scholarly journals Toward Large‐Eddy Simulations of Dust Devils of Observed Intensity: Effects of Grid Spacing, Background Wind, and Surface Heterogeneities

2019 ◽  
Vol 124 (14) ◽  
pp. 7697-7718
Author(s):  
S. Giersch ◽  
M. Brast ◽  
F. Hoffmann ◽  
S. Raasch
Keyword(s):  
Large Eddy Simulations ◽  
Background Wind ◽  
Grid Spacing ◽  
Dust Devils ◽  
Large Eddy

scholarly journals Vortex Encounter Rates with Fixed Barometer Stations: Comparison with Visual Dust Devil Counts and Large-Eddy Simulations

2014 ◽  
Vol 71 (12) ◽  
pp. 4461-4472 ◽  
Author(s):  
Ralph D. Lorenz
Keyword(s):  
Pressure Drop ◽  
Field Studies ◽  
Large Eddy Simulations ◽  
Encounter Rate ◽  
Dust Devil ◽  
Dust Devils ◽  
Translation Speed ◽  
Pressure Drops ◽  
Large Eddy ◽  
Dust Lifting

Abstract A phenomenological model is developed wherein vortices are introduced at random into a virtual arena with specified distributions of diameter, core pressure drop, longevity, and translation speed, and the pressure history at a fixed station is generated using an analytic model of vortex structure. Only a subset of the vortices present are detected as temporary pressure drops, and the observed peak pressure-drop distribution has a shallower slope than the vortex-core pressure drops. Field studies indicate a detection rate of about two vortex events per day under favorable conditions for a threshold of 0.2 mb (1 mb = 1 hPa): this encounter rate and the observed falloff of events with increasing pressure drop can be reproduced in the model with approximately 300 vortices per square kilometer per day—rather more than the highest visual dust devil counts of approximately 100 devils per square kilometer per day. This difference can be reconciled if dust lifting typically only occurs in the field above a threshold core pressure drop of about 0.3 mb, consistent with observed laboratory pressure thresholds. The vortex population modeled to reproduce field results is concordant with recent high-resolution large-eddy simulations, which produce some thousands of 0.04–0.1-mb vortices per square kilometer per day, suggesting that these accurately reproduce the character of the strongly heated desert boundary layer. The amplitude and duration statistics of observed pressure drops suggest large dust devils may preferentially be associated with low winds.

scholarly journals Comment on ''Observing desert dust devils with a pressure logger" by Lorenz (2012) – insights on measured pressure fluctuations from large-eddy simulations

2012 ◽  
Vol 1 (2) ◽  
pp. 151-154 ◽  
Author(s):  
A. Spiga
Keyword(s):  
Pressure Fluctuations ◽  
Field Measurements ◽  
Large Eddy Simulations ◽  
Pressure Measurements ◽  
Dust Devils ◽  
Pressure Drops ◽  
Large Eddy ◽  
Measured Pressure ◽  
Term Field

Abstract. Lorenz et al. (2012) proposes to use pressure loggers for long-term field measurements in terrestrial deserts. The dataset obtained through this method features both pressure drops (reminiscent of dust devils) and periodic convective signatures. Here we use large-eddy simulations to provide an explanation for those periodic convective signatures and to argue that pressure measurements in deserts have broader applications than monitoring dust devils.

scholarly journals Comment on "Observing desert dust devils with a pressure logger" by Lorenz (2012)

2012 ◽  
Vol 2 (2) ◽  
pp. 593-601 ◽  
Author(s):  
A. Spiga
Keyword(s):  
Field Measurements ◽  
Large Eddy Simulations ◽  
Pressure Measurements ◽  
Dust Devils ◽  
Pressure Drops ◽  
Desert Dust ◽  
Large Eddy ◽  
Term Field

Abstract. Lorenz (2012) proposes to use pressure loggers for long-term field measurements in terrestrial deserts. The dataset obtained through this method features both pressure drops (reminiscent of dust devils) and periodic convective signatures. Here we use Large-Eddy Simulations to provide an explanation for those periodic convective signatures and to argue that pressure measurements in deserts have broader applications than monitoring dust devils.

scholarly journals Large-Eddy Simulations of Dust Devils and Convective Vortices

2016 ◽  
Vol 203 (1-4) ◽  
pp. 245-275 ◽  
Author(s):  
Aymeric Spiga ◽  
Erika Barth ◽  
Zhaolin Gu ◽  
Fabian Hoffmann ◽  
Junshi Ito ◽  
...  
Keyword(s):  
Large Eddy Simulations ◽  
Dust Devils ◽  
Large Eddy ◽  
Convective Vortices

RAMS and WRF sensitivity to grid spacing in large-eddy simulations of the dry convective boundary layer

2015 ◽  
Vol 123 ◽  
pp. 54-71 ◽  
Author(s):  
G. Ercolani ◽  
C. Gorlé ◽  
C. García-Sánchez ◽  
C. Corbari ◽  
M. Mancini
Keyword(s):  
Boundary Layer ◽  
Convective Boundary Layer ◽  
Large Eddy Simulations ◽  
Grid Spacing ◽  
Convective Boundary ◽  
Large Eddy

Mixing efficiency in large-eddy simulations of stratified turbulence

2018 ◽  
Vol 849 ◽  
pp. 373-394 ◽  
Author(s):  
Sina Khani
Keyword(s):  
Numerical Simulations ◽  
Large Eddy Simulations ◽  
Mixing Efficiency ◽  
Stratified Turbulence ◽  
Grid Spacing ◽  
Computational Costs ◽  
Large Eddy ◽  
Ozmidov Scale ◽  
Computational Resources ◽  
Scaling Coefficient

The irreversible mixing efficiency is studied using large-eddy simulations (LES) of stratified turbulence, where three different subgrid-scale (SGS) parameterizations are employed. For comparison, direct numerical simulations (DNS) and hyperviscosity simulations are also performed. In the regime of stratified turbulence where $Fr_{v}\sim 1$, the irreversible mixing efficiency $\unicode[STIX]{x1D6FE}_{i}$ in LES scales like $1/(1+2Pr_{t})$, where $Fr_{v}$ and $Pr_{t}$ are the vertical Froude number and turbulent Prandtl number, respectively. Assuming a unit scaling coefficient and $Pr_{t}=1$, $\unicode[STIX]{x1D6FE}_{i}$ goes to a constant value $1/3$, in agreement with DNS results. In addition, our results show that the irreversible mixing efficiency in LES, while consistent with this prediction, depends on SGS parameterizations and the grid spacing $\unicode[STIX]{x1D6E5}$. Overall, the LES approach can reproduce mixing efficiency results similar to those from the DNS approach if $\unicode[STIX]{x1D6E5}\lesssim L_{o}$, where $L_{o}$ is the Ozmidov scale. In this situation, the computational costs of numerical simulations are significantly reduced because LES runs require much smaller computational resources in comparison with expensive DNS runs.

scholarly journals An Anisotropic Subgrid-Scale Parameterization for Large-Eddy Simulations of Stratified Turbulence

2020 ◽  
Vol 148 (10) ◽  
pp. 4299-4311
Author(s):  
Sina Khani ◽  
Michael L. Waite
Keyword(s):  
Equations Of Motion ◽  
Computational Cost ◽  
Large Eddy Simulations ◽  
Subgrid Scale ◽  
Stratified Turbulence ◽  
Grid Spacing ◽  
Large Eddy ◽  
Vertical Grid ◽  
Ocean Models ◽  
Les Model

AbstractSubgrid-scale (SGS) parameterizations in atmosphere and ocean models are often defined independently in the horizontal and vertical directions because the grid spacing is not the same in these directions (anisotropic grids). In this paper, we introduce a new anisotropic SGS model in large-eddy simulations (LES) of stratified turbulence based on horizontal filtering of the equations of motion. Unlike the common horizontal SGS parameterizations in atmosphere and ocean models, the vertical derivatives of the horizontal SGS fluxes are included in our anisotropic SGS scheme, and therefore the horizontal and vertical SGS dissipation mechanisms are not disconnected in the newly developed model. Our model is tested with two vertical grid spacings and various horizontal resolutions, where the horizontal grid spacing is comparatively larger than that in the vertical. Our anisotropic LES model can successfully reproduce the results of direct numerical simulations, while the computational cost is significantly reduced in the LES. We suggest the new anisotropic SGS model as an alternative to current SGS parameterizations in atmosphere and ocean models, in which the schemes for horizontal and vertical scales are often decoupled. The new SGS scheme may improve the dissipative performance of atmosphere and ocean models without adding any backscatter or other energizing terms at small horizontal scales.

scholarly journals Large-Eddy Simulations of Dust Devils and Convective Vortices

2017 ◽  
pp. 245-275 ◽  
Author(s):  
Aymeric Spiga ◽  
Erika Barth ◽  
Zhaolin Gu ◽  
Fabian Hoffmann ◽  
Junshi Ito ◽  
...  
Keyword(s):  
Large Eddy Simulations ◽  
Dust Devils ◽  
Large Eddy ◽  
Convective Vortices

Buoyancy scale effects in large-eddy simulations of stratified turbulence

2014 ◽  
Vol 754 ◽  
pp. 75-97 ◽  
Author(s):  
Sina Khani ◽  
Michael L. Waite
Keyword(s):  
Scale Effects ◽  
Vertical Direction ◽  
Local Maximum ◽  
Large Eddy Simulations ◽  
Energy Spectra ◽  
Buoyancy Frequency ◽  
Stratified Turbulence ◽  
Grid Spacing ◽  
Smagorinsky Model ◽  
Large Eddy

AbstractIn this paper large-eddy simulations (LES) of forced stratified turbulence using two common subgrid scale (SGS) models, the Kraichnan and Smagorinsky models, are studied. As found in previous studies using regular and hyper-viscosity, vorticity contours show elongated horizontal motions, which are layered in the vertical direction, along with intermittent Kelvin–Helmholtz (KH) instabilities. Increased stratification causes the layer thickness to collapse towards the dissipation scale, ultimately suppressing these instabilities. The vertical energy spectra are relatively flat out to a local maximum, which varies with the buoyancy frequency $\def \xmlpi #1{}\def \mathsfbi #1{\boldsymbol {\mathsf {#1}}}\let \le =\leqslant \let \leq =\leqslant \let \ge =\geqslant \let \geq =\geqslant \def \Pr {\mathit {Pr}}\def \Fr {\mathit {Fr}}\def \Rey {\mathit {Re}}N$. The horizontal energy spectra depend on the grid spacing $\varDelta $; if the resolution is fine enough, the horizontal spectrum shows an approximately $-5/3$ slope along with a bump at the buoyancy wavenumber $k_b = N/u_{rms}$, where $u_{rms}$ is the root-mean-square (r.m.s.) velocity. Our results show that there is a critical value of the grid spacing $\varDelta $, below which dynamics of stratified turbulence are well-captured in LES. This critical $\varDelta $ depends on the buoyancy scale $L_b$ and varies with different SGS models: the Kraichnan model requires $\varDelta < 0.47 L_b$, while the Smagorinsky model requires $\varDelta < 0.17 L_b$. In other words, the Smagorinsky model is significantly more costly than the Kraichnan approach, as it requires three times the resolution to adequately capture stratified turbulence.

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