Spectra in the Unstable Surface Layer

Peltier, L. J.; Wyngaard, J. C.; Khanna, Samir; Brasseur, JL

doi:10.1175/1520-0469(1996)053<0049:situsl>2.0.co;2

Cited by 66 publications

(60 citation statements)

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“…Wijesekera et al [2001] examined horizontal wave number spectra of temperature in the unstably stratified oceanic surface layer. Wijesekera et al showed that when spectra were normalized according to M-O similarity theory, the shapes of the oceanic spectra agree qualitatively with observed [e.g., Kaimal et al, 1972] and modeled [Peltier et al, 1996] atmospheric spectra including the wavelength of peaks and the variation of peak wave number with stability. However, spectral levels in the energy containing range differed by as much as a factor of two.…”

Section: Introductionmentioning

confidence: 62%

“…[7] Following Peltier et al [1996], we model the twodimensional spectrum of a conservative scalar (e.g., temperature or salinity) with the form…”

Section: Spectral Modelmentioning

confidence: 99%

“…The Kansas experiment revealed horizontal wave number properties of scalar and wind fields; when normalized in accordance with Monin-Obukhov (M-O) similarity theory, Kansas spectra of velocity and temperature reduce to a family of curves that depend on the M-O stability parameter at low wave number and reduce to a single universal curve in the inertial subrange [Kaimal et al, 1972]. Several approaches have been used to model surface layer spectra [e.g., Hojstrup, 1982;Kristensen et al, 1989;Mann, 1994;Peltier et al, 1996]. Peltier et al [1996] modeled spectra in the unstably stratified atmospheric surface layer by assuming horizontal isotropy in the horizontal directions.…”

Section: Introductionmentioning

confidence: 99%

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Scaled temperature spectrum in the unstable oceanic surface layer

2004

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[1] A nondimensional form of the temperature spectrum in a convective near-surface layer was derived empirically as a function of stability parameter x = z/L and surface wave parameter g = u * /(gz) 1/2 , under the assumption of horizontal isotropy, where z is the depth of the measurement, L is the Monin-Obukhov length scale, u * is the surface friction velocity, and g is the acceleration due to gravity. The wave number-weighted, onedimensional spectrum had +1 slope at low wave numbers and À2/3 slope at high wave numbers (characteristic of an inertial subrange). Spectral levels in the À2/3 range varied with g, and the spectrum width was a function of x. The wavelength of the spectral peak decreased as Àx (>0) increased. The variation of spectral level in the inertial subrange suggested that dissipation due to wave breaking was enhanced by a factor of 1.7 at 2-m depth for a wind speed of 10 m s À1 . Root-mean-square temperature fluctuations at 2-m depth versus Àx were in excellent agreement with atmospheric surface layer observations and agreed moderately well (within 30%) with the results of large-eddy simulation experiments. Root-mean-square fluctuations were proportional to (Àx) À1/3 for Àx > 0.4, consistent with the predictions of similarity theory. The skewness of temperature fluctuations varied with Àx, qualitatively similar to variation in the atmospheric surface layer. The skewness of the horizontal gradient of temperature at 2-m depth varied relative to the wind direction and was well described by a cosine function with amplitude 0.6.

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Section: Introductionmentioning

confidence: 62%

“…[7] Following Peltier et al [1996], we model the twodimensional spectrum of a conservative scalar (e.g., temperature or salinity) with the form…”

Section: Spectral Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scaled temperature spectrum in the unstable oceanic surface layer

2004

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“…It has been suggested that Taylor's hypothesis should be valid when the turbulence intensity is small compared to the average wind speed (Wyngaard and Clifford, 1977;Stull, 1988;Peltier et al, 1996) and this criterion has been applied successfully in several studies (e.g., Kiely et al, 1996;Albertson et al, 1997Tong et al, 1998Tong et al, , 1999Porté-Agel et al, 1998, 2000, 2001. Data files with T I > 0.5 have been rejected to satisfy the requirement for the applicability of Taylor's hypothesis.…”

Section: Turbulence Statisticsmentioning

confidence: 99%

On Monin–Obukhov Similarity In The Stable Atmospheric Boundary Layer

Pahlow

Parlange

Porté‐Agel

2001

Boundary-Layer Meteorology

217

150

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Abstract. Atmospheric measurements from several field experiments have been combined to develop a better understanding of the turbulence structure of the stable atmospheric boundary layer. Fast response wind velocity and temperature data have been recorded using 3-dimensional sonic anemometers, placed at several heights (≈1 m to 4.3 m) above the ground. The measurements were used to calculate the standard deviations of the three components of the wind velocity, temperature, turbulent kinetic energy (TKE) dissipation and temperature variance dissipation. These data were normalized and plotted according to Monin-Obukhov similarity theory. The non-dimensional turbulence statistics have been computed, in part, to investigate the general applicability of the concept of z-less stratification for stable conditions. From the analysis of a data set covering almost five orders of magnitude in the stability parameter ζ = z/L (from near-neutral to very stable atmospheric stability), it was found that this concept does not hold in general. It was only for the non-dimensional standard deviation of temperature and the average dissipation rate of turbulent kinetic energy that zless behaviour has been found. The other variables studied here (non-dimensional standard deviations of u, v, and w velocity components and dissipation of temperature variance) did not follow the concept of z-less stratification for the very stable atmospheric boundary layer. An imbalance between production and dissipation of TKE was found for the near-neutral limit approached from the stable regime, which matches with previous results for near-neutral stability approached from the unstable regime.

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“…Fourier analysis in two spatial directions rather than three is generally more useful in analysis of large-eddy simulation of horizontally homogeneous atmospheric boundary layers, as discussed in detail by Peltier et al [1996]. We shall follow their development but for a vertical plane rather than a horizontal one.…”

Section: Discussionmentioning

confidence: 99%

Electromagnetic wave propagation through simulated atmospheric refractivity fields

Gilbert¹,

Xiao²,

Khanna³

et al. 1999

Radio Science

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Abstract. Large-eddy simulation (LES) provides three-dimensional, time-dependent fields of turbulent refractivity in the atmospheric boundary layer on spatial scales down to a few tens of meters. These fields are directly applicable to the computation of electromagnetic (EM) wave propagation in the megahertz range but not in the gigahertz range. We present an approximate technique for extending LES refractivity fields to the smaller scales needed for calculating EM propagation at gigahertz frequencies. We demonstrate the technique by computing refractivity fields through 128 3 LES, extending them to smaller scales in two dimensions, and using them in a parabolic equation EM propagation model. At 0.263 GHz the very small scale structure in the extended fields has a negligible effect on the predicted EM levels. At 2 GHz, however, it increases the predicted levels by 15-25 dB. We relate these results to the refractivity structure sampled by EM waves at 0.263 and 2 GHz. We also show that at long range an EM field calculated through an LES-based refractivity field is generally less coherent and significantly weaker than one computed from a "plywood" (i.e., stratified, range-independent) model of the small-scale refractivity field. We give a physical explanation for the differences in the EM fields computed in these two ways. Finally, although the plywood model gives results that fit the EM levels observed in the recent Variability of Coastal Atmospheric Refractivity (VOCAR) experiment, it is not physically realistic. The instantaneous top of the atmospheric boundary layer is known to be sharp and horizontally varying, and we show that using such a top also yields a fit to the VOCAR data. IntroductionThe research discussed here is an interdisciplinary effort between boundary-layer meteorologists and physicists working in electromagnetic (EM) propagation. The purpose of the collaboration was to use large-eddy simulation (LES) methods and a parabolic equation ( Large-Eddy SimulationTurbulence is characterized by three-dimensional, stochastic motions (eddies) of a wide range of spatial scales. These motions generate corresponding stochastic structure in advected scalars such as refractive index. The largest eddies in the turbulent atmospheric boundary layer scale with the integral scale, which is of the order of the boundary-layer depth zi (-1 km). The smallest eddies scale with the Kolmogorov microscale, which is typically of the order of 1 mm. A direct numerical solution of the governing equations for such a turbulent flow is far beyond the present or expected capabilities of computers. 1413

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Spectra in the Unstable Surface Layer

Cited by 66 publications

References 0 publications

Scaled temperature spectrum in the unstable oceanic surface layer

Scaled temperature spectrum in the unstable oceanic surface layer

On Monin–Obukhov Similarity In The Stable Atmospheric Boundary Layer

Electromagnetic wave propagation through simulated atmospheric refractivity fields

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