Estimating Turbulence Kinetic Energy Dissipation Rates in the Numerically Simulated Stratocumulus Cloud-Top Mixing Layer: Evaluation of Different Methods

Akinlabi, Emmanuel O.; Wacławczyk, Marta; Mellado, Juan Pedro; Malinowski, Szymon P.

doi:10.1175/jas-d-18-0146.1

Cited by 25 publications

(30 citation statements)

References 43 publications

(72 reference statements)

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“…We expect this result follows from the presence of external intermittency (laminar regions will give zero or close to zero local values of d). Even in the in-cloud region the volume fraction occupied by turbulent flow is smaller than one and equals approximately γ = 0.9 − 0.95, where γ is the intermittency parameter, see [60]. Due to small values of γ in the outer-cloud regions, the average value of d also decreases therein to, approximately 0.25, see Fig.…”

Section: Dns Of Stratocumulus-top Mixing Layermentioning

confidence: 93%

Fractal Reconstruction of Sub-Grid Scales for Large Eddy Simulation

Akinlabi

Wacławczyk

Malinowski

et al. 2019

Flow Turbulence Combust

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In this work, the reconstruction of sub-grid scales in large eddy simulation (LES) of turbulent flows in stratocumulus clouds is addressed. The approach is based on the fractality assumption of turbulent velocity field. The fractal model reconstructs sub-grid velocity field from known filtered values on LES grid, by means of fractal interpolation, proposed by Scotti and Meneveau (Physica D 127, 198-232 1999). The characteristics of the reconstructed signal depend on the stretching parameter d, which is related to the fractal dimension of the signal. In many previous studies, the stretching parameter values were assumed to be constant in space and time. To improve the fractal interpolation approach, we account for the stretching parameter variability. The local stretching parameter is calculated from direct numerical simulation (DNS) data with an algorithm proposed by Mazel and Hayes (IEEE Trans. Signal Process 40(7), [1724][1725][1726][1727][1728][1729][1730][1731][1732][1733][1734] 1992), and its probability density function (PDF) is determined. It is found that the PDFs of d have a universal form when the velocity field is filtered to wave-numbers within the inertial range. In order to investigate Reynolds number (Re) dependence, we compare the inertial-range PDFs of d in DNS and large eddy simulation (LES) of stratocumulus cloud-top and experimental airborne data from Physics of Stratocumulus Top (POST) research campaign. Next, fractal reconstruction of the subgrid velocity is performed and energy spectra and statistics of velocity increments are compared with DNS data. It is assumed that the stretching parameter d is a random variable with the prescribed PDF. We show that the agreement with the DNS is in such case better and the error in mass conservation is smaller compared to the use of constant values of d. The motivation of this study is to reproduce effect of sub-grid scales on a motion of Lagrangian particles (e.g. droplets) in clouds.

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Section: Dns Of Stratocumulus-top Mixing Layermentioning

confidence: 93%

Fractal Reconstruction of Sub-Grid Scales for Large Eddy Simulation

Akinlabi

Wacławczyk

Malinowski

et al. 2019

Flow Turbulence Combust

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“…In the previous works [10,11] alternative approaches to estimate EDR from low or moderate-resolution velocity time series, based on zero-crossings and variance of velocity derivative were proposed. The zero-crossings is calculated simply as the number of times the signal crosses the level zero per unit length or per unit time.…”

Section: Estimation Of Edr From 1d Intersections Of the Turbulent Velmentioning

confidence: 99%

“…Moreover, the aim of this work is to establish a list of guidelines to specify which method(s) should be used under certain circumstances (e.g., in the case of low-frequency measurements or short fitting ranges). In this study, we will analyse the standard methods for EDR estimate from power spectra, second-order structure functions, as well as the recently proposed techniques based on the number of signal's zero-crossings and iterative methods [10,11]. The iterative methods are based on the second as well as the first Kolmogorov's hypothesis.…”

Section: Introductionmentioning

confidence: 99%

“…This fact is prospective for further applications, e.g., in the analysis of stratocumulus cloud turbulence, as it could allow to detect anisotropic zones and the existence of non-turbulent flow patches due to entrainment of clear-air or local relaminarisation [16]. We can expect, EDR estimates in such regions are deteriorated [11], as the Kolmogorov's assumptions are not satisfied.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Different Techniques to Calculate Properties of Atmospheric Turbulence from Low-Resolution Data

et al. 2020

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In this work we study different techniques to estimate basic properties of turbulence, that is its characteristic velocity and length scale from low-resolution data. The methods are based on statistics of the signals like the velocity spectra, second-order structure function, number of signal’s zero-crossings and the variance of velocity derivative. First, in depth analysis of estimates from artificial velocity time series is performed. Errors due to finite averaging window, finite cut-off frequencies and different fitting ranges are discussed. Next, real atmospheric measurement data are studied. It is demonstrated that differences between results of the methods can indicate deviations from the Kolmogorov’s theory or the presence of external intermittency, that is the existence of alternating laminar/turbulent flow patches.

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“…Large eddy simulation (LES) has provided an outstanding tool to study fog evolution [10,38,40,[45][46][47][48][49] though it still exhibits some limitations in representing the realistic synoptic processes of fog [50]. One of the limitations can be attributed to the inherent uncertainty of the parameterized turbulence models used in LES schemes [51,52] and partially to the embedded inaccuracy of the large-scale forcing [35]. To tackle the former limitation, we performed a three-dimensional direct numerical simulation (DNS) of a fog layer to avoid the uncertainty of turbulence parameterization.…”

Section: Numerical Simulationmentioning

confidence: 99%

Direct Numerical Simulation of Fog: The Sensitivity of a Dissipation Phase to Environmental Conditions

Karimi

2019

Atmosphere

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The sensitivity of fog dissipation to the environmental changes in radiation, liquid-water lapse rate, free tropospheric temperature and relative humidity was studied through numerical experiments designed based on the 2007-Paris Fog observations. In particular, we examine how much of the stratocumulus-thinning mechanism can be extended to the near-surface clouds or fog. When the free troposphere is warmed relative to the reference case, fog-top descends and become denser. Reducing the longwave radiative cooling via a more emissive free troposphere favors thickening the physical depth of fog, unlike cloud-thinning in a stratocumulus cloud. Drying the free troposphere allows fog thinning and promotes fog dissipation while sustaining the entrainment rate. The numerical simulation results suggest that the contribution of entrainment drying is more effective than the contribution of entrainment warming yielding the reduction in liquid water path tendency and promoting the onset of fog depletion relative to the reference case studied here. These sensitivity experiments indicate that the fog lifting mechanism can enhance the effect of the inward mixing at the fog top. However, to promote fog dissipation, an inward mixing mechanism only cannot facilitate removing humidity in the fog layer unless a sufficient entrainment rate is simultaneously sustained.Atmosphere 2020, 11, 12 2 of 22 stage. From the modeling standpoint, this challenge is attributed to a lack of knowledge of the physical processes and limitations in the ability to represent these processes in models. Forecasting numerical models predominantly use single-column schemes and also require parameterization of turbulent processes [15][16][17]. The role of turbulence on fog evolution remains controversial. Some argue that turbulence hampers the development of fog [18,19], while others deduce that turbulence favors fog development [20]. Poor predictive capabilities of most fog simulation schemes are attributed to not accurately capturing the local characteristics of turbulent mixing and surface-atmosphere coupling [10,21].The role of entrainment in fog life cycle has been mostly highlighted in marine fog through observational campaigns [22][23][24] and numerical simulations [25][26][27][28]. High entrainment drying is suggested to be not conducive to fog dissipation unless the radiative cooling increases [25]. While enhancement of TKE and Deardroff's convective velocity during the dissipation phase of an advection-radiation fog event has been observed [24], it was shown that due to low entrainment rate, the vertical turbulent fluxes are not strong enough to provide a convective source to prompt fog dissipation [29]. The contribution of the entrainment of drier and warmer air from aloft to fog dissipation has been identified as crucial to the role of the high subsidence rate and the shortwave radiative flux [30]. Some investigators attribute marine fog dissipation to thermal turbulence and dry air entrainment in the fog-top region [23,24,31], and others to the li...

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Estimating Turbulence Kinetic Energy Dissipation Rates in the Numerically Simulated Stratocumulus Cloud-Top Mixing Layer: Evaluation of Different Methods

Cited by 25 publications

References 43 publications

Fractal Reconstruction of Sub-Grid Scales for Large Eddy Simulation

Fractal Reconstruction of Sub-Grid Scales for Large Eddy Simulation

Comparison of Different Techniques to Calculate Properties of Atmospheric Turbulence from Low-Resolution Data

Direct Numerical Simulation of Fog: The Sensitivity of a Dissipation Phase to Environmental Conditions

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