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6 results for “Turbulent kinetic energy”
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Data from: Can IR images of the water surface be used to quantify the energy spectrum and the turbulent kinetic energy dissipation rate?
Open the record for dataset details and reuse information.
Data accompanying the paper "Regime-dependent turbulence length scale formulation for NWP models based on turbulence kinetic energy, shear and stratification", submitted to Monthly Weather Review
<p>This repository contains the outputs of MicroHH LES (van Heerwaarden et al., 2017) and ALADIN-CZ single-column model simulations of four idealized cases:</p> <p>1) The continental cumulus case utilizing measurements from the Atmospheric Radiation Measurement (ARM) program, and Cloud and Radiation Testbed (CART) site in Oklahoma (Brown et al. 2002; Lenderink et al. 2004)</p> <p>2) The trade wind cumulus case from the Barbados Oceanographic and Meteorological Experiment (BOMEX; Siebesma et al. 2003)</p> <p>3) A drizzling stratocumulus case based on the first research flight data of the second period of the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) campaign (Stevens et al., 2005)</p> <p>4) A stable planetary boundary layer case based on the Global Energy and Water Cycle Experiment (GEWEX) Atmospheric Boundary Layer Study (GABLS1) data (Beare et al. 2006; Cuxart et al. 2006; Holtslag 2006)</p> <p>The MicroHH LES outputs are taken from Reilley et al. (2022) study and can be also found at https://doi.org/10.5281/zenodo.6372434. Additionally, we provide case study outputs from the ALADIN-CZ 3D NWP model, wherein the fields roughly match those verified/shown in Fig. 9.</p> <p> </p> <p>The files are organized in the following way:</p> <p>1) MicroHH LES model configuration files (.ini) and output files (NetCDF format) are stored in the file "LES.zip" within "conf" and "data" folders, respectivelly. Additionally, a sample script to plot LES-derived Turbulence Length Scales (TLS) and those based on NWP formulations (using LES data as input) is provided (plot.py).</p> <p>2) The vertical profiles of (i) conserved variables and (ii) turbulent fluxes from the ALADIN-CZ single-column model for four idealized cases are provided in the file "single-column_simulations.zip", consisted of individual ASCII files (per case and TLS formulation). A detailed description of its content can be found in the associated README file.</p> <p>3) Chosen surface and upper-air fields for (i) 23 November 2019 inversion and (ii) 24 June 2022 mesoscale convection system cases are provided in the file "case_studies.zip" and consisted of individual GRIB files per field and prognostic hour. A detailed description of its content can be found in the associated README file.</p> <p> </p> <p> </p>
Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy
<p>This data deposit contains all the datasets needed to draw Figures 8 and 9 in the paper "Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy", which is now under review for potential publication in Water Resources Research. </p>
Turbulent Kinetic Energy Production in a Far-Field River Plume under Upwelling-Favorable Winds
<p>Data for submitted paper "Turbulent Kinetic Energy Production in a Far-Field River Plume under Upwelling-Favorable Winds"</p>
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OpenNeuro
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