Responses of Shallow Cumulus Convection to Large-Scale Temperature and Moisture Perturbations: A Comparison of Large-Eddy Simulations and a Convective Parameterization Based on Stochastically Entraining Parcels

2012 ◽  
Vol 69 (6) ◽  
pp. 1936-1956 ◽  
Author(s):  
Ji Nie ◽  
Zhiming Kuang
Keyword(s):  
Large Scale ◽  
Potential Temperature ◽  
Large Eddy Simulations ◽  
Cumulus Parameterization ◽  
Temperature Perturbation ◽  
Cumulus Convection ◽  
Positive Temperature ◽  
Shallow Cumulus ◽  
Scale Temperature ◽  
Large Eddy

Abstract Responses of shallow cumuli to large-scale temperature/moisture perturbations are examined through diagnostics of large-eddy simulations (LESs) of the undisturbed Barbados Oceanographic and Meteorological Experiment (BOMEX) case and a stochastic parcel model. The perturbations are added instantaneously and allowed to evolve freely afterward. The parcel model reproduces most of the changes in the LES-simulated cloudy updraft statistics in response to the perturbations. Analyses of parcel histories show that a positive temperature perturbation forms a buoyancy barrier, which preferentially eliminates parcels that start with lower equivalent potential temperature or have experienced heavy entrainment. Besides the amount of entrainment, the height at which parcels entrain is also important in determining their fate. Parcels entraining at higher altitudes are more likely to overcome the buoyancy barrier than those entraining at lower altitudes. Stochastic entrainment is key for the parcel model to reproduce the LES results. Responses to environmental moisture perturbations are quite small compared to those to temperature perturbations because changing environmental moisture is ineffective in modifying buoyancy in the BOMEX shallow cumulus case. The second part of the paper further explores the feasibility of a stochastic parcel–based cumulus parameterization. Air parcels are released from the surface layer and temperature/moisture fluxes effected by the parcels are used to calculate heating/moistening tendencies due to both cumulus convection and boundary layer turbulence. Initial results show that this conceptually simple parameterization produces realistic convective tendencies and also reproduces the LES-simulated mean and variance of cloudy updraft properties, as well as the response of convection to temperature/moisture perturbations.

scholarly journals Using the Sensitivity of Large-Eddy Simulations to Evaluate Atmospheric Boundary Layer Models

2012 ◽  
Vol 69 (5) ◽  
pp. 1582-1601 ◽  
Author(s):  
Gilles Bellon ◽  
Bjorn Stevens
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Stationary State ◽  
Large Scale ◽  
Large Eddy Simulations ◽  
Tropospheric Temperature ◽  
Unstable Regime ◽  
Shallow Cumulus ◽  
Large Eddy ◽  
Bulk Model

Abstract A simple framework to study the sensitivity of atmospheric boundary layer (ABL) models to the large-scale conditions and forcings is introduced. This framework minimizes the number of parameters necessary to describe the large-scale conditions, subsidence, and radiation. Using this framework, the sensitivity of the stationary ABL to the large-scale boundary conditions [underlying sea surface temperature (SST) and overlying humidity and temperature in the free troposphere] is investigated in large-eddy simulations (LESs). For increasing SST or decreasing free-tropospheric temperature, the LES exhibits a transition from a cloud-free, well-mixed ABL stationary state, through a cloudy, well-mixed stationary state and a stable shallow cumulus stationary state, to an unstable regime with a deepening shallow cumulus layer. For a warm SST, when increasing free-tropospheric humidity, the LES exhibits a transition from a stable shallow cumulus stationary state, through a stable cumulus-under-stratus stationary state, to an unstable regime with a deepening, cumulus-under-stratus layer. For a cool SST, when increasing the free-tropospheric humidity, the LES stationary state exhibits a transition from a cloud-free, well-mixed ABL regime, through a well-mixed cumulus-capped regime, to a stratus-capped regime with a decoupling between the subcloud and cloud layers. This dataset can be used to evaluate other ABL models. As an example, the sensitivity of a bulk model based on the mixing-line model is presented. This bulk model reproduces the LES sensitivity to SST and free-tropospheric temperature for the stable and unstable shallow cumulus regimes, but it is less successful at reproducing the LES sensitivity to free-tropospheric humidity for both shallow cumulus and well-mixed regimes.

Investigation on hydrodynamic forces leading to coarse particle entrainment using Large-Eddy Simulations

2021 ◽  
Author(s):  
Gaston Latessa ◽  
Angela Busse ◽  
Manousos Valyrakis
Keyword(s):  
Experimental Data ◽  
Flow Field ◽  
Large Scale ◽  
Large Eddy Simulations ◽  
Flow Conditions ◽  
Mean Flow ◽  
Protection Measures ◽  
Practical Applications ◽  
Particle Entrainment ◽  
Large Eddy

<p>The prediction of particle motion in a fluid flow environment presents several challenges from the quantification of the forces exerted by the fluid onto the solids -normally with fluctuating behaviour due to turbulence- and the definition of the potential particle entrainment from these actions. An accurate description of these phenomena has many practical applications in local scour definition and to the design of protection measures.</p><p>In the present work, the actions of different flow conditions on sediment particles is investigated with the aim to translate these effects into particle entrainment identification through analytical solid dynamic equations.</p><p>Large Eddy Simulations (LES) are an increasingly practical tool that provide an accurate representation of both the mean flow field and the large-scale turbulent fluctuations. For the present case, the forces exerted by the flow are integrated over the surface of a stationary particle in the streamwise (drag) and vertical (lift) directions, together with the torques around the particle’s centre of mass. These forces are validated against experimental data under the same bed and flow conditions.</p><p>The forces are then compared against threshold values, obtained through theoretical equations of simple motions such as rolling without sliding. Thus, the frequency of entrainment is related to the different flow conditions in good agreement with results from experimental sediment entrainment research.</p><p>A thorough monitoring of the velocity flow field on several locations is carried out to determine the relationships between velocity time series at several locations around the particle and the forces acting on its surface. These results a relevant to determine ideal locations for flow investigation both in numerical and physical experiments.</p><p>Through numerical experiments, a large number of flow conditions were simulated obtaining a full set of actions over a fixed particle sitting on a smooth bed. These actions were translated into potential particle entrainment events and validated against experimental data. Future work will present the coupling of these LES models with Discrete Element Method (DEM) models to verify the entrainment phenomena entirely from a numerical perspective.</p>

scholarly journals Automated tracking of shallow cumulus clouds in large domain, long duration Large Eddy Simulations

2013 ◽  
Vol 6 (2) ◽  
pp. 2287-2323 ◽  
Author(s):  
T. Heus ◽  
A. Seifert
Keyword(s):  
Large Data ◽  
Current Method ◽  
Large Eddy Simulations ◽  
Large Data Sets ◽  
Data Sets ◽  
Cumulus Clouds ◽  
3 Dimensional ◽  
Shallow Cumulus ◽  
Long Duration ◽  
Large Eddy

Abstract. This paper presents a method for feature tracking of fields of shallow cumulus convection in Large Eddy Simulations (LES) by connecting the projected cloud cover in space and time, and by accounting for splitting and merging of cloud objects. Existing methods tend to be either imprecise or, when using the full 3 dimensional spatial field, prohibitively expensive for large data sets. Compared to those 3-D methods, the current method reduces the memory footprint by up to a factor 100, while retaining most of the precision by correcting for splitting and merging events between different clouds. The precision of the algorithm is further enhanced by taking the vertical extent of the cloud into account. Furthermore, rain and subcloud thermals are also tracked, and links between clouds, their rain, and their subcloud thermals are made. The method compares well with results from the literature. Resolution and domain dependencies are also discussed. For the current simulations, the cloud size distribution converges for clouds larger than an effective resolution of 6Δx, and smaller than about 20% of the horizontal domains size.

scholarly journals High-resolution large-eddy simulations of sub-kilometer-scale turbulence in the upper troposphere lower stratosphere

2013 ◽  
Vol 13 (12) ◽  
pp. 31891-31932 ◽  
Author(s):  
R. Paoli ◽  
O. Thouron ◽  
J. Escobar ◽  
J. Picot ◽  
D. Cariolle
Keyword(s):  
Large Scale ◽  
Lower Stratosphere ◽  
Atmospheric Model ◽  
Large Eddy Simulations ◽  
Stratified Flows ◽  
Flow Topology ◽  
Upper Troposphere ◽  
Large Eddy ◽  
Scale Turbulence ◽  
The Impact

Abstract. Large-eddy simulations of sub-kilometer-scale turbulence in the upper troposphere lower stratosphere (UTLS) are carried out and analyzed using the mesoscale atmospheric model Méso-NH. Different levels of turbulence are generated using a large-scale stochastic forcing technique that was especially devised to treat atmospheric stratified flows. The study focuses on the analysis of turbulence statistics, including mean quantities and energy spectra, as well as on a detailed description of flow topology. The impact of resolution is also discussed by decreasing the grid spacing to 2 m and increasing the number of grid points to 8×109. Because of atmospheric stratification, turbulence is substantially anisotropic, and large elongated structures form in the horizontal directions, in accordance with theoretical analysis and spectral direct numerical simulations of stably stratified flows. It is also found that the inertial range of horizontal kinetic energy spectrum, generally observed at scales larger than a few kilometers, is prolonged into the sub-kilometric range, down to the Ozmidov scales that obey isotropic Kolmorogov turbulence. The results are in line with observational analysis based on in situ measurements from existing campaigns.

scholarly journals Statistical ensemble of large-eddy simulations

2002 ◽  
Vol 455 ◽  
pp. 195-212 ◽  
Author(s):  
DANIELE CARATI ◽  
MICHAEL M. ROGERS ◽  
ALAN A. WRAY
Keyword(s):  
Large Scale ◽  
Isotropic Turbulence ◽  
Velocity Fields ◽  
Large Eddy Simulations ◽  
Statistical Ensemble ◽  
Model Parameters ◽  
Spatial Averaging ◽  
Subgrid Scale ◽  
Large Eddy ◽  
New Models

A statistical ensemble of large-eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large-scale velocity fields is used in an ensemble-averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the ensemble-averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the ensemble of LES provides statistics of the large-scale velocity that can be used for building new models for the subgrid-scale stress tensor. The ensemble-averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time-developing plane wake. It is found that the results are almost independent of the number of LES in the statistical ensemble provided that the ensemble contains at least 16 realizations.

A study of shallow cumulus cloud droplet dispersion by large eddy simulations

2011 ◽  
Vol 25 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Xiaofeng Wang ◽  
Huiwen Xue ◽  
Wen Fang ◽  
Guoguang Zheng
Keyword(s):  
Cloud Droplet ◽  
Large Eddy Simulations ◽  
Cumulus Cloud ◽  
Droplet Dispersion ◽  
Shallow Cumulus ◽  
Large Eddy
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