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41 results for “large-eddy simulation”
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models
<p>Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."</p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
Supporting data for "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics"
<p>Numerical settings, routines and post-processed data used to generate the figures presented in "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics", manuscript submitted to Journal of Advances in Modeling Earth Systems. This version is an update after accounting for comments of three reviewers to the submitted manuscript.</p>
Realtime WRF Large-Eddy Simulation Data
<p>Sample data files from realtime micro-scale weather simulations that were performed to support UAV (Unmanned Aerial Vehicles) flights during ISARRA Lower Atmospheric Process Studies at Elevation – Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field experiment. Two types of data are available: (1) ascii files that contain profiles and surface variables at select grid points that correspond with 3 key observing locations, (2) grib2 files contain 2D and 3D grids of variables described below and stored on levels AGL as described more below. The sample data includes all leadtimes from both domains. Domain 01 (1 km grid spacing) is initialized at 0400 UTC, lead times = 00h00m to 18h00m by 10m increments (108 files) and DO2 is initialized at 1000 UTC with leadtimes of 06h00m-18h00m by 10 min increments (72 files). Each grib2 file from the WRF-LES domain (D02) are around 100 MB per lead time. The full dataset is available here: <a href="https://doi.org/10.5065/83r2-0579">https://doi.org/10.5065/83r2-0579</a></p> <p>These simulations were performed by driving a nested grid configuration of the Weather Research and Forecasting model with its innermost mesh being run at 111 m grid spacing. The innermost grid was nested within a grid with 1 km grid spacing. The outermost grid being driven using operational forecast models data as described below. While the MYNN2 PBL scheme is used to parameterize turbulence in the 1 km grid, the PBL scheme is turned off within the 111 m grid, thus, allowing large-scale turbulent eddies to be resolved by WRF primitive equations. Subgrid-scale turbulence is diagnosed and stored within the TKE variable using Lilly (1966, 1967).<br> <br> The realtime simulations were produced twice per day in order to support mission planning and UAVs flight operations. A next-day simulation was run using forcing data from NCEP's Global Forecast System (GFS) while a day-of simulation was run using data from the High Resolution Rapid Refresh (HRRR). Both simulations were valid between 04:00 and 16:00 local time providing an opportunity to explore the impact of lateral boundary conditions on forecast skill. The dataset consists of a series of two sets of files: 3D grids and point profiles. The 3D grids consist of all relevant basic state parameters (p,T,U,RH) and diagnostics (e.g., sub-grid scale TKE, ceiling height, visibility) that have been interpolated to flight levels AGL using the Unified Post-Processor (UPP). The UPP was used to de-stagger the mass and wind fields (and compute wind in earth-relative coordinates), interpolate forecast data to flight levels AGL and to compute diagnostics such as visibility, ceiling height, and radar reflectivity.</p> <p>Profile and surface data are stored in ascii format for select grid points coincident with up to 3 fixed observation sites set up during LAPSE-RATE (i.e., Saguache, Moffat and Leach Airfield) with a time resolution of 0.666 sec. See README files for details. The 3D output files are stored in grib2 format which are available every 10 min. The grib2 data can be converted to netCDF using a variety of tools such as ncl_convert2nc command available on many linuxOS.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Details of ascii file names:</p> <p>XXX.dnn.VV.yyyymmddhh.gz</p> <p>where</p> <p>XXX is the location name (SAG - Saguache, MOF - Moffat, LEA - Leach)</p> <p>nn is the domain number (01,02,03)</p> <p>VV is the variable (see readme file for details)</p> <p>yyyymmddhh is the model initialization time (UTC)</p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Details of grib2 data files:</p> <p>Data are stored as follows: one file per forecast lead time using the following file naming convention:</p> <p>WRFPRS_YYYYMMDDhhmm_dnn.lh_lm</p> <p>where</p> <p>hhmm is the hour and minute of the day the model run was initialize</p> <p>nn = domain number</p> <p>lh = forecast outlook hour</p> <p>lm = forecast outlook min</p> <p>valid_time = hhmm + lhlm</p> <p>zlevels = (30,80,150,300,500,750,1000,1250,1500,1750,2000,2500,3000,3500,4000,4500,5000 m AGL)<br> <br> DatasetTemporalCoverage: 14 - 19 July 2018<br> Forecasts were issued twice per-day and valid between 04:00 and 16:00 LT (10:00 and 22:00 UTC)</p> <p>Gridded data are stored at 10 min intervals</p> <p><br> Interpolated 3D Variables: temperature,pressure,u,v,w,RH,sub-gridscale turbulence kinetic energy, energy dissipation, etc at selected heights AGL<br> <br> 2D Diagnosed variables:<br> Ceiling height, visibility, precip rate, accumulated precip, precipitable water, vertically- integrated condensed water, downwelling shortwave and longwave radiation at surface, sensible and latent heat flux, 10 m U and V, 2 m T and specific humidity<br> </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 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>
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>
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>
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>
Diagnosing nonlocal effects and coherent structure scales in moist convection using a large-eddy simulation
Open the record for dataset details and reuse information.
DATA for Evaluating Scale-Aware Boundary Layer Similarity Functions and Their Mechanisms in Tropical Cyclone Modeling Using Idealized Large-Eddy Simulations
<p>data.xlsx has the data to make Figs.1-4</p> <p> </p>
Large-eddy simulation model data for article Study of surface layer characteristics in the presence of suspended snow particles using observational data and large-eddy simulation
<p>Large-eddy simulation model data [8. Mortikov E.V., Glazunov A.V., <br> Lykosov V.N. Numerical study of plane Couette flow: <br> turbulence statistics and the structure of pressure-strain correlations <br> // Russ. J. Numer. Analysis Math. Model. 2019. V. 34. № 2. P. 119–132.]<br> The setup of experiments was based<br> on the GABLS-1. The height, width and length of the domain was 4000 m<br> with spatial resolution of 11.7m. U18, U16 - geostrophic wind 18 and 16 m/s,<br> CR - cooling rate 0K/h, 1K/h, 2K/h.<br> Two series of experiments were performed: “NS“ and “SS“.<br> In the experiment “NS“ the surface layer was described according to the<br> Monin-Obukhov similarity theory. The experiments “SS“<br> utilized the parameterization, which takes into account the effect<br> snow particles.</p>
Large-eddy Simulation of a Wind-turbine Array subjected to Active Yaw Control
<p>Dataset of the paper "Large-eddy Simulation of a Wind-turbine Array subjected to Active Yaw Control" published on Wind Energy Science [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2022). Large-eddy Simulation of a Wind-turbine Array subjected to Active Yaw Control. <em>Wind Energy Science Discussions</em>, 1-22.</p>
Large-eddy simulation of turbulent oscillatory flow over three-dimensional transient vortex ripple geometries in quasi-equilibrium
<p>dataset for figures of 'Large-eddy simulation of turbulent oscillatory flow over three-dimensional transient vortex ripple geometries in quasi-equilibrium'</p>
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