Statistical analysis and stochastic modelling of boundary layer wind speed

Laubrich, T.

doi:10.1140/epjst/e2009-01100-1

Cited by 8 publications

(5 citation statements)

References 18 publications

(22 reference statements)

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“…This type of p.d.f. has been widely used in the analysis of atmospheric turbulence (Beck et al 2005;Böttcher et al 2007;Laubrich and Kantz 2009;Liu and Hu 2013).…”

Section: Probability Density Functionmentioning

confidence: 99%

A Multifractal Random-Walk Description of Atmospheric Turbulence: Small-Scale Multiscaling, Long-Tail Distribution, and Intermittency

Liu

Huang

2019

Boundary-Layer Meteorol

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The prevalent multifractal characteristics of turbulent velocity fluctuations in the atmosphere are important for estimating various wind effects in wind engineering. Here, the multifractal characteristics of turbulent velocity fluctuations, including the small-scale multiscaling, the long-tail distributions and the intermittency, are thoroughly investigated by using a highfrequency dataset of three-dimensional velocities (100 Hz) collected at three levels during one month. To reduce uncertainties in the estimate of multiscaling exponents, a new method, the sequential extended self-similarity, is proposed. Based on this method, we obtain the multiscaling exponents of qth-order moments of velocity increments as a function of q, that is the so-called multifractal spectrum. The multifractal random walk (MRW) model is then shown to describe the various multifractal spectra of turbulent velocity fluctuations. With the help of this model, we find a connection between the small-scale multiscaling and the long-tail distributions, which is generally observed in our dataset, again validating the MRW model. A non-linear multifractal spectrum is commonly considered to be related to the intermittency of turbulent velocity fluctuations at small scales and its curvature is usually used as a quantification of intermittency intensity. However, we suggest that models capturing the nonlinear multifractal spectrum may fail to represent the long-tail distribution, which is a more direct quantification of intermittency. Finally, qualitative variations of validated indicators with specific boundary-layer parameters are investigated. Results show that the intermittency of turbulent velocity fluctuations is more relevant to the friction velocity, compared with the average wind speed, the average temperature, and the surface-layer stability.

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“…This type of p.d.f. has been widely used in the analysis of atmospheric turbulence (Beck et al 2005;Böttcher et al 2007;Laubrich and Kantz 2009;Liu and Hu 2013).…”

Section: Probability Density Functionmentioning

confidence: 99%

A Multifractal Random-Walk Description of Atmospheric Turbulence: Small-Scale Multiscaling, Long-Tail Distribution, and Intermittency

Liu

Huang

2019

Boundary-Layer Meteorol

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“…They also address trends in extreme values and introduce a new approach to test if trends in extremes result from the trends in the mean and variance. Laubrich and Kantz [37] also find a clear association between mean and variance, in this case in atmospheric boundary layer wind speed. They describe in detail the wind speed statistics and provide a stochastic model for wind speed simulation based on a geometric autoregressive process.…”

Section: Trends Cycles and Extremes In Present-day Climatementioning

confidence: 57%

“…This enables them to distinguish abrupt transitions of intermittent structures observed there. Laubrich and Kantz [37] develop a stochastic modelling approach which mainly focuses on the increment statistics of turbulence. They demonstrate the strong potential of their approach by analysing boundary layer wind speed which is highly non-stationary.…”

Section: Dynamical Processes In Atmosphere and Oceanmentioning

confidence: 99%

Understanding the Earth as a Complex System – recent advances in data analysis and modelling in Earth sciences

Donner

Barbosa

Kurths

et al. 2009

Eur. Phys. J. Spec. Top.

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Abstract. This topical issue collects contributions exemplifying the recent scientific progress in the development and application of data analysis methods and conceptual modelling for understanding the dynamics of the Earth as a complex dynamical system. The individual papers focus on different questions of presentday interest in Earth sciences and sustainability, which are often of paramount importance for mankind (recent and future climate change, occurrence of natural hazards, etc.). This editorial shall motivate the link between the different contributions from both topical and methodological perspectives. The holistic view on the Earth as a complex system is important for identifying mutual links between the individual subsystems and hence for improving the physical understanding of how these components interact with each other on various temporal as well as spatial scales and how the corresponding interactions determine the dynamics of the full system.

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“…A number of authors have commented on the lack of stationarity in wind speeds at various timescales, and this presents a particular challenge when attempting to parameterize quantities such as mean wind speeds, turbulent time and length scales, etc. Figures and lend support to this hypothesis where it is seen that there is variance over a large range of timescales.…”

Section: An Overview Of Different Scales Of Variabilitymentioning

confidence: 99%

Quantifying the variability of wind energy

Watson

2013

WIREs Energy & Environment

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Citation: WATSON, S.J., 2014. Quantifying the variability of wind energy.Wiley Interdisciplinary Reviews: Energy and Environment, 3 (4), pp. 330 - 342.Additional Information:• This is the pre-peer reviewed version of the following article: WATSON, J., 2014. Quantifying the variability of wind energy. Wiley Interdisci- AbstractWind by its very nature is a variable element. Its variation is different on different timescales and spatially its magnitude can change dramatically depending on local climatology and terrain. This has implications in a variety of sectors, not least the wind energy sector. The accuracy of weather forecasting models has increased significantly in the last few decades and these models are able to give an insight into variability on the hourly and daily timescales. On shorter timescales, predicting chaotic turbulent fluctuations is far more challenging. Similarly, the ability to make seasonal forecasts is extremely limited. General Circulation Models (GCMs) can give insights into possible future decadal fluctuations but there are still large uncertainties. Observational data can give useful information concerning variation on a variety of timescales but data quality and spatial coverage can be variable. An understanding of local scale spatial variations in wind is extremely important in wind farm siting. In the last 40 years, there have been significant advances in predicting these variations using computer models, though there remain significant challenges in understanding the behaviour of the wind in certain environments. Both the spatial and temporal variations of wind are important considerations when wind power is integrated into electricity networks and this will become an ever more important consideration as wind generation makes an increasing contribution to our global energy needs.

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Statistical analysis and stochastic modelling of boundary layer wind speed

Cited by 8 publications

References 18 publications

A Multifractal Random-Walk Description of Atmospheric Turbulence: Small-Scale Multiscaling, Long-Tail Distribution, and Intermittency

A Multifractal Random-Walk Description of Atmospheric Turbulence: Small-Scale Multiscaling, Long-Tail Distribution, and Intermittency

Understanding the Earth as a Complex System – recent advances in data analysis and modelling in Earth sciences

Quantifying the variability of wind energy

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