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143 results for “Boundary layer”
Calibration of Absorbing Boundary Layers for Geoacoustic Wave Modeling in Pseudo-Spectral Time-Domain Methods
<p>Mesh.zip contains three folders each of them having seven meshes for each ABL. These data are required for reproducing TEST 3 which can be downloaded from https://github.com/carlosSpa/PSTD_ABL.git</p>
Dataset used in the paper "Verifying the parameterization of vertical eddy viscosity and diffusivity in the bottom boundary layer"
<p>This dataset is created from the data obtained over the continental shelf of the East China Sea in July 2020 during a cruise of the training ship Nagasaki-maru of Nagasaki University (NN055), and used in the manuscript entitled "Verifying the parameterization of vertical eddy viscosity and diffusivity in the bottom boundary layer" by Takahiro Endoh, Takuya Hirooka, and Yoshinobu Wakata, which will be submitted to Journal of Physical Oceanography.</p>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Planetary boundary layer height (PBLH) over the SGP
<p>PBLH is a critical parameter influencing weather phenomena, air quality, and various meteorological processes. However, accurately retrieving PBLH has been a challenging task due to limitations such as coarse temporal resolution and measurement drift in traditional radiosonde observations. To address these limitations, we have devised a lidar-based methodology that capitalizes on a newly developed algorithm for PBLH retrieval. This algorithm demonstrates enhanced capabilities in capturing diurnal fluctuations in PBLH compared to existing lidar-based methods (Su et al., 2020). In addition, we have refined this algorithm specifically for PBLH retrieval under cloudy conditions through a novel scheme (Su et al., 2022). To ensure data reliability, a quality-control process has been implemented to filter out questionable data points. Accompanying the dataset is a quality-control flag for ease of reference. It should be noted that we have assimilated all available radiosonde observations to provide a more robust estimate of PBLH, making the dataset valuable for a variety of related studies.</p> <p>Data for PBLH are collected between 07:00 and 19:00 Local Time and are expressed in meters. In the dataset, a Quality Control (QC) value of 0 signifies valid data. A QC value of -2 denotes data that are "Not a Number" (NaN), indicating missing or non-applicable information. A QC value of 2 flags problematic data that may require further scrutiny.</p>
Simulation performance of different planetary boundary layer schemes in WRF V4.3.1 on wind field over Sichuan Basin within "Gray zone" resolution
<p>Weather Research and Forecasting (WRF) model was <span>used</span> to <span>investigate the performance of different planetary </span>boundary layer <span>(PBL) </span>parameteri<span>z</span>ation schemes <span>on </span>simulat<span>ing</span> surface wind <span>fields over Sichuan Basin</span> at a spatial resolution of <span>0.33</span>km<span>.</span> <span>T</span>he <span>experiment</span> is based on <span>multi-</span>case stud<span>ies, so</span> 2<span>8 near-surface </span>wind events from 2021 to 2022 <span>were selected, and</span> <span>a</span> total of 112 <span>sensitivity </span>simulations were <span>carried out by</span> employing four common<span>ly used</span> <span>PBL </span>schemes: YSU, MYJ, MYNN2, and QNSE<span>, and</span> compared to observations.</p> <p>The mean 10 minutes observations of wind speed and direction during the study period from the Guanghan Airport are stored in one seperate txt file named OBS-28cases-every10min-10m-wind-Guanghan-Airoprt.txt.</p> <p>Wind field (U10, V10) at the central point from the WRF simulations of inner domain from all the PBL simulations are investigated, which are stored in txt format (.txt), and all the results are available from the first author(yuet@mail.iap.ac.cn).</p>
Boundary layer turbulence and abyssal recipes (BLT): Moored observations
Open the record for dataset details and reuse information.
Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert
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Data from: Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: results from the ultraparameterized CAM
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Gas phase acid, ammonia and aerosol ionic and trace element concentrations at Cape Verde during the Reactive Halogens in the Marine Boundary Layer (RHaMBLe) 2007 intensive sampling period
<p>The data files are in NASA Ames Format.</p> <p>A full description of the data set has been published in the journal<br> Earth System Science Data at http://www.earth-syst-sci-data.net/5/385.</p> <p> </p>
LMDZ6iso output - Antarctic water stable isotopes in the global atmospheric model LMDZ6: from climatology to boundary layer processes
<h3><strong>Update of the Simulation</strong></h3> <p dir="ltr">This archive contains the output of a revised simulation, addressing issues identified in the original simulation described in the following article:</p> <p><em>Dutrievoz, N., Agosta, C., Risi, C., Vignon, É., Nguyen, S., Landais, A., ... & Prié, F. (2025). Antarctic water stable isotopes in the global atmospheric model LMDZ6: From climatology to boundary layer processes. Journal of Geophysical Research: Atmospheres, 130(5), e2024JD042073.</em></p> <p>All information is available here:<strong> <a href="https://docs.google.com/document/d/1x4FSGtNiJGqsMoks38yATzTQJ7T8sO8iXqeWagTU5yE/edit?usp=sharing">README</a></strong></p> <p>Researchers and colleagues are welcome to contact me at <strong>niels.dutrievoz@lsce.ipsl</strong>.<strong>fr</strong> for any further information regarding this updated simulation or potential collaborations. Please feel free to reach out if you are interested in daily or hourly output data from this simulation.</p> <p dir="ltr">I look forward to any exchanges that might arise from this work.</p> <p> </p>
SCM files for "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization"
<p>Supporting files for manuscript "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization" by N.P. Arnold.</p> <p>The data consists of source code modifications relative to v11.2.0 of the GEOS GCM, modified boundary conditions for running single column model experiments, and netcdf model output from those experiments.</p>
Supporting data for "Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades" (previously for ch. 5 of "Mesoscale Cloud Patterns in the Trade-Wind Boundary Layer")
<p>This contains both the data and scripts required to produce the figures in the preprint "Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades". The scripts labeled 1-5 produce the main figures; the other scripts produce supporting data or figures.</p> <p>Earlier versions of this dataset contained the scripts and data supporting Ch. 5 of the PhD thesis "Mesoscale Cloud Patterns in the Trade-Wind Boundary Layer". The scripts labeled 1-5 produce the main figures; the other scripts produce either the underlying data, or supporting figures (prefix S). </p>
PIV vector fields from: Boundary layer hydrodynamics of patchy biofilms
<p>This dataset contains the instantaneous velocity vector fields from PIV data taken over large acrylic plates fouled with diatomaceous biofilm of varying patchiness. </p> <p>See associated article, Boundary layer hydrodynamics of patchy biofilms, for methods description.</p> <p>Each zip folder contains data for one of the 4 non-uniform biofilms examined, PB-1 (patchy biofilm 1); PB-2 (patchy biofilm 2); SB-1 (sparse biofilm 1); SB-2 (sparse biofilm 2). For each biofilm, the corresponding folder contains 4000 statistically independent instantaneous velocity vector fields. Each vector field is saved in a .mat file, and the workspace variable that contains the data is called ‘vecfield’. </p> <p>Size calibration and water temperature are provided in the spreadsheet ‘experiment_metadata.xlsx’</p> <p> </p> <p>Column 1: X (streamwise distance [pixels]) </p> <p>Column 2: Y (wall-normal distance from bottom of frame [pixels])</p> <p>Column 3: U (streamwise velocity vector [pixels / 250 microseconds]) </p> <p>Column 4: V (vertical velocity vector [pixels / 250 microseconds]) </p> <p>Column 5: CHC (number of tracked particles. A value < 1 gives the location of the biofilm, which was masked out)</p> <p> </p>
Data from the article "Modulation of wintertime canopy Urban Heat Island (CUHI) intensity in Beijing by synoptic weather pattern in planetary boundary layer"
<p>The link includes four datasets, "pcttype" is weather typing data, "pblh" is PBLH data, "uhii-UV" is the mean value of CUHII and wind direction UV of all urban stations, and "uhii-sws" is the value of CUHII, wind speed and wind direction of all urban stations.</p>
Dataset 2 "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021"
<p>This dataset provides additional profiles for the manuscript titled "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021". It is available for those who are interested.</p>
Data for "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression"
<p>The dataset for the manuscript titled</p> <p>"Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression".</p> <blockquote> <p>Oh, G. and Austin, P. H.: Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-352, 2024.</p> </blockquote> <p> </p> <p>The dataset contains input parameters used for the SAM LES model run (Khairoutdinov and Randall, 2003) based on the CGILS S2 parameters (CGILS.zip) and the main raw output used for the manuscript (clusters.zip). The latter contains a list of Parquet files for the 12-hour model run, sampled every minute. The raw output consists of the coordinates of all cloudy cells in the model domain, which is used to construct the cloud size distribution as described in the manuscript.</p>
Spatio-temporal Characterization of Coherent Structures in Simulated Tropical Cyclone Boundary Layer
<p>Jupyter-notebooks to read and analyze processed outputs from three Tropical Cyclone (TC) simulations (CONTROL, LOWOR, and NONPARAM) and plot figures. The datasets are large and can be made available by the authors upon request.</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>
DYAMOND-II Model Outputs for "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations"
<p>This dataset contains outputs from the DYAMOND Phase-II simulations, focusing on Global Storm-Resolving Models (GSRMs) at various Atmospheric Radiation Measurement (ARM) sites. The dataset supports the analysis presented in the manuscript "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations."</p> <p>Included are high-resolution model outputs from nine GSRMs, detailing simulations of atmospheric processes at six ARM sites, including variables of clouds, temperature, humidity, wind, and surface fluxes. This dataset allows for a direct comparison between GSRM simulations and ARM field observations, facilitating the evaluation of PBL-coupled and decoupled clouds. For further inquiries or assistance regarding the dataset, please contact the corresponding author at su10@llnl.gov.</p>
Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"
<p>Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.