The horizontal distribution of space correlation coefficients of wind fluctuations in the atmospheric surface layer

Hayashi, Taiichi

doi:10.1007/bf00119330

Cited by 6 publications

(8 citation statements)

References 14 publications

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“…The integral distance scale derived for the smaller-scale component is 300 m for the 1-min averaged data. This is close to the integral distance scale of 400 m derived by Hayashi (1991) for 1-min averaged data over a much smaller domain (200 m maximum station separations). Hayashi's network was too small to be able to detect the longer-scale component that was found to influence our observations.…”

Section: Statistics For Pairs Of Observing Stationsmentioning

confidence: 78%

“…At the other extreme, theoretical formulas based on spectral analyses can be applied to the problem (e.g., Pielke and Panofsky (1970), Perry et al (1978) and Panofsky and Dutton (1984)). Still other researchers are concerned with the horizontal distribution of spatial correlations over distances of 100 m or less (e.g., Hayashi, 1991). Brief reviews of these two types of studies are given below.…”

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

“…OBSERVATIONS AT SPATIAL SEPARATIONS OF 100 M OR LESS The variability of turbulence over distances of 100 m or less, for averaging times of 1 s and sampling times of 30 min, was studied by Hayashi (1991) using a gridded network of 28 cup anemometers. 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.…”

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

“…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.…”

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

“…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. Hayashi (1991) also derived integral length scales for longer averaging times, ranging up to 60 s, and found that the effective integral scale increased by a median factor of about five between the 1 and 60 s averaging times. The median integral length scale for the 60 s averages was about 400 m for the along-wind direction and 70 m for the cross-wind direction.…”

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

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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: Statistics For Pairs Of Observing Stationsmentioning

confidence: 78%

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

Section: Previous Studies Of Mesoscale Wind Variabilitymentioning

confidence: 99%

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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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Statistical Characteristics of Gusty Wind Conditionally Sampled with an Array of Ultrasonic Anemometers

Taniwaki

Sassa

Hayashi

et al. 2012

Springer Proceedings in Physics

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Gust and downward momentum transport in the atmospheric surface layer

Hayashi

1992

Boundary-Layer Meteorol

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The intermittent structure of the turbulent wind field in the atmospheric surface layer is investigated by conditional sampling methods. The results show that downward momentum can be transported efficiently during a peak gust. The horizontal structure of the gust is also revealed by observing the spatial structure of wind speed fluctuations from a network of 28 anemometers. The high wind region consists of a gust front with a sudden increase of wind speed at the front and a slow decrease to its rear. The development of the gust front is related closely to the momentum flux during the short time of passage of the gust.

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The horizontal distribution of space correlation coefficients of wind fluctuations in the atmospheric surface layer

Cited by 6 publications

References 14 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

Statistical Characteristics of Gusty Wind Conditionally Sampled with an Array of Ultrasonic Anemometers

Gust and downward momentum transport in the atmospheric surface layer

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