors across the UK. The analysis reveals that for wind energy applications, ERA5 wind speed errors exhibit strong spatial variability, with the most significant underestimations in the Scottish Highlands and mountainous areas of Wales. These findings highlight the importance of explicitly accounting for geographic variability when correcting reanalysis wind speeds, and provide new insights into region-specific bias patterns relevant for high-resolution wind energy modelling.

Reanalysis datasets are widely used in wind energy modelling and power system analysis, particularly in regions where long-term observational records are unavailable. However, their application at wind-farm scale remains challenging, as reanalysis wind fields often exhibit systematic biases linked to simplified physical representations, observational uncertainty, and coarse spatial resolution. In particular, limited spatial resolution restricts the ability of reanalysis data to represent local variability that is critical for accurate wind power simulation.To address this challenge, we investigate a spatially differentiated bias correction strategy that differs from conventional nationally uniform adjustment schemes. The method adopts a cluster-based representation of wind farm locations, allowing bias correction factors to vary across groups of spatially coherent sites. This framework is applied to a large fleet of UK wind farms to assess its performance under realistic modelling conditions.Using multi-year operational data from UK wind farms, we evaluate monthly wind power simulations driven by corrected and uncorrected reanalysis winds. The spatially differentiated correction yields reductions in simulation error exceeding 30% relative to baseline ERA5-driven results, demonstrating clear improvements over uniform correction approaches. To assess robustness across reanalysis products, the same methodology is applied to MERRA-2, where comparable performance gains are observed.Beyond aggregate error metrics, the analysis reveals pronounced regional structure in reanalysis wind speed errors across the UK. Underestimation is most evident in areas of complex terrain, including the Scottish Highlands and mountainous regions of Wales, whereas wind farms located on flat inland plains and offshore sites exhibit relatively minor and more spatially consistent biases. These spatial patterns highlight the importance of accounting for geographic variability when correcting reanalysis wind speeds and demonstrate the value of spatially resolved correction strategies for wind energy applications.

Rapid deployment of offshore wind is expected within the coming decades to help meet climate goals. With offshore wind turbine lifetimes of 25-30 years, and new offshore leases spanning 60 years, it is vital to consider long-term changes in potential wind power resource at the farm planning stage. Such changes may arise from multiple sources, including climate change, and increasing wake-induced power losses. In this work, we investigate and compare these two sources of long-term change in wind power, for a case study consisting of 21 wind farms within the German Bight. Consistent with previous studies, we find a small but significant reduction in wind resource due to climate change by the end of the 21st century under the high-emission RCP8.5 scenario, compared with a historical period, with a mean power reduction (over an ensemble of seven climate models) of 2.1%. To assess the impact of wake-induced losses due to increasingly dense farm build-out, we model wakes within the German Bight region using an engineering wake model, under various stages of (planned) build-out corresponding to the years 2010-2027. By identifying clusters of wind farms, we decompose wake effects into long-range (inter-cluster), medium-range (intra-cluster) and short-range (intra-farm) effects. Inter-cluster wake-induced losses increase from 0 for the 2010 scenario to 2.5% for the 2027 scenario, with intra-cluster losses also increasing from 0 to 4.3%. Intra-farm losses are relatively constant, at around 13%. While the evolution of wake effects therefore outweighs the climate effect, and impacts over a shorter timescale, both factors are significant. We also find evidence of an interaction between the climate and wake effects. Both climate change and evolving wake effects must therefore be considered within resource assessment and wind farm planning.

High-resolution wind speed forecasts are of great importance to the wind energy industry, from short-term energy forecasting and trading to longer-term resource assessment and planning. Generating high-resolution regional wind forecasts currently requires compute-intensive numerical models to downscale from a global forecast. Black-box AI models, once trained, can produce results in a fraction of the time and cost; however, they tend to produce smoothed outputs, are not interpretable and generalise poorly. The domain-informed AI architecture presented in this work seeks to address these problems by incorporating prior static fields directly into the model architecture. Specifically, the proposed approach combines two sequential U-Nets – the first upsamples the input wind fields and expands the number of feature maps, a fusion layer then injects prior static data such as topography, and a second U-Net generates the final output wind field. This approach improves all performance metrics versus a baseline U-Net model and generalises better to out-of-sample scenarios. In addition, this study compares the performance of several loss functions, including standard pixel-wise measures such as mean-squared error, structural similarity and frequency-focused functions, and a function based on Wiener filter theory. All loss functions, with the exception of the Wiener loss, perform comparably and tend to attenuate higher-frequency detail. Although the Wiener loss encourages higher frequencies, it over-estimates amplitudes. A composite Wiener-L1 loss function balances generating high-frequency detail and correctly predicting amplitudes.

This study assesses global offshore wind energy resources, wake-induced losses, array layout optimisation potential and climate change impacts. Global offshore ambient potential is first mapped based on reanalysis data. Wake-induced losses are then estimated using an engineering wake model, revealing that locations with low (high) resource typically experience larger (smaller) percentage losses. However, the specific wind speed distribution is found to be important, with narrower distributions generally leading to greater losses. This is due to the overlap between the wind speed distribution and the high-sensitivity region of the turbine thrust and power curves. Broadly, this leads to much stronger wake-induced losses in the tropics (which experience the trade winds) than mid-latitudes. However, the tropics also experience a narrower wind direction distribution; the results of this study demonstrate that this leads to greater potential for mitigation of wake effects via layout optimisation. Finally, projected changes in wind potential and wake losses due to climate change under a high-emission scenario are assessed. Many regions are projected to decrease in ambient wind resources, and furthermore these regions will typically experience greater wake-induced losses, exacerbating the climate change impact. These results highlight the different challenges and opportunities associated with exploiting offshore wind resources across the globe.

Abstract Wind energy resource assessment typically requires numerical modeling at fine resolutions, which is computationally expensive for multi‐year timescales. Increasingly, unsupervised machine learning techniques are used to identify representative weather patterns that can help simulate long‐term behavior. Here we develop a novel wind energy workflow that combines the weather patterns from unsupervised clustering with a numerical weather prediction model (WRF) to obtain efficient long‐term predictions of wind farm power and downstream wakes, which provide a good approximation to full WRF simulations at vastly reduced computational cost. We use ERA5 reanalysis data and compare clustering on low altitude pressure and wind velocity, a more relevant variable for wind resource assessment. We also compare varying domain sizes for the clustering. A WRF simulation is run at each cluster center and the results aggregated into a long‐term prediction using a novel post‐processing technique. We consider two case study regions and show that our long‐term predictions achieve good agreement with a year of WRF simulations in 2% of the computational time. Moreover, clustering over a Europe‐wide domain produces good agreement for predicting wind farm power output, but clustering over smaller domains is required for downstream wake predictions which agree with the year of WRF simulations. Our approach facilitates multi‐year predictions of power output and downstream farm wakes, by providing a fast, reliable, and flexible methodology applicable to any global region. Moreover, this constitutes the first tool to help mitigate effects of wind energy loss downstream of wind farms.

There is a need for efficient methods to simulate wind power output to assist the expected rapid uptake of new wind farms. Reanalysis products provide our best estimate for the previous state of the atmosphere and are popular due to their global coverage and convenience. However, these models are known to misestimate wind power output by up to +/- 50% due to significant spatial biases. Previous work applied bias correction methods to improve power simulations. However, there has been no assessment of the spatial and temporal resolution that these bias correction factors should be derived at for the best accuracy. In this paper, we investigate the impact of the spatial and temporal resolution by grouping turbines into a varying number of clusters and varying the frequency at which correction factors are calculated across a year. The correction factors are used to simulate the power output of 4,834 turbines across Denmark resulting in monthly capacity factors. The best correction scenario decreased the error of simulated outputs by 43%. Increasing the spatial resolution of bias correction reduced the error by up to 11%. Correction factors with a bimonthly (every two months) frequency decreased the error by 3% from the time-independent correction factors.

In this work, we assess global offshore wind energy resources, wake-induced losses, array layout optimisation potential and climate change impacts. We first map global offshore ambient wind resource from reanalysis data. We estimate wake-induced losses using an engineering wake model, revealing that locations with low (high) resource typically experience larger (smaller) percentage losses. However, we further find that the specific wind speed distribution is important, with narrower distributions generally leading to greater losses. This is due to the overlap between the wind speed distribution and the high-sensitivity region of the turbine thrust and power curves. Broadly, this leads to much stronger wake-induced losses in the tropics (which experience the trade winds) than mid-latitudes. However, the tropics also experience a narrower wind direction distribution; our results demonstrate that this leads to greater potential for mitigation of wake effects via layout optimisation. Finally, we assess projected changes in wind resource and wake losses due to climate change under a high-emission scenario. Many regions are projected to decrease in ambient wind resources, and furthermore these regions will typically experience greater wake-induced losses, exacerbating the climate impact. These results highlight the different challenges and opportunities associated with exploiting offshore wind resources across the globe.

The Maldives face the threat of tsunamis from a multitude of sources. However, the limited availability of critical data, such as bathymetry (a recurrent problem for many island nations), has meant that the impact of these threats has not been studied at an island scale. Conducting studies of tsunami propagation at the island scale but across multiple atolls is also a challenging task due to the large domain and high resolution required for modelling. Here we use a high-resolution bathymetry dataset of the Maldives archipelago, as well as corresponding high numerical model resolution, to carry out a scenario-based tsunami hazard assessment for the entire Maldives archipelago to investigate the potential impact of plausible far-field tsunamis across the Indian Ocean at the nearshore island scales across the atolls. The results indicate that the bathymetry of the atolls, which are characterized by very steep boundaries offshore, is extremely efficient in absorbing and redirecting incoming tsunami waves. Results also highlight the importance that local effects have in modulating tsunami amplitude nearshore, including the location of the atoll in question, the location of a given island within the atoll, and the distance of that island to the reef, as well as a variety of other factors. We also find that the refraction and diffraction of tsunami waves within individual atolls contribute to the maximum tsunami amplitude patterns observed across the islands in the atolls. The findings from this study contribute to a better understanding of tsunamis across complex atoll systems and will help decision and policy makers in the Maldives assess the potential impact of tsunamis across individual islands. An online tool is provided which presents users with a simple interface, allowing the wider community to browse the simulation results presented here and assess the potential impact of tsunamis at the local scale.

Accurately representing the bottom friction effect is a significant challenge in numerical tidal models. Bottom friction effects are commonly defined via parameter estimation techniques. However, the bottom friction coefficient (BFC) can be related to the roughness of the sea bed. Therefore, sedimentological data can be beneficial in estimating BFCs. Taking the Bristol Channel and Severn Estuary as a case study, we perform a number of BFC parameter estimation experiments, utilising sedimentological data in a variety of ways. Model performance is explored through the results of each parameter estimation experiment, including applications to tidal range and tidal stream resource assessment. We find that theoretically derived sediment-based BFCs are in most cases detrimental to model performance. However, good performance is obtained by retaining the spatial information provided by the sedimentological data in the formulation of the parameter estimation experiment; the spatially varying BFC can be represented as a piecewise-constant field following the spatial distribution of the observed sediment types. By solving the resulting low-dimensional parameter estimation problem, we obtain good model performance as measured against tide gauge data. This approach appears well suited to modelling tidal range energy resource, which is of particular interest in the case study region. However, the applicability of this approach for tidal stream resource assessment is limited, since modelled tidal currents exhibit a strong localised response to the BFC; the use of piecewise-constant (and therefore discontinuous) BFCs is found to be detrimental to model performance for tidal currents.

Calibration with respect to a bottom friction parameter is standard practice within numerical coastal ocean modelling. However, when this parameter is assumed to vary spatially, any calibration approach must address the issue of overfitting. In this work, we derive calibration problems in which the control parameters can be directly constrained by available observations, without overfitting. This is achieved by carefully selecting the ‘experiment design’, which in general encompasses both the observation strategy, and the choice of control parameters (i.e. the spatial variation of the friction field). In this work we focus on the latter, utilising existing observations available within our case study regions. We adapt a technique from the optimal experiment design (OED) literature, utilising model sensitivities computed via an adjoint-capable numerical shallow water model, Thetis . The OED method uses the model sensitivity to estimate the covariance of the estimated parameters corresponding to a given experiment design, without solving the corresponding parameter estimation problem. This facilitates the exploration of a large number of such experiment designs, to find the design producing the tightest parameter constraints. We take the Bristol Channel as a primary case study, using tide gauge data to estimate friction parameters corresponding to a piecewise-constant field. We first demonstrate that the OED framework produces reliable estimates of the parameter covariance, by comparison with results from a Bayesian inference algorithm. We subsequently demonstrate that solving an ‘optimal’ calibration problem leads to good model performance against both calibration and validation data, thus avoiding overfitting.

Numerical models are commonly employed for predicting tidal stream resource, since harmonic analysis is typically insufficient for representing tidal currents. This is particularly true in regions of high-energy flow and complex bathymetric and coastline features, resulting in highly nonlinear dynamics. Within this work, we demonstrate that nondeterministic effects pose a further barrier to resource predictability in such regions. We take the Inner Sound of the Pentland Firth as a case study region, situated between mainland Scotland and the island of Stroma, where nondeterministic effects are introduced by eddy shedding off islands and headlands in the region. We set up a numerical model of the region using the Thetis finite element coastal ocean model. We first demonstrate the presence of nondeterministic effects, by forcing the model with a single tidal constituent and observing the departure from purely periodic behaviour within the Inner Sound, and also by comparing an ensemble of fully-forced model runs generated with perturbed initial conditions. We then take steps towards model calibration with respect to the model bottom friction and viscosity parameters, comparing a variety of choices of model-observation error metrics for this purpose. The non-deterministic component not only places a limit on the predictability of the flow (and hence the tidal stream resource), but also on the calibration process. Focusing on calibration with respect to the bottom friction parameter, we quantify the identifiability limit which the non-deterministic behaviour places on the parameter. We explore the sensitivity of this identifiability limit with respect to the viscosity parameter and the choice of error metric. We find that calibration based on matching the modelled and observed mean kinetic power density is a good choice, and is relatively robust with respect to the non-deterministic behaviour. Based on these results, and on the sensitivity of the error metrics to each model parameter and the mesh resolution, we make recommendations for how to proceed with model calibration in the presence of non-deterministic flow. Since the non-deterministic effect is due to complex, high-energy flow leading to highly nonlinear dynamics, we expect these results to generalise to other regions of high tidal stream resource.

The Maldives faces a unique range of environmental challenges. While the country is almost entirely dependent upon oceanic resources with more than 99% of the area covered by ocean, the absence of a suitable bathymetric map of the seafloor of the Maldives severely limits the adoption and application of modern scientific methods for the prediction of both physical and biological oceanic processes across the country. Here, we present a new bathymetric data set for the country based upon accumulating data from various sources and demonstrate that the synthesis of these provides a far more accurate representation of the shallow water areas of the region than currently available products. We also show that the new bathymetric data set is of sufficiently high resolution to model tidal flows across the archipelago for the first time. The new bathymetric data set provides numerous opportunities to better understand oceanic flow, associated physical and biogeochemical processes, and their correlation to one another across the Maldives archipelago.

Changes to coastlines and bathymetry alter tidal dynamics and associated sediment transport processes, impacting upon a number of threats facing coastal regions, including flood risk and erosion. Especially vulnerable are coral atolls such as those that make up the Maldives archipelago, which has undergone significant land reclamation in recent years and decades and is also particularly exposed to sea level rise. Here we develop a tidal model of Malé Atoll, Maldives, the first atoll-scale and multi-atollscale high-resolution numerical model of the atolls of the Maldives and use it to assess potential changes to sediment grain size distributions in the deeper atoll basin, under sea level rise and coastline alteration scenarios. The results indicate that the impact of coastline modification over the last two decades at the island scale is not limited to the immediate vicinity of the modified island but can also significantly impact the sediment grain size distribution across the wider atoll basin. Additionally, the degree of change in sediment distribution which can be associated with sea level rise that is projected to occur over relatively long time periods is predicted to occur over far shorter time periods with coastline changes, highlighting the need to better understand, predict and mitigate the impact of land reclamation and other coastal modifications before conducting such activities.

Numerical storm surge models are essential to forecasting coastal flood hazard and informing the design of coastal defences. However, such models rely on a variety of inputs, many of which carry uncertainty. An awareness and understanding of the sensitivity of model outputs with respect to those uncertain inputs is therefore essential when interpreting model results. Here, we use an unstructured-mesh numerical coastal ocean model, Thetis, and its adjoint, to perform a sensitivity analysis for a hindcast of the 5th/6th December 2013 North Sea surge event, with respect to the bottom friction coefficient, bathymetry and wind stress forcing. The results reveal spatial and temporal patterns of sensitivity, providing physical insight into the mechanisms of surge generation and propagation. For example, the sensitivity of the skew surge to the bathymetry reveals the protective effect of a sand bank off the UK east coast. The results can also be used to propagate uncertainties through the numerical model; based on estimates of model input uncertainties, we estimate that modelled skew surges carry uncertainties of around 5 cm and 15 cm due to bathymetry and bottom friction, respectively. While these uncertainties are small compared with the typical spread in an ensemble storm surge forecast due to uncertain meteorological inputs, the adjoint-derived model sensitivities can nevertheless be used to inform future model calibration and data acquisition efforts in order to reduce uncertainty. Our results demonstrate the power of adjoint methods to gain insight into a storm surge model, providing information complementary to traditional ensemble uncertainty quantification methods.

Numerical tidal models are essential to the study of a variety of coastal ocean processes, but typically rely on uncertain inputs, including a bottom friction parameter which can in principle be spatially varying. Here we employ an adjoint-capable numerical ocean model, Thetis, and apply it to the Bristol Channel and Severn Estuary, using a spatially varying Manning coefficient within the bottom friction parameterisation. The spatial variation in the coefficient is a priori constrained by a categorisation of the sediment type found on the sea bed into three groups: rock, sediment containing gravel, and sediment containing only sand. We compare two calibration methods to estimate the three corresponding Manning coefficients using tide gauge observation data. The first method consists of Bayesian inversion via a Markov Chain Monte Carlo algorithm, using a Gaussian process emulator as a surrogate for the full numerical model, while the second uses a gradient-based approach via the adjoint mode of the numerical model. We first apply these methods to a `synthetic experiment, then to the assimilation of real data; in each experiment we compare the results from each method and their respective computational cost. We further find that the use of the estimated Manning coefficients also reduces the model-observation misfit when tested within an independent numerical model, TELEMAC-2D, indicating that the calibration procedure has identified non model-specific and physically meaningful parameters.