Structure of mean winds and turbulence in the planetary boundary layer over rural terrain

Teunissen, H.W.

doi:10.1007/bf00117220

Cited by 92 publications

(46 citation statements)

References 22 publications

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“…The average separation of the anemometers on this grid was about 20 m. Assuming an exponential shape for the spatial correlation coefficient between wind speeds, Hayashi (1991) derived integral distance scales of 75 m and 25 m for along-wind and cross-wind directions, respectively. Teunissen (1980) derived similar values of the integral scales in an independent experiment. In general, the correlation coefficients between the winds at a given location and the winds at the center of Hayashi's network had dropped to about 0.3 to 0.5 at the edge of the network.…”

Section: Previous Studies Of Mesoscale Wind Variabilitysupporting

confidence: 49%

Representativeness of wind measurements on a mesoscale grid with station separations of 312 m to 10 km

Hanna

Chang

1992

Boundary-Layer Meteorol

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A field experiment was carried out in which wind speed and direction were measured over flat terrain at a height of 10m using 13 identical instruments spaced logarithmically along two perpendicular 10 km lines. Station separations ranged from 312 m to 10 km. One-minute data from 11 sampling periods of duration 6 to 10 h were studied.The statistics showed little dependence on whether the line of instruments was oriented along the wind or across the wind. The correlation coefficients between wind fluctuations at two stations separated by distance AX were found to vary exponentially with AX, with an integral distance scale on the order of 1 km. The integral time scale derived from the variation of the single station variances with averaging time was found to equal several minutes. At a station separation of 10 km, the correlation coefficients between the wind components at the two sites were calculated to be 0.24, 0.37, and 0.47 for averaging times of 1, 10, and 60min, respectively. These values for the correlation coefficients correspond to root-mean-square differences in wind speed at the two stations of about 1.3, 1.0, and 0.7m/s, respectively.Exponential formulas based on dimensional analysis are suggested for fitting these observations. It is found that the observations of spatial correlations are best fit if two independent integral distance scales are used -a boundary-layer distance scale of about 300m that best applies to small station separations and a mesoscale distance scale of about 10 km that applies to larger station separations. ObjectivesIn order to use wind observations to estimate atmospheric transport and dispersion, it is necessary to assume that the wind measurements at one site represent the wind flow at a nearby site. The separation between these two sites can range from short distances (10 m) to large distances (100 km). The assumption of spatial homogeneity of the wind field is sometimes quite good, but situations often occur where wind direction observations differ by 180" between two towers in the same mesoscale network. There are a few limited studies of wind variability over mesoscale distances (e.g., Perry et al. (1978), Lockhart and Irwin (1980), Hanna (1982), and Panofsky and Dutton (1984), but much more theoretical and observational work is needed.This study is part of a comprehensive research program in which the contributions of meteorological uncertainties to errors in air quality modeling and source emission estimation are being investigated. A cooperative two-week field experiment was conducted in which 13 wind instruments were set up on two perpendicular lines along an "L-shaped" pattern with maximum station separation of 10 km. Variances and spatial correlations of wind speeds and directions are calculated, Boundary-Layer Meteorology 60: 309-324, 1992.

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Section: Previous Studies Of Mesoscale Wind Variabilitysupporting

confidence: 49%

Representativeness of wind measurements on a mesoscale grid with station separations of 312 m to 10 km

Hanna

Chang

1992

Boundary-Layer Meteorol

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“…where v = w = 0 m·s −1 [19]. Unless the scanning beam is perpendicular to the flow, the mean velocity of the along-beam component verifies v r = 0 m·s −1 .…”

Section: Data Processingmentioning

confidence: 87%

Measurements of Surface-Layer Turbulence in a Wide Norwegian Fjord Using Synchronized Long-Range Doppler Wind Lidars

et al. 2017

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Three synchronized pulsed Doppler wind lidars were deployed from May 2016 to June 2016 on the shores of a wide Norwegian fjord called Bjørnafjord to study the wind characteristics at the proposed location of a planned bridge. The purpose was to investigate the potential of using lidars to gather information on turbulence characteristics in the middle of a wide fjord. The study includes the analysis of the single-point and two-point statistics of wind turbulence, which are of major interest to estimate dynamic wind loads on structures. The horizontal wind components were measured by the intersecting scanning beams, along a line located 25 m above the sea surface, at scanning distances up to 4.6 km. For a mean wind velocity above 8 m·s −1 , the recorded turbulence intensity was below 0.06 on average. Even though the along-beam spatial averaging leads to an underestimated turbulence intensity, such a value indicates a roughness length much lower than provided in the European standard EN 1991-1-4:2005. The normalized spectrum of the along-wind component was compared to the one provided by the Norwegian Petroleum Industry Standard and the Norwegian Handbook for bridge design N400. A good overall agreement was observed for wave-numbers below 0.02 m −1 . The along-beam spatial averaging in the adopted set-up prevented a more detailed comparison at larger wave-numbers, which challenges the study of wind turbulence at scanning distances of several kilometres. The results presented illustrate the need to complement lidar data with point-measurement to reduce the uncertainties linked to the atmospheric stability and the spatial averaging of the lidar probe volume. The measured lateral coherence was associated with a decay coefficient larger than expected for the along-wind component, with a value around 21 for a mean wind velocity bounded between 10 m·s −1 and 14 m·s −1 , which may be related to a stable atmospheric stratification.

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“…The integral scale can be defined in several ways: the larger scale of the flow, or the scale above which the Fourier transform has a slope inferior to a −5/3 slope, such as where the turbulent kinetic energy (TKE) is maximum. Micrometeorological studies have found integral scales varying in a huge range, from around 100 to 1000 m (Teunissen, 1980;Kaimal and Finnigan, 1994).…”

Section: Introductionmentioning

confidence: 99%

Influence of atmospheric stratification on the integral scale and fractal dimension of turbulent flows

Tijera

Maqueda

Yagüe

2016

Nonlin. Processes Geophys.

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Abstract. In this work the relation between integral scale and fractal dimension and the type of stratification in fully developed turbulence is analyzed. The integral scale corresponds to that in which energy from larger scales is incoming into a turbulent regime. One of the aims of this study is the understanding of the relation between the integral scale and the bulk Richardson number, which is one of the most widely used indicators of stability close to the ground in atmospheric studies. This parameter will allow us to verify the influence of the degree of stratification over the integral scale of the turbulent flows in the atmospheric boundary layer (ABL). The influence of the diurnal and night cycles on the relationship between the fractal dimension and integral scale is also analyzed. The fractal dimension of wind components is a turbulent flow characteristic, as has been shown in previous works, where its relation to stability was highlighted. Fractal dimension and integral scale of the horizontal (u ) and vertical (w ) velocity fluctuations have been calculated using the mean wind direction as a framework. The scales are obtained using sonic anemometer data from three elevations 5.8, 13 and 32 m above the ground measured during the SABLES 98 field campaign (Cuxart et al., 2000). In order to estimate the integral scales, a method that combines the normalized autocorrelation function and the best Gaussian fit (R 2 ≥ 0.70) has been developed. Finally, by comparing, at the same height, the scales of u and w velocity components, it is found that the turbulent flows are almost always anisotropic.

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Structure of mean winds and turbulence in the planetary boundary layer over rural terrain

Cited by 92 publications

References 22 publications

Representativeness of wind measurements on a mesoscale grid with station separations of 312 m to 10 km

Representativeness of wind measurements on a mesoscale grid with station separations of 312 m to 10 km

Measurements of Surface-Layer Turbulence in a Wide Norwegian Fjord Using Synchronized Long-Range Doppler Wind Lidars

Influence of atmospheric stratification on the integral scale and fractal dimension of turbulent flows

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