Turbulence Characteristics of Wind-Speed Fluctuations in the Presence of Open Cells: A Case Study

Larsén, Xiaoli Guo; Larsen, Søren Ejling; Petersen, Erik Lundtang; Mikkelsen, Torben

doi:10.1007/s10546-019-00425-8

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…It is known that the uncertainty of the SAR wind greater than 30 ms −1 is large, though the spatial pattern shown in the data is useful to validate the spatial structure of wind in the presence of the open cells. Bakan and Schwarz reported that the average diameter of open cells is 41 km with a standard deviation of 15 km, although the size depends on the observed latitude: cases with diameters of 20 to 30 km prevail north of 70° N, while diameters of 50 to 60 km are most abundant south of 60° N. A summary of the characteristics of the open cells over the North Sea can be found in Larsén et al, where it was also shown that the wind fields in the presence of open cells are characterized by large spatiotemporal fluctuations in the wind speed. While mesoscale modeling can capture the size, propagation, and wind fluctuation of open cells,() the exact locations of these cells are difficult to model.…”

Section: Resultsmentioning

confidence: 99%

Estimation of offshore extreme wind from wind‐wave coupled modeling

Larsén

Bolaños

et al. 2019

Wind Energy

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A coupledwind‐wave modeling system is used to simulate 23 years of storms and estimate offshore extreme wind statistics. In this system, the atmospheric Weather Research and Forecasting (WRF) model and Spectral Wave model for Near shore (SWAN) are coupled, through a wave boundary layer model (WBLM) that is implemented in SWAN. The WBLM calculates momentum and turbulence kinetic energy budgets, using them to transfer wave‐induced stress to the atmospheric modeling. While such coupling has a trivial impact on the wind modeling for 10‐m wind speeds less than 20 ms−1, the effect becomes appreciable for stronger winds—both compared with uncoupled WRF modeling and with standard parameterization schemes for roughness length. The coupled modeling output is shown to be satisfactory compared with measurements, in terms of the distribution of surface‐drag coefficient with wind speed. The coupling is also shown to be important for estimation of extreme winds offshore, where the WBLM‐coupled results match observations better than results from noncoupled modeling, as supported by measurements from a number of stations.

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Section: Resultsmentioning

confidence: 99%

Estimation of offshore extreme wind from wind‐wave coupled modeling

Larsén

Bolaños

et al. 2019

Wind Energy

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“…As a next step, the low-resolution time series are temporally downscaled by adding physically realistic turbulence fluctuations using a recent full-scale spectral model [15,16]. Finally, the resulting spectrum can be converted to the time domain with a Fourier transform to obtain high-frequency wind speed time series for any desired output resolution dt.…”

Section: Methodsmentioning

confidence: 99%

“…However, similar as in the Spectral Correction Method (SCM) from [9,11], the spectrum for higher frequencies is extended following S(f ) ∝ f −5/3 which is only valid for lower frequencies and therefore limits its applicability to model fluctuations up to ∼ 10 min. Recent studies present full-scale boundary layer spectra that cover the mesoscale atmospheric motions down to the fast fluctuations of the boundary layer turbulence [15,16]. Therefore, these spectral models enable to simulate wind speed fluctuations for a higher frequency and generate realistic turbulence that can be added to the mesoscale time series.…”

Section: Introductionmentioning

confidence: 99%

“…This work addresses this issue by first applying a correction on the mesoscale fluctuations in a similar fashion as the SCM but directly on the spectral components rather than on the spectral moments to allow for a time series analysis (subsection 3.1). Then, physically realistic turbulence at any desired sampling are generated by building upon the advancements in the boundary layer spectral models from [15] and [16] (subsection 3.1). The combination of these modelled low-and high-frequency spectra covers the full boundary-layer spectrum and thus closes the spectral gap between the meso-and microscale (subsection 3.2).…”

Section: Introductionmentioning

confidence: 99%

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Generating long-term sub-hourly wind speed time series by coupling mesoscale models with full-scale spectra

Schillebeeckx

Leroy

2022

J. Phys.: Conf. Ser.

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Time series-based wind studies are gaining more and more interest and importance within the wind resource community. Benefits of such time series-based analysis being, among others, more accurate production and loss estimations, better representation of ramp-up and extreme events, and improvements in the dimensioning of energy storage systems. While mesoscale Numerical Weather Prediction (NWP) models can simulate long-term winds that capture non-stationary weather patterns, it is known that they are not able to properly resolve sub-scale processes leading to a smoothing effect. Recent studies presented spectral models that describe the full atmospheric spectrum of boundary layer winds which connect the microscale turbulent movements to the large, mesoscale fluctuations. In this work, mesoscale simulations from the Weather Research and Forecasting (WRF) model are coupled with stochastic turbulence simulations using state-of-the-art full-scale boundary-layer spectra to efficiently bridge the spectral gap between the mesoscale and the turbulence fluctuations, without requiring any local measurements nor expensive CFD simulations. The study provides a practical step-by-step approach to generate long wind speed time series at high sampling rate at any desired location and height.

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“…Svensson et al 2017, Smedman 1991. Over the North Sea, it was found that open cells can add an extra of 20 -50% to the turbulence intensity (Larsén et al 2019b).…”

Section: Offshore Wind Energy Activitiesmentioning

confidence: 99%

Natural Hazards and Extreme Events in the Baltic Sea region

Rutgersson

Kjellström

Haapala

et al. 2021

Preprint

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Abstract. A natural hazard is a naturally occurring extreme event with a negative effect on people and society or the environment. Natural hazards may have severe implications for human life and they can potentially generate economic losses and damage ecosystems. A better understanding of their major causes, probability of occurrence, and consequences enables society to be better prepared and to save human lives and to invest in adaptation options. Natural Hazards related to climate change are identified as one of the Grand Challenges in the Baltic Sea region. We here summarise existing knowledge of extreme events in the Baltic Sea region with the focus on past 200 years, as well as future climate scenarios. The events considered here are the major hydro-meteorological events in the region and include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. We also address some ecological extremes and implications of extreme events for society (phytoplankton blooms, forest fires, coastal flooding, offshore infrastructures, and shipping). Significant knowledge gaps are identified, including the response of large scale atmospheric circulation to climate change, but also concerning specific events, for example, occurrences of marine heat waves and small-scale variability of precipitation. Suggestions for future research includes further development of high-resolution Earth System models, and the potential use of methodologies for data analysis (statistical methods and machine learning). With respect to expected impact of climate change, changes are expected for sea-level, extreme precipitation, heat waves and phytoplankton blooms (increase) and cold spells and severe ice winters (decrease). For some extremes (drying, river flooding and extreme waves) the change depends on the area and time period studies.

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Turbulence Characteristics of Wind-Speed Fluctuations in the Presence of Open Cells: A Case Study

Cited by 10 publications

References 35 publications

Estimation of offshore extreme wind from wind‐wave coupled modeling

Estimation of offshore extreme wind from wind‐wave coupled modeling

Generating long-term sub-hourly wind speed time series by coupling mesoscale models with full-scale spectra

Natural Hazards and Extreme Events in the Baltic Sea region

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