A Model for Turbulence Spectra in the Equilibrium Range of the Stable Atmospheric Boundary Layer

Cheng, Yizong; Li, Qi; Argentini, Stefania; Sayde, Chadi; Gentine, Pierre

doi:10.1029/2019jd032191

Cited by 11 publications

(8 citation statements)

References 127 publications

(374 reference statements)

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“…Here, this factor is justified by the fact that is not a wavelength, but a scale, and that = (2 ⁄ ) = 1/ . However, the results discussed here are not strongly dependent on this , where is a velocity scale and L the corresponding horizontal velocity scale) consisting of quasi-horizontal turbulent motions may lead to the presence of inertial subranges in horizontal spectra at scales larger than the Ozmidov scale [37][38]. In stable boundary layers, dynamical processes strongly affected by the stable stratification can lead to various regimes producing −5/3 or shallower slopes at scales larger or smaller than the Ozmidov…”

Section: Attempt At Interpretation Of the −5/3 Subranges In Terms Of mentioning

confidence: 76%

“…Stratified turbulence at low Froude number (F r = σ U /NL 1, where σ U is a velocity scale and L the corresponding horizontal velocity scale) consisting of quasi-horizontal turbulent motions may lead to the presence of inertial subranges in horizontal spectra at scales larger than the Ozmidov scale [37,38]. In stable boundary layers, dynamical processes strongly affected by the stable stratification can lead to various regimes producing −5/3 or shallower slopes at scales larger or smaller than the Ozmidov scale [14,38]. The identification of −5/3 domain associated with non-Kolmogorov regime may lead to erroneous estimates of ε and C 2…”

Section: Attempt At Interpretation Of the −5/3 Subranges In Terms Of mentioning

confidence: 99%

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Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data

et al. 2019

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Turbulence parameters in the lower troposphere (up to ~4.5 km) are estimated from measurements of high-resolution and fast-response cold-wire temperature and Pitot tube velocity from sensors onboard DataHawk Unmanned Aerial Vehicles (UAVs) operated at the Shigaraki Middle and Upper atmosphere (MU) Observatory during two ShUREX (Shigaraki UAV Radar Experiment) campaigns in 2016 and 2017. The practical processing methods used for estimating turbulence kinetic energy dissipation rate ε and temperature structure function parameter C T 2 from one-dimensional wind and temperature frequency spectra are first described in detail. Both are based on the identification of inertial (−5/3) subranges in respective spectra. Using a formulation relating ε and C T 2 valid for Kolmogorov turbulence in steady state, the flux Richardson number R f and the mixing efficiency χ m are then estimated. The statistical analysis confirms the variability of R f and χ m around ~ 0.13 − 0.14 and ~ 0.16 − 0.17 , respectively, values close to the canonical values found from some earlier experimental and theoretical studies of both the atmosphere and the oceans. The relevance of the interpretation of the inertial subranges in terms of Kolmogorov turbulence is confirmed by assessing the consistency of additional parameters, the Ozmidov length scale L O , the buoyancy Reynolds number R e b , and the gradient Richardson number Ri. Finally, a case study is presented showing altitude differences between the peaks of N 2 , C T 2 and ε , suggesting turbulent stirring at the margin of a stable temperature gradient sheet. The possible contribution of this sheet and layer structure on clear air radar backscattering mechanisms is examined.

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Section: Attempt At Interpretation Of the −5/3 Subranges In Terms Of mentioning

confidence: 76%

Section: Attempt At Interpretation Of the −5/3 Subranges In Terms Of mentioning

confidence: 99%

Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data

et al. 2019

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“…The opinion in this paper was supported by various studies of direct numerical simulations. In a recent study, Cheng et al [75] presented a comprehensive discussion about the spectral shape in the stable ABL and proposed a new hypothesis for stable ABL. The inertial subrange in the equilibrium range could be separated by a larger scale k b (buoyancy wave number) and smaller scale k o (or an antitropic scale k a ) into three regions: buoyancy subrange, a transition region and the isotropic inertial subrange.…”

Section: Spectral Analysismentioning

confidence: 99%

Atmospheric Disturbance Modelling for a Piloted Flight Simulation Study of Airplane Safety Envelope over Complex Terrain

et al. 2022

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A concept of a new energy management system synthesizing meteorological and orographic influences on airplane safety envelope was developed and implemented at the ZHAW Centre for Aviation. A corresponding flight simulation environment was built in a Research and Didactics Simulator (ReDSim) to test the first implementation of the cockpit display system. A series of pilot-in-the-loop flight simulations were carried out with a group of pilots. A general aviation airplane model Piper PA-28 was modified for the study. The environment model in the ReDSim was modified to include a new ad hoc subsystem simulating atmospheric disturbance. In order to generate highly resolved wind fields in the ReDsim, a well-established large-eddy simulation model, the Parallelized Large-Eddy Simulation (PALM) framework, was used in the concept study, focusing on a small mountainous region in Switzerland, not far from Samedan. For a more realistic representation of specific meteorological situations, PALM was driven with boundary conditions extracted from the COSMO-1 reanalysis of MeteoSwiss. The essential variables (wind components, temperature, and pressure) were extracted from the PALM output and fed into the subsystem after interpolation to obtain the values at any instant and any aircraft position. Within this subsystem, it is also possible to generate statistical atmospheric turbulence based on the widely used Dryden turbulence model. The paper compares two ways of generating atmospheric turbulence, by combining the numerical method with the statistical model and introduces the flight test procedure with an emphasis on turbulence realism; it then presents the experiment results including a statistical assessment achieved by collecting pilot feedback on turbulence characteristics and turbulence/task combination.

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“…To understand the role of the molecular dissipation and the transfer of kinetic energy across scales, we look at the spectral representation of the TKE equation for horizontally homogeneous turbulence (see e.g., [4,34]):…”

Section: Spectral Tke Equationmentioning

confidence: 99%

“…Alternatively, the spectral form of TKE equation [4,33,34] can be used to estimate the turbulence length scale. The length scale is then usually associated with TKE loss via molecular dissipation and can be computed from the dissipation rate and the spectrally integrated TKE [15].…”

Section: Introductionmentioning

confidence: 99%

A Budget-Based Turbulence Length Scale Diagnostic

2020

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The most frequently used boundary-layer turbulence parameterization in numerical weather prediction (NWP) models are turbulence kinetic energy (TKE) based-based schemes. However, these parameterizations suffer from a potential weakness, namely the strong dependence on an ad-hoc quantity, the so-called turbulence length scale. The physical interpretation of the turbulence length scale is difficult and hence it cannot be directly related to measurements or large eddy simulation (LES) data. Consequently, formulations for the turbulence length scale in basically all TKE schemes are based on simplified assumptions and are model-dependent. A good reference for the independent evaluation of the turbulence length scale expression for NWP modeling is missing. Here we propose a new turbulence length scale diagnostic which can be used in the gray zone of turbulence without modifying the underlying TKE turbulence scheme. The new diagnostic is based on the TKE budget: The core idea is to encapsulate the sum of the molecular dissipation and the cross-scale TKE transfer into an effective dissipation, and associate it with the new turbulence length scale. This effective dissipation can then be calculated as a residuum in the TKE budget equation (for horizontal sub-domains of different sizes) using LES data. Estimation of the scale dependence of the diagnosed turbulence length scale using this novel method is presented for several idealized cases.

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A Model for Turbulence Spectra in the Equilibrium Range of the Stable Atmospheric Boundary Layer

Cited by 11 publications

References 127 publications

Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data

Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data

Atmospheric Disturbance Modelling for a Piloted Flight Simulation Study of Airplane Safety Envelope over Complex Terrain

A Budget-Based Turbulence Length Scale Diagnostic

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