For free-space optical communication or ground-based optical astronomy, ample data of optical turbulence strength (C 2 n) are imperative but typically scarce. Turbulence conditions are strongly site dependent, so their accurate quantification requires in situ measurements or numerical weather simulations. If C 2 n is not measured directly (e.g., with a scintillometer), C 2 n parameterizations must be utilized to estimate it from meteorological observations or model output. Even though various parameterizations exist in the literature, their relative performance is unknown. We fill this knowledge gap by performing a systematic three-way comparison of a flux-, gradient-, and variance-based parameterization. Each parameterization is applied to both observed and simulated meteorological variables, and the resulting C 2 n estimates are compared against observed C 2 n from two scintillometers. The variance-based parameterization yields the overall best performance, and unlike other approaches, its application is not limited to the lowest part of the atmospheric boundary layer (i.e. the surface layer). We also show that C 2 n estimated from the output of the Weather Research and Forecasting model aligns well with observations, highlighting the value of mesoscale models for optical turbulence modeling.

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Wind ramps, or rapid changes in wind speed, are a crucial aspect of atmospheric dynamics and have significant implications for various wind energy applications. For example, wind ramps tend to increase uncertainty in power output predictions. Furthermore, they also induce fatigue damage to wind turbines. In a recent study, DeMarco and Basu (2018; Wind Energy) used long-term observational data from four geographical locations to characterize the tails of the wind ramp probability distribution functions (pdfs). They showed that the pdfs from these various sites (ranging from offshore to complex terrain) portray quasi-universal behavior. The tails of the pdfs are much heavier than the Gaussian pdf and decay faster with increasing time increments. The tail-index statistics, computed via the so-called Hill plots, exhibited minimal height dependency up to approximately one hundred meters above the land or sea surface level. However, wind ramp statistics at higher altitudes at Cabauw (the Netherlands) were quite distinct. In the present study, we investigate if state-of-the-art reanalysis datasets capture the intrinsic traits of wind ramp pdfs. Specifically, we make use of the newly released Copernicus European Regional ReAnalysis (CERRA) dataset in conjunction with the popular fifth-generation ECMWF reanalysis (ERA5) dataset. These datasets allow us to describe the characteristics of wind ramp pdfs at high altitudes (up to 500 m). Given the disparity of the spatial resolution of CERRA (~5.5 km) and ERA5 (~32 km) datasets, we are also able to demonstrate the impact of spatial resolution on simulated tail index characteristics. Lastly, the influence of natural climate patterns such as El-Nino and La-Nina on wind ramp pdfs are examined.@en

In this chapter, we elaborate on several similarity theory-based and empirical wind speed profile formulations. We highlight their strengths and weaknesses through various examples from the literature. Some of these formulations are sometimes inappropriately used in various practical applications (e.g., offshore wind resource assessments). We provide guidance in their proper usage.

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Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.@en

Only a few studies on the overall impact of climate change on offshore wind power production and wind power ramps in the North Sea region have been published. This study focuses on the characteristics of expected wind power production and wind power ramps in the future climate aided by the classification of circulations patterns using a self-organizing map (SOM). A SOM is used to cluster high-resolution CMIP5-CORDEX sea level pressure data into 30 European area weather patterns. These patterns are used to better understand wind power production trends and any potential changes. An increased frequency of occurrence and extended persistence of high pressure systems lasting at least 24 h is projected in the future. Whereas a contrasting reducing tendency for low-pressure systems is estimated. No significant evidence is seen for a change in wind power capacity factor over the North Sea, though tentative evidence is seen for a reduction in wind power ramps. Annual energy production is seen to be dominated by a small number of weather patterns with westerly, south-westerly or north-westerly winds. Future wind power production is projected to become less from westerly winds and more from south-westerly and north-westerly flows. Ramp up events are primarily associated with strong south-westerly winds or weather patterns with a weak pressure gradient. Ramp down events have a stronger association with more north-westerly flow. In a future climate, a reduction in ramp up events associated with weak pressure gradients is projected.

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Free-Space Optical Communication (FSOC) links are considered a key technology to support the increasing needs of our connected, data-heavy world, but they are prone to disturbance through atmospheric processes such as optical turbulence. Since turbulence is highly dependent on local topographic and meteorological conditions, modeling optical turbulence strength (Cn 2) is challenging during the design phase of an optical link or network. Over the past 25 years, (see manuscript PDF for symbol) parameterizations of varying complexities have been combined with various numerical weather prediction models for the spatio-temporal estimation of (Cn 2). However, the outputs of these models can exhibit substantial variability based on the user-defined configuration that determines how atmospheric processes are represented. To address this concern, we propose to run not a single model configuration but multiple diverse ones to generate an ensemble estimate of (Cn 2). We employ the Weather Research and Forecasting model (WRF) with ten different Planetary Boundary Layer (PBL) physics schemes forming a diverse ensemble yielding a probabilistic (Cn 2) estimate. We demonstrate that this ensemble outperforms the individual runs when compared to scintillometer field measurements and show it to be robust against outliers. We believe that FSOC downstream tasks such as link budget estimations should also become more robust if based on a (Cn 2) ensemble estimate compared to single model runs.

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Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations (𝐶2𝑛) is highly relevant for the successful development and deployment of future free-space optical communication links. In this Letter, we propose a physics-informed machine learning (ML) methodology, Π-ML, based on dimensional analysis and gradient boosting to estimate 𝐶2𝑛. Through a systematic feature importance analysis, we identify the normalized variance of potential temperature as the dominating feature for predicting 𝐶2𝑛. For statistical robustness, we train an ensemble of models which yields high performance on the out-of-sample data of R2 = 0.958 ± 0.001.@en

Wind energy is anticipated to play a central role in enabling a rapid transition from fossil fuels to a system based largely on renewable power. For wind power to fulfill its expected role as the backbone – providing nearly half of the electrical energy – of a renewable-based, carbon-neutral energy system, critical challenges around design, manufacture, and deployment of land and offshore technologies must be addressed. During the past 3 years, the wind research community has invested significant effort toward understanding the nature and implications of these challenges and identifying associated gaps. The outcomes of these efforts are summarized in a series of 10 articles, some under review by Wind Energy Science (WES) and others planned for submission during the coming months. This letter explains the genesis, significance, and impacts of these efforts.@en

In this study, we utilize a novel approach to solve the Ekman equations for eddy-viscosity profiles in the stable boundary-layer. By doing so, a well-known expression for the stable boundary-layer height by Zilitinkevich (Boundary-Layer Meteorology, 1972, Vol. 3, 141–145) is rediscovered.

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This Deliverable, 6.1 Renewable Correlation of offshore resources, aims to investigate the potential for correlation between parameters of different renewable energies.
The analysis is based on a first layer on coarse data, and will allow us to identify which resources have more “connectivity”. The resource assessment, even at coarse level, will indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The correlation analysis is expected to showcase the potential of temporal overlaps by the different resources.
The Deliverable examines the overlap of stochastic conditions, the analysis will consider different “time windows” for the base resource, and assess its complementarity with other stochastic renewables. The aim of this deliverable is the estimation of overlap and production of mean maps that indicate to which extend each resource is connected. It is expected that the wind and wave resources will produce higher interest, due to their temporal variability. However, peak solar performance will also be analysed in terms of overlap with wind and/or wave.@en

In this paper, we revisit a well-known formulation of temperature structure parameter (CT2), originally proposed by V. I. Tatarskii. We point out its limitations and propose a revised formulation based on turbulence variance and flux budget equations. Our formulation includes a novel physically-based outer length scale which can be estimated from routine meteorological data.

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Recently, Basu and Holstlag (2021) proposed a unified framework for describing outer length scales (OLS). By utilizing this framework, we document various characteristics of OLS in nocturnal boundary layers over the US Great Plains.

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This Deliverable, 6.2 Renewable Coarse Resource Assessment for the European Region, aims to offer a preliminary overview of the available wind, wave and solar resources across the European Continent. The coarse assessment aims to analyse and assess the current levels of these renewable resources, analysing and discussing the expected variations per regions.
The resource assessment, even at coarse level, can indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The estimated energy densities of wind, wave and solar, are partially the main indicators, we also discuss the impacts of variability, as this is expected to alter the performance of power production, when each resource is utilised by specific technologies.
This report also introduces some of the main statistical approaches and ways to estimate the resource potentials. They will be used and expanded upon in forthcoming Deliverables that will also look into power production, via coupling of high fidelity wind-wave-solar models with specific renewable converters.
Finally, in this Deliverable we discuss the role of open source coarse data and underline their limitations for operational renewable energy projects.@en

In this study, the stability dependence of turbulent Prandtl number (Pr_t) is quantified via a novel and simple analytical approach. Based on the variance and flux budget equations, a hybrid length scale formulation is first proposed and its functional relationships to well-known length scales are established. Next, the ratios of these length scales are utilized to derive an explicit relationship between Pr_t and gradient Richardson number. In addition, theoretical predictions are made for several key turbulence variables (e.g., dissipation rates, normalized fluxes). The results from our proposed approach are compared against other competing formulations as well as published datasets. Overall, the agreement between the different approaches is rather good despite their different theoretical foundations and assumptions.

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In the coming decades, the European energy system is expected to become increasingly reliant on non-dispatchable generation such as wind and solar power. Under such a renewable energy scenario, a better characterization of the extreme weather condition ‘Dunkelflaute’, which can lead to a sustained reduction of wind and solar power, is important. In this paper, we report findings from the very first climatological study of Dunkelflaute events occurring in eleven countries surrounding the North and Baltic Sea areas. By utilizing multi-year meteorological and power production datasets, we have quantified various statistics pertaining to these events and also identified their underlying meteorological drivers. It was found that almost all periods tagged as Dunkelflaute events (with a length of more than 24 h) are in November, December, and January for these countries. On average, there are 50–100 h of such events happening in each of these three months per year. The limited wind and solar power production during Dunkelflaute events is shown to be mainly driven by large-scale high-pressure systems and extensive low-cloud coverage. Even though the possibility of simultaneous Dunkelflaute events in neighboring countries can be as high as 30–40%, such events hardly occur simultaneously in all the eleven countries. Through an interconnected EU-11 power system, the mean frequency of Dunkelflaute drops from 3–9% for the individual countries to approximately 3.5% for the combined region, highlighting the importance of aggregating production over a wide area to better manage the integration of renewable energy generation.@en

Thunderstorm downbursts have been reported to cause damage or failure to wind turbine arrays. We extend a large-eddy simulation model used in previous work to generate downburst-related inflow fields with a view toward defining correlated wind fields that all turbines in an array would experience together during a downburst. We are also interested in establishing what role contrasting atmospheric stability conditions can play on the structural demands on the turbines. This interest is because the evening transition period, when thunderstorms are most common, is also when there is generally acknowledged time-varying stability in the atmospheric boundary layer. Our results reveal that the structure of a downburst’s ring vortices and dissipation of its outflow play important roles in the separate inflow fields for turbines located at different parts of the array; these effects vary with stability. Interacting with the ambient winds, the outflow of a downburst is found to have greater impacts in an “average” sense on structural loads for turbines farther from the touchdown center in the stable cases. Worst-case analyses show that the largest extreme loads, although somewhat dependent on the specific structural load variable considered, depend on the location of the turbine and on the prevailing atmospheric stability. The results of our calculations show the highest simulated foreaft tower bending moment to be 85.4 MN-m, which occurs at a unit sited in the array farther from touchdown center of the downburst initiated in a stable boundary layer.

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In the near future, wind and solar generation are projected to play an increasingly important role in Europe's energy sector. With such fast-growing renewable energy development, the presence of simultaneous calm wind and overcast conditions could cause significant shortfalls in production with potentially serious consequences for system operators. Such events are sometimes dubbed “Dunkelflaute” events and have occurred several times in recent history. The capabilities of contemporary mesoscale models to reliably simulate and/or forecast a Dunkelflaute event are not known in the literature. In this paper, a Dunkelflaute event near the coast of Belgium is simulated utilizing the Weather Research and Forecasting (WRF) model. Comprehensive validation using measured power production data and diverse sets of meteorological data (e.g., floating lidars, radiosondes, and weather stations) indicates the potential of WRF to reproduce and forecast the boundary layer evolution during the event. Extensive sensitivity experiments with respect to grid-size, wind farm parameterization, and forcing datasets provide further insights on the reliability of the WRF model in capturing the Dunkelflaute event.

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Large-scale weather patterns and their variability can influence both the amount of wind power production and its temporal variation, i.e., wind power ramps. In this study, we use a self-organizing map to cluster hourly sea level pressure into a discrete number of weather patterns. The dependency of wind power production and wind power ramps on these weather patterns is studied for the Belgian offshore wind farm fleet. A newly developed wavelet-surrogate ramp-detection algorithm is used for the identification of wind power ramps. It was observed that low-pressure systems, southwesterly and northeasterly wind flows are often associated with high levels of wind power production. Regarding wind power ramps, the type of transition between weather patterns was shown to determine whether ramp up or ramp down events would occur. Ramp up events tend to occur due to the transition from a high-pressure to a low-pressure system, or the weakening of the intensity of a deep low-pressure system. The reverse is associated with ramp down events.@en

Large-scale weather systems have the potential to modulate offshore wind energy production. The Northern European sea areas have recently seen a rapid increase in wind power capacity and thus there is a need to understand how different weather systems affect offshore production from the perspective of energy system integration. In this study, mean sea level pressure data from a new-generation reanalysis (ERA5) are utilised to classify synoptic systems into 30 different weather patterns using a self-organising map (SOM) approach. ERA5 wind speeds are then used in conjunction with a reference 8 MW wind turbine power curve to estimate wind power values at selected offshore sites. We assess how wind power output varies for different weather patterns, specifically, the impact on power production and power ramps.

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We use a database of direct numerical simulations to evaluate parametrizations for energy dissipation rate in stably stratified flows. We show that shear-based formulations are more appropriate for stable boundary layers than commonly used buoyancy-based formulations. As part of the derivations, we explore several length scales of turbulence and investigate their dependence on local stability.

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