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143 results for “Boundary layer”
On the importance of representing snow over sea-ice for simulating the Arctic boundary layer
<p>Correctly representing the snow on sea-ice in coupled numerical weather prediction models has great potential to improve weather forecast and climate monitoring applications, such as climate reanalyses, which are usually produced using such systems. In this study two different methodologies to account for the effect of the snowpack accumulating over the sea-ice in 5-day global forecasts are compared. </p> <p>This dataset contains the high-resolution (dx=9km) model data for the different simulations, with and without the snow over sea-ice, co-located with observations at the locations of the SHEBA and N-ICE2015 field campaigns. The dataset also contains the daily averaged skin temperature, for the different simulations (interpolated on 0.25x0.25) performed over the extended time period, 2015-01-01 to 2015-02-28. </p>
Turbulent Mechanisms for the Deep Convective Boundary Layer in the Taklimakan Desert
<p>The deep convective boundary layer (CBL) in the Taklimakan Desert plays an important role in the climate system in East Asia. Based on the observation experiment and large-eddy simulation, turbulent mechanisms for its formation were revealed in this study. This explained why the daily maximum CBL depth was independent of surface heating. In the late-morning, there was a weak temperature inversion and a near-neutral residual layer (RL) above the CBL. With the development of the CBL, stronger convection could penetrate the RL and even overshoot the top of the RL. The distinctive boundary-layer process entrained free-tropospheric air to warm the RL and then promoted the entrainment of the warmed air in the RL into the CBL. This extra energy supply effectively contributed to the growth of the CBL. With further positive feedback between the CBL and RL depths, a deeper CBL would form in consecutive fair-weather conditions.</p>
"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"
<p>Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." <em>Journal of Fluid Mechanics</em> 943 (2022).</p>
Supersonic/Hypersonic Zero-Pressure-Gradient Turbulent Boundary Layers
<p>Datasets M2.zip and M5.zip of long ZPG supersonic/hypersonic turbulent boundary layers related to the work<br>"Ceci, A., Palumbo, A., Larsson, J. et al. Numerical tripping of high-speed turbulent boundary layers. Theor. Comput. Fluid Dyn. 36, 865–886 (2022). https://doi.org/10.1007/s00162-022-00623-0"</p> <p>Files cf_fav*dat (Favre averaged velocities) contain columns with streamwise dstribution of the following quantities:<br> Supersonic cases, x/delta0,cf,retau,retheta,redelta2,delta99/delta0,p'_w^2/tauw, turbuent tau_xy peak, turbulent tau_xx peak<br> Hypersonic cases, x/delta0,cf,retau,retheta,redelta2,delta99/delta0,p'_w^2/tauw, turbuent tau_xy peak, ch<br><br>Files meanfields_*.prof (Favre averaged velocities) contain 2D streamwise and wall-normal dstribution of the following mean fields:<br> All cases, x/delta0,y/delta0,retau,y/delta99,y+,u+,rho/rhow,rho/rho0,T/Tw,T/T0,p/pw,p/p0,u''u''+,v''v''+,w''w''+,-u''v''+,ufav/u0</p>
Data supporting the conclusions of Atmospheric boundary layer classification with Doppler lidar
<p>This is data set includes Doppler wind lidar quantities which were calculated from Halo Photonics Streamline measurements between 2 September 2015 and 16 November 2016 at Hyytiälä, Finland and between 1 January 2015 and 31 December 2016 at Jũlich, Germany. The data set also includes the respective boundary layer classification results generated from the calculated lidar quantities from both of the sites.</p>
Turbulence statistics in smooth wall oscillatory boundary layer flow
<p>Experimental and numerical datasets belonging to Van der A, D.A., Scandura, P., O’Donoghue, T. (2018). Turbulence statistics in smooth wall oscillatory boundary layer flow, Journal of Fluid Mechanics, 849, 192-230. </p> <p> </p>
Data for "Transition of the hurricane boundary layer during the landfall of Hurricane Irene (2011)"
<p>This archive contains Shared Mobile Atmospheric Research and Teaching (SMART) radar data during the landfall of Hurricane Irene (2011). The data here are presented in cfradial format delineated into individual volumes for use in wind retrievals. Please refer to the included readme_sr2.txt file for information on the data contained in the volumes.</p> <p>Two range height indicators (RHIs) are also included in the archive with data description in the readme_sr2.txt file.</p> <p>The Rapid Scanning X-Band Polarimetric (RaXPol) radar from the University of Oklahoma also collected data sufficient to produce velocity-azimuth display (VAD) boundary layer profiles. The processed VADs are contained in python .npy format. Please see the readme_raxpol.txt file for information.</p> <p>This dataset is assocaited with the following submission, which is currently under review:</p> <p>Alford, A. A., J. A. Zhang, M. I. Biggerstaff, P. Dodge, F. D. Marks, and David J. Bodine, 2020: Transition of the hurricane boundary layer during the landfall of Hurricane Irene (2011). <em>Journal of the Atmospheric Sciences</em>, in review.</p> <p>At the time of publicaiton, the citation will be updated to reflect the proper DOI reference.</p>
Planetary Boundary Layer Height Retrievals from the Cloud-Aerosol Transport System (CATS) around the US Southern Great Plains and the Eastern North Atlantic
<p>Planetary Boundary Layer Height (PBLH) retrievals in kilometers from the Cloud-Aerosol Transport System (CATS) around the DOE ARM US Southern Great Plains (SGP) and the Eastern North Atlantic (ENA), using a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm. Quality control Flags are included as follows:</p> <ul> <li>0 = 'Good Quality'</li> <li>1 = 'Mediate Quality'</li> <li>2 = 'Bad Quality'</li> </ul> <p>In addition, -999 values in the dataset represent no data. <br>The PBLH for daytime denoised CATS photon counts at SGP is named: "daytime-denoised-dtds-pblh-sgp.csv"<br>The PBLH for the original daytime and nighttime data at SGP and ENA, without denoising the data, are named: "original-cats-dtds-pblh-daytime-nighttime-sgp.csv" and "original-cats-dtds-pblh-daytime-nighttime-ena.csv"</p> <p>References: </p> <p>Roldán-Henao, N., Yorks, J., Su, T., Selmer, P., & Li, Z. (2024). Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data. <em>Remote Sensing. </em></p> <p>Su, T., Li, Z., & Kahn, R. (2020). A new method to retrieve the diurnal variability of planetary boundary layer height from lidar under different thermodynamic stability conditions. <em>Remote Sensing of Environment</em>, <em>237</em>, 111519.</p>
GNSS deep SNR retrievals of marine atmosphere boundary layer (MABL) specific humidity
<p>This folder contains 5 prediction files ended with *_v2.h5. These files can be loaded using the provided code "prediction_data_loader.py". All variables are in their respective physical units.</p> <p>The *.tgz file contains the training and validation codes as well as sample training and validation datasets from METOP-B satellite. Please refer to the paper for details. All variables had been normalized in the training and validation datasets so no real physical meaning attached.</p> <p>Reference:</p> <p><a href="https://publications.copernicus.org/">Gong, J., Wu, D. L., Badalov, M., Ganeshan, M., and Zheng, M.: A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals, Atmos. Meas. Tech., 18, 4025–4043, https://doi.org/10.5194/amt-18-4025-2025, 2025.</a></p> <p> </p> <p>POC: Jie.Gong@nasa.gov</p> <p>10/17/2024</p> <p> </p> <p>Update on 08/27/2025: The final paper has been published on AMT. Please see updated reference information above.</p> <p>--- THE END ---</p>
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
Data used in "Climatology and variability of air mass transport from the boundary layer to the Asian monsoon anticyclone"
<p>The data presented here are needed to reproduce the analyses of the publication: Nützel, M., Brinkop, S., Dameris, M., Garny, H., Jöckel, P., Pan, L. L., and Park, M.: Climatology and variability of air mass transport from the boundary layer to the Asian monsoon anticyclone, Atmos. Chem. Phys., 22, 15659–15683, https://doi.org/10.5194/acp-22-15659-2022, 2022. A short explanation of the archived data is presented in the accompanying README.</p> <p>Note: In the previous data set version (<a href="https://doi.org/10.5281/zenodo.7275804">10.5281/zenodo.7275804</a>) one file was missing and is added here.</p> <p> </p> <p> </p>
Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)
<p>This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.</p>
Map-based stochastic simulation data of a transient Ekman boundary layer
<p><strong>Overview</strong><br> <br> A journal paper in Advances in Science and Research [1] details the numerical modeling approach used to create the data. Here, the model input files, the raw data, processed data, and plot scripts are provided that support the research.</p> <p>The code used here [2,3] is an extended version of the one-dimensional turbulence (ODT) model [4,5]. The current model implementation utilizes an adaptive grid that further increases numerical efficiency [6.7]. A truncated version of the adaptive ODT code of this work is described in [8] and publicly available free of charge in [9].</p> <p>The theoretical foundation and numerical as well as experimental evidence for this work is given in [10,11,12,13], and the main motivation in [14].</p> <p>The bash script makePlot.sh is the top-level driver and contains all additional information about the cases. Some other Details are provided by low-level README files. Python-3.8 is required to run the scripts.</p> <p><strong>References</strong></p> <p>[1] M. Klein, and H. Schmidt. Capturing features of turbulent Ekman–Stokes boundary layers<br> with a stochastic modeling approach. <em>Adv. Sci. Res,</em> <strong>20</strong>, 55–64, https://doi.org/10.5194/asr-20-55-2023, 2023.</p> <p>[2] M. Klein, and H. Schmidt. Exploring stratification effects in stable Ekman boundary layers using a stochastic one-dimensional turbulence model, <em>Adv. Sci. Res.</em>, <strong>19</strong>, 117–136, https://doi.org/10.5194/asr-19-117-2022, 2022.</p> <p>[3] M. Klein, and H. Schmidt. A stochastic modeling strategy for intermittently unstable Ekman—Stokes boundary layers, <em>Proc. Appl. Math. Mech.</em>, <strong>20</strong>, e202000127, https://doi.org/10.1002/pamm.202000127, 2020.</p> <p>[4] A. R. Kerstein. One-dimensional turbulence: Model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows, <em>J. Fluid Mech.</em>, <strong>392</strong>, 277–334, https://doi.org/10.1017/S0022112099005376, 1999.</p> <p>[5] A. R. Kerstein, and S. Wunsch. _Simulation of a stably stratified atmospheric boundary layer using one-dimensional turbulence, <em>Boundary-Layer Meteorol.</em>, <strong>118</strong>, 325–356, https://doi.org/10.1007/s10546-005-9004-x, 2006.</p> <p>[6] D. O. Lignell, A. R. Kerstein, G. Sun, and E. T. Monson. Mesh adaption for efficient multiscale implementation of one-dimensional turbulence, <em>Theor. Comput. Fluid Dyn.</em>, <strong>27</strong>, 273–295, https://doi.org/10.1007/s00162-012-0267-9, 2013.</p> <p>[7] D. O. Lignell V. B. Lansinger, J. Medina, M. Klein A. R. Kerstein, H. Schmmidt, M. Fistler, and M. Oevermann. One-dimensional turbulence modeling for cylindrical and spherical flows: model formulation and application, <em>Theor. Comput. Fluid Dyn.</em>, <strong>32</strong>, 495–520, https://doi.org/10.1007/s00162-018-0465-1, 2018.</p> <p>[8] V. B. Stephens, and D. O. Lignell. One-dimensional turbulence (ODT): Computationally efficient modeling and simulation of turbulent flows, <em>Software X</em>, <strong>13</strong>, 100641, https://doi.org/10.1016/j.softx.2020.100641, 2021.</p> <p>[9] BYU Ignite. Adaptive ODT source code, https://github.com/BYUignite/ODT.</p> <p>[10] S. Salon, and V. Armenio. A numerical investigation of the turbulent Stokes–Ekman bottom boundary layer, <em>J. Fluid Mech.</em>, <strong>684</strong>, 316–352, https://doi.org/10.1017/jfm.2011.303, 2011.</p> <p>[11] M. Klein, T. Seelig, M. V. Kurgansky, A. Ghasemi V., I. D. Borcia, A. Will, E. Schaller, C. Egbers, and U. Harlander. Inertial wave excitation and focusing in a liquid bounded by a frustum and a cylinder, <em>J. Fluid Mech.</em>, <strong>751</strong>, 255–297, https://doi.org/10.1017/jfm.2014.304, 2014.</p> <p>[12] A. Ghasemi, M. Klein, A. Will, and U. Harlander. Mean flow generation by an intermittently unstable boundary layer over a sloping wall, <em>J. Fluid Mech.</em>, <strong>853</strong>, 111–149, https://doi.org/10.1017/jfm.2018.552, 2018.</p> <p>[13] M. Vincze, N. Fenyvesi, M. Klein, J. Sommeria, S. Viboud, and Y. Ashkenazy. Evidence for wind-induced Ekman layer resonance based on rotating tank experiments, <em>EPL</em>, <strong>125</strong>, 44001, https://doi.org/10.1209/0295-5075/125/44001, 2019.</p> <p>[14] L. S. Freire. Large-eddy simulation of the atmospheric boundary layer with near-wall resolved turbulence, <em>Boundary-Layer Meteorol.</em>, <strong>184</strong>, 25–43, https://doi.org/https://doi.org/10.1007/s10546-022-00702-z, 2022.</p> <p> </p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Turbulent Mechanisms for the Deep Convective Boundary Layer in the Taklimakan Desert
Open the record for dataset details and reuse information.
Data from: Turbulence organization and mean profile shapes in the stably stratified boundary layer: Zones of uniform momentum and air temperature
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SGS-LTER GIS layer with detailed information on pasture boundaries on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on CPER Boundary on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
Experimental and numerical data of an article on the prediction of wind load on pylons for a neutral atmospheric boundary layer ow over two successive hills
<p>his deposit contains experimental and numerical data related to the article entitled "Towards prediction of wind load on pylons for a neutral atmospheric boundary layer flow over two successive hills" that will be publised in Journal of Wind Engineering & Industrial Aerodynamics. It consists in post-processed experimental data (turbulence statistics) for flows around 2D hills, measured by PIV, and numerical date of k-epsilon simulations of the same configirations. All data are provided in matlab metadata format. Matlab functions are given to plot all the figures of the article.</p>
Cyclic tensile-compressive tests on thin concrete boundary elements with a single layer of reinforcement prone to out-of-plane instability
<p>The growing need for residential housing in Latin American countries has led to the construction of reinforced concrete buildings with wall thicknesses as low as 8-10 cm. Such walls have typically only a single layer of vertical rebars and are therefore particularly susceptible to out-of-plane failure. To investigate the response of the corresponding wall boundary elements, twelve reinforced concrete columns with a single layer of vertical rebars were tested under tension-compression cycles. The objective of this test series was to gain insights into parameters triggering wall instability and out-of-plane failure. The experimental tests investigate the effect of thickness, reinforcement ratio, and eccentricity of the longitudinal rebars with respect to the element axis. This paper summarises the results of the test campaign. The specimen response is analysed at the global and local level, and the influence of the crack pattern on the out-of-plane response of the column and the conditions leading to out-of-plane failure are described. Furthermore, the differences between members with a single layer of vertical rebars to members with two layers are discussed. The influence on the response of the parameters analysed in the experimental campaign is addressed, showing that section with small thickness and large reinforcement content are more prone to out-of-plane failures. Finally, the predictions of existing models are compared to the new experimental data. The entire data set is publically available.</p>
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.