How Long Is Long Enough When Measuring Fluxes and Other Turbulence Statistics?

Lenschow, D. H.; Mann, Jakob; Kristensen, Leif

doi:10.1175/1520-0426(1994)011<0661:hlilew>2.0.co;2

Cited by 513 publications

(521 citation statements)

References 2 publications

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“…A crucial issue in the application of the eddy correlation technique is the choice of an appropriate averaging time, during which the mean wind is assumed to be steady (Panofsky and Dutton, 1984;Lenschow et al, 1994;Nakamura and Mahrt, 2006). As a first consequence of stationarity, when the averaging interval is further increased, variance and covariance values should not change after they have reached a steady value, implied by the supposed existence of an integral time-scale (Lee et al, 2004).…”

Section: Time Averagingmentioning

confidence: 99%

“…For the calculations of second-order moments, an averaging time of 30 min was set, following a relatively common choice in atmospheric turbulence studies (Lenschow et al, 1994;Berger et al, 2001;Sakai et al, 2001;Lee et al, 2004). Averaging intervals were centred at 00 and 30 min past each hour, which allowed a comparison between mean values of various quantities (wind speed and direction and temperature) and those obtained at the conventional weather station.…”

Section: Time Averagingmentioning

confidence: 99%

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Analysis of second‐order moments in surface layer turbulence in an Alpine valley

Franceschi

Zardi

Tagliazucca

et al. 2009

Quart J Royal Meteoro Soc

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ABSTRACT:The paper presents the analysis of field measurements in the atmospheric surface layer over the floor of the Adige Valley, near the city of Bolzano in the Alps. Turbulence quantities, such as drag coefficient, displacement height and roughness length, appear similar to those reported in the literature concerning surface-layer turbulence over flat uniform terrain. The analysis of the non-dimensional standard deviations (σ u , σ v , σ w ) legitimates the adoption, for all the wind components, of the same Monin-Obukhov similarity relationship in the form σ i /u * = α i (1 + β i |ζ |) 1/3 , originally proposed only for flat uniform terrain under steady state conditions, and the extension of this expression to the case of winds over a valley floor in slowly varying situations. The coefficients α i are very similar for along-valley and cross-valley winds, as are the β i ones. Moreover the α u values are only slightly larger than the α v , contrary to what is reported in the literature. Conversely, σ θ /θ * shows distinctly different behaviour in the stable and unstable regimes. In particular, in the latter case a large scatter in the data is observed. However, since the most scattered data turn out to occur in transition periods (i.e. sunrise and sunset), after a specific data selection the best-fit parameters are more accurately estimated. In addition, emphasis is laid on the relevance of an appropriate formulation and use of a suitable recursive filter to separate the low-frequency unsteadiness of the mean flow from the turbulence signal.

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Section: Time Averagingmentioning

confidence: 99%

Section: Time Averagingmentioning

confidence: 99%

Analysis of second‐order moments in surface layer turbulence in an Alpine valley

Franceschi

Zardi

Tagliazucca

et al. 2009

Quart J Royal Meteoro Soc

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“…Thus again the stability-induced long-term mean shift in geostrophic friction velocity needs to be treated (i.e. stable conditions affect u * for a given G), meaning the normalized dimensionless profile kU (z)/[u * 0 ln(z/z0)] will need to be multiplied by a factor (1 + ∆u * off /u * 0); since in practice the perturbation (aGH off /f G 2 , as seen in eq.15) is much smaller than 1, and because the the other normalized perturbations are relatively small, for the new 'tall' profile we approximate by adding aGH off /f G 2 to p T in (17). Figure 1 shows the correction factor (17) as a function of target height, i.e.…”

Section: Adaptation For Use With Geostrophic Drag-lawmentioning

confidence: 99%

“…One can see from Figure 1 how the new tall model diverges from the EWA model above ∼150 m; this is due to the 'tall profile' accounting for the effect of the ABL depth, and it prevents the new model from over-predicting winds far above the surface layer. Profile of mean wind speed correction factor (dimensionless wind profile) cf1(z) for source and target sites, via EWA (magenta) and new tall (blue/dashed) model (17). Observation height is zsrc =40 m and corresponding roughness length is z0,src =3 cm; here with typical stability statistics {σ±, n+, h eff } and suggested EWA parameters {H off , Hrms}.…”

Section: Adaptation For Use With Geostrophic Drag-lawmentioning

confidence: 99%

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Probabilistic stability and ‘tall’ wind profiles: theory and method for use in wind resource assessment

Kelly¹,

Troen²

2015

Wind Energy

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A model has been derived for calculating the effects of stability and the finite height of the planetary boundary layer upon the long-term mean wind profile. A practical implementation of this probabilistic extended similarity-theory model is made, including its incorporation within the European Wind Atlas (EWA) methodology for site-to-site application. Theoretical and practical implications of the EWA methodology are also derived and described, including unprecedented documentation of the theoretical framework encompassing vertical extrapolation, as well as some improvement to the methodology. Results of the modelling are shown for a number of sites, with discussion of the models' efficacy and the relative improvement shown by the new model, for situations where a user lacks local heat flux information, as well as performance of the new model using measured flux statistics. Further, the uncertainty in vertical extrapolation is characterized for the EWA model contained in standard (i.e. WAsP) wind resource assessment, as well as for the new model.

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Quantified turbulent diffusion of suspended sediment using acoustic Doppler current profilers

Sassi

Hoitink

Vermeulen

2013

Geophysical Research Letters

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[1] Collocated profiles of the Reynolds stress tensor and eddy covariance fluxes are obtained to derive vertical profiles of turbulent momentum and sediment diffusivity in a tidal river, using coupled acoustic Doppler current profilers (ADCPs). Shear and normal stresses are obtained by combining the variances in radial velocities measured by the ADCP beams. The covariances between radial velocities and calibrated acoustic backscatter allow the determination of the three Cartesian components of the turbulent flux of suspended sediment. The main advantage of this new approach is that flow velocity and sediment concentration measurements are exactly collocated, and allowing for profiling over longer ranges, in comparison to existing techniques. Results show that vertical profiles of the inverse turbulent PrandtlSchmidt number are coherent with corresponding profiles of the sediment diffusivity, rather than with profiles of the eddy viscosity. Citation: Sassi, M. G., A. J. F. Hoitink, and B.Vermeulen (2013), Quantified turbulent diffusion of suspended sediment using acoustic Doppler current profilers, Geophys. Res. Lett.,

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How Long Is Long Enough When Measuring Fluxes and Other Turbulence Statistics?

Cited by 513 publications

References 2 publications

Analysis of second‐order moments in surface layer turbulence in an Alpine valley

Analysis of second‐order moments in surface layer turbulence in an Alpine valley

Probabilistic stability and ‘tall’ wind profiles: theory and method for use in wind resource assessment

Quantified turbulent diffusion of suspended sediment using acoustic Doppler current profilers

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