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70 results for “Large Eddy Simulations”
Data and Code for Sensitivities of Large Eddy Simulations of Aerosol Plume Transport and Cloud Response
<p>This record provides code, analysis notebooks, numerical model input files and data associated with the manuscript "Sensitivities of Large Eddy Simulations of Aerosol Plume Transport and Cloud Response."</p><p>The file "simulation_table.pdf" defines the key model settings that differentiate the set of simulations (identified by a number 1 through 12).</p><p>The file "Dataset.tar.gz" provides simulation input files (namelist files) and output files from each simulation that are needed to reproduce the figures of the manuscript (with the exception noted below). It also contains Jupyter notebooks that can be used to create the figures.</p><p>The file "PINACLES_forPlumeStudy.tar" contains a version of the large eddy simulation modeling code<a href="https://doi.org/10.2172/1869291"> </a>used to perform the simulations. </p><p>To reproduce all figures, also obtain the data here: <a href="https://doi.org/10.5281/zenodo.10278509">10.5281/zenodo.10278509</a></p>
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of potential temperature)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of potential temperature) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of wind speed)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of wind speed) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (wind speed at 90 m AGL)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (wind speed at 90 m AGL) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
Large Eddy Simulation data of flow in a rectangular diffuser
<p>This dataset contains the results of large-eddy simulations of the flow in a rectangular asymmetric diffuser. Here only the D2 diffuser's data are provided because experimental and DNS data are avilable for D1. .</p>
Large-eddy simulation of airborne wind energy farms: AWES virtual flight data
<p>Large-eddy simulation of airborne wind energy farms: AWES virtual flight data.</p> <p>This dataset contains virtual flight data collected from individual systems in airborne wind energy parks obtained by means of large-eddy simulations. All data are stored as Python dictionary objects in the Pickle format. Additional Python scripts are provided to visualize the data. </p> <p> </p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Article Data
<p>NetCDF datatset of presented results from the publication titled "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model" in the Journal of Geophysical Research - Atmospheres, Paper #2021JD036214R.</p>
Large eddy simulations of precipitating marine stratocumulus are sensitive to free tropospheric aerosol concentrations
<p>A sample of prognostic and diagnostic Control simulation data generated for our study. Data include the time series and vertical profiles used in the analysis of our study into the importance of free tropospheric aerosol in marine stratocumulus clouds. Full datasets are available on request from the author.</p>
Supporting files for "Towards understanding the differences between mesoscale and large-eddy simulations of tropical cyclones"
<p>This deposit includes the time- and azimuth-averaged velocity fields for the five idealized tropical cyclones described in "Towards understanding the differences between mesoscale and large-eddy simulations of tropical cyclones". The horizontal wind speed magnitude, radial velocity, tangential velocity, vertical velocity, and potential temperature fields are included for the mesoscale (d01) and LES (d02) domains.</p>
Parameterization of Wave-Induced Stress in Large-Eddy Simulations of the Marine Atmospheric Boundary Layer
<p>This dataset contains the PALM simulation data for all groups and the reference data from Jiang et al.</p>
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains
<p>Animations from weather radars (observations) and simulations (modeling) of an atmospheric bore and associated gravity waves during the AWAKEN field campaign from a case study on 06 June 2023. The first animation is reflectivity from the NEXRAD WSR-88D system at the Oklahoma City radar site (KTLX) operated by the National Weather Service. The second animation is wind speed at 95, 145, and 270 m agl from the Texas Tech X-Band radars at the AWAKEN site. The third and fourth animations are for simulation results using the Weather Research and Forecasting model (WRF) with two domains. The third animation is vertical velocity at 1 km agl and potential temperature at 200 m agl on domain d01, which has a horizontal grid spacing of 300 m. The fourth animation is hub-height wind speed and perturbation pressure along with simulated power output for 3 wind turbines in each of the four rows on domain d01, which has a horizontal grid spacing of 20 m. The wind turbines are NREL 2.8 MW turbines parameterized using a generalized actuator disk. </p> <p>These animations are included as supplementary material for the manuscript "Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains" submitted to <em>Wind Energy Science</em>. </p>
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
<p>This dataset accompanies the GMD article 'A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0' (https://doi.org/10.5194/egusphere-2024-96).</p> <ul> <li>The input files to run the presented cases using uDALES are contained in 'inputs'.</li> <li>The model outputs are contained in separate folders: 'XCC', 'indoor-outdoor', 'XCB', and 'SEB'. When downloaded, move into a folder called 'outputs' so that the paths defined in the scripts work as intended (see below).</li> <li>The Matlab scripts to plot the figures are contained in 'scripts'.</li> <li>The figures shown in the article are contained in 'figures'.</li> </ul>
Input data for article "Large eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations"
<p>Input dataset used when performing LES simulations for journal article "Large eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations" (Karttunen et al., in preparation). The dataset was used with the PALM model system revision 3698 and most likely it won't work on older or newer versions.</p> <p>Instructions for use:<br> A precursor run must be run first. Output data (BINOUT) of it should be linked into a BININ directory of the actual scenario runs. You'll most likely have to tweak the CPU grid settings in ENVPAR and PARIN files in order to fit them to your computational resources. For more information on usage please refer to the PALM model documentation available online in <a href="https://palm.muk.uni-hannover.de/trac/wiki/doc">https://palm.muk.uni-hannover.de/trac/wiki/doc</a>.</p>
Large eddy simulation input data for publication "Pressure fields in the airflow over wind-generated surface waves" by Funke et al.
<p>Input files allow for the simulation of a turbulent flow over a sinusoidal surface using the PALM LES model, version 6.0, revision 4901.</p>
Dataset of "Divergent convective outflow in large eddy simulations "
<p>See README.txt</p> <p>See download_all_simulations.sh</p>
Codes and datasets associated with the paper "Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers"
<p>From here, you will find some of the codes, simulation data in the article: </p> <p>Dai, Y., Basu, S., Maronga, B. and de Roode, S.R., 2020. Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers. <em>arXiv preprint arXiv:2003.09463</em>.</p> <p><strong>Simulation results from DALES: </strong></p> <p>within the folder of DALES, D52 denotes default scheme of Deardorff, H52 denotes the revised Deardorff scheme. Numbers of each fold indicates the grid number in each direction. </p> <p><strong>Simulation results from MATLES:</strong></p> <p>file name: MATLES/variable.out</p> <p>Example: MATLES/aT.out, the potential temperature data (2D, time and height) from MATLES</p> <p><strong>Python code for plotting: </strong></p> <p>dalesfunc.py is the function file used to process data</p> <p>DALES_plotting.py is the code file used for plotting the simulation results from DALES and MATLES</p> <p><strong>DALES input:</strong></p> <p>namoptions in each folder is the file for DALES options input</p>
Large eddy simulation over idealized urban terrain
<p>The data contains results from large eddy simulations over idealized urban topography.</p>
Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions
<p>Dataset to produce the results of the publication: "<em>Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions</em>". This dataset supports the findings presented in the publication and includes comprehensive resources for replicating its analysis and visualization. </p> <p> The dataset encompasses:</p> <ul> <li><strong>Dutch Atmospheric Large-Eddy Simulation (DALES) Data</strong><br> <ul> <li>Configuration files</li> <li>Selected simulation output data</li> </ul> </li> <li><strong>Image Data</strong> <ul> <li>Rendered stereo camera images from the DALES output</li> <li>Actual stereo camera images</li> <li>Cloud masks generated from these images</li> </ul> </li> <li><strong>Camera-Based Reconstructions</strong> <ul> <li>Reconstructed cloud fields from the rendered camera images</li> <li>Reconstructed cloud fields from the actual camera images</li> </ul> </li> <li><strong>Derived Cloud Metrics</strong> <ul> <li>Cloud base areas, cloud base heights, and cloud cover from the camera-based reconstructions</li> </ul> </li> <li><strong>Observational Data</strong> <ul> <li>Radiosondes, Ceilometer, and Cloudnet measurements</li> <li>Cloud cover from radiation measurements</li> <li>Mixed layer height from the Doppler lidar</li> </ul> </li> <li><strong>Reproduction Scripts</strong> <ul> <li>Scripts to reproduce the analysis and figures</li> </ul> </li> </ul> <p> </p> <p>This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126 and by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under the projects RCONGM and VIRTUALLAB. JOYCE data were provided by the Institute for Geophysics and Meteorology of the University of Cologne. JOYCE is a collaborative research platform between University of Cologne and Forschungszentrum Jülich within the European research infrastructure ACTRIS. We acknowledge ACTRIS and the Finnish Meteorological Institute for providing Cloudnet data which is available for download from https://cloudnet.fmi.fi. We acknowledge ECMWF for providing IFS model data.</p>
Datasets used in "Large Eddy Simulation for Investigating Coupled Forest Canopy and Turbulence Influences on Atmospheric Chemistry"
<p>Model archived fields used in the generation of the figures in: Clifton, O. E., E. G. Patton, S. Wang, M. Barth, J. Orlando, R. H. Schwantes (2022), Large Eddy Simulation for Investigating Coupled Forest Canopy and Turbulence Influences on Atmospheric Chemistry, Journal of Advances in Modeling Earth Systems.</p>
"Halo Region around Shallow Cumulus Clouds in Large Eddy Simulations"
<p>This dataset is used to examine the halo regions around shallow cumulus clouds. We performed large eddy simulations of shallow cumulus clouds based on the Barbados Oceanographic and Meteorological Experiment (BOMEX) using Met Office-NERC (National Environment Research Concil) Cloud model (MONC). A set of simulations with different model resolutions is performed. The available horizontal grid spacings are 10 m, 25 m, 50 m and 100 m and the corresponding vertical resolutions are 10 m, 25 m, 25 m, and 40 m, respectively. All simulations have the same model top, that is, 3 km. To save the computational cost, we use the same horizontal grid boxes (600 X 600). To test the robustness, we perform sensitivity simulations with modified mixing length scales in the sub-grid scale turbulence scheme. We also perform large eddy simulations using another numerical model, Cloud Model 1 (CM1) to check if the results are sensitive to the numerical details (advection schemes) and domain size. The simulations with CM1 have the same model resolution configurations as in MONC, but they have the same domain size (7.2 X 7.2 X 3)km^3. For more details of the simulation set up, please refer to our submitted paper. As the whole simulation has a huge dataset, we only choose part of the results that can reproduce the figures in our submitted paper for repository. </p>
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OpenNeuro
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