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20 results for “atmospheric turbulence”
Datasets for: Generalizing Monin-Obukhov Similarity Theory (1954) for Complex Atmospheric Turbulence, Stiperski and Calaf 2023, PRL
<p>Scaling variables for the generalized flux-variance scaling relations that include turbulence anisotropy. Dataset is a companion to the manuscript Stiperski, I., Calaf, M., 2023: Generalizing Monin-Obukhov similarity theory (1954) for complex atmospheric turbulence. Physical Review Letters, 130 (12), 124001, https://doi.org/10.1103/PhysRevLett.130.124001</p> <p>The dataset contains the turbulence statistics from 13 datasets: AHATS, Cabauw, CASES-99, METCRAX II campaign (NEAR and RIM towers), T-Rex campaign (Central tower - TRexC, West tower - TRexW) and i-Box measurement network (CCS-VF0 tower - i-Box0, CS-SF1 tower - i-Box1, CS-NF10 tower - i-Box10, CS-NF27 tower - i-Box27, CS-MT21 tower - i-BoxTop, im Hinteren Eis tower - imHint).</p> <p><br>Data are organized in csv files for each datasets and only contain high quality (for applied criteria see the Supplemental Material of the companion paper, https://journals.aps.org/prl/supplemental/10.1103/PhysRevLett.130.124001) data with 30 min averaging for unstable stratification and 1 min for stable stratification. Since the data were used for scaling, there is no reference to time, but the measurement height is provided as an additional variable. </p> <p>Meaning of variables:</p> <p>zeta - z/L where z is height above ground and L is the local Obukhov length</p> <p>SigmaU - $\overline{u'u'}/u_*$ scaled standard deviation of streamwise velocity, where $u_*$ is the local friction velocity</p> <p>SigmaU - $\overline{v'v'}/u_*$ scaled standard deviation of spanwise velocity</p> <p>SigmaU - $\overline{v'v'}/u_*$ scaled standard deviation of surface-normal velocity</p> <p>SigmaT - $\overline{T'T'}/T_*$ scaled standard deviation of sonic temperature, where $T_*$ is the local temperature scale</p> <p>SigmaEpsU - scaled dissipation rate of the streamwise velocity</p> <p>SigmaEpsW - scaled dissipation rate of the surface-normal velocity </p>
Auralization of virtual microphone array sensors considering coherence loss by atmospheric turbulence for two moving monopole sources
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Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread
<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
Data Sets: Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer
<p>Date of data analysis: Statistical analyses conducted throughout the 2023 year </p><p>Information about funding sources that supported the collection of the data:</p><p>The research was supported by the Cooperative Institute for Modeling the Earth System at Princeton University under Award NA18OAR4320123 from the National Oceanic and Atmospheric Administration, and by the US National Science Foundation under award number AGS 2128345. Also, it was supported by the National Defense Science and Engineering Graduate Fellowship from the U.S. Department of Defense and Army Research Office. Similarly, the National Science Foundation provided support to complete the PHOXMELT field studies (Grant PLR- 1417914) to collect the data. Also, the study was supported by the U.S. National Science Foundation (NSF-AGS-2028633) and the Department of Energy (DE-SC0022072).</p><p>The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration.</p><p>This dataset contains the observational data for the two field experiments (Barrow and Wendell) in .nc file format.</p>
Data of Atmospheric Turbulent Characteristics under Summer Shamal in Coastal Qatar
<p>Here are the data for the figures in the paper 'Atmospheric Turbulent Characteristics under Summer Shamal in Coastal Qatar'. The data are in '.mat' which can be easily processed through MATLAB.</p>
Dataset for large-scale self-organisation in dry turbulent atmospheres
<p>Datasets for all figures in "Large-scale self-organisation in dry turbulent atmospheres". The data comes from a simulation of the Boussinesq equations in a triply periodic domain of vertical height H and horizontal dimension L = 32H, in the presence of gravity, a stable mean density gradient, and solid body rotation in the vertical direction. Datasets are in TXT format except for two dimensional spectra, which are stored in NetCDF format.</p>
The simulated dataset associated with the paper "Mesoscale modelling of optical turbulence in the atmosphere: The need for ultrahigh vertical grid resolution"
<p>The WRF model-generated meteorological profiles are available in netcdf format. More information will be provided shortly. </p>
Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 1)
<p>Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 1). For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 2)
<p>Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 2). For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Atmospheric turbulence distorted video sequence dataset
<p>This contains the full version of the dataset utilized in our paper "Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning". Three main types of data are covered, which include algorithm simulated data, physical simulated data and real-world data. Specifically, the algorithm/physical simulated sequences are given with reference without turbulence distortion.</p>
Varying partitioning of surface turbulent fluxes regulates temperature-humidity dissimilarity in the convective atmospheric boundary layer
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Huang, and Xiao 2021. Please refer to the manuscript for the detailed description of the dataset.</p>
Identification of non-turbulent motions for enhanced estimation of turbulent transport using the anisotropy of atmospheric Turbulence
Open the record for dataset details and reuse information.
Raw data for "Overestimation of closed-chamber soil CO2 effluxes at low atmospheric turbulence"
<p>The LI-COR chamber raw data and the meteorological data used in for the paper Overestimation of closed-chamber soil CO2 effluxes at low atmospheric turbulence. </p>
Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor"
<p>Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor". For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Atmospheric Turbulence Image Dataset
<p>You can use this dataset for atmospheric turbulence mitigation studies</p>
Atmospheric Turbulence Dataset.
<h2><strong>This dataset comprises 38 video sequences that contain various levels of atmospheric turbulence.</strong></h2> <h3>Paul Hill, The University of Bristol</h3> <div>Please contact Paul Hill for any further information </div> <div>Paul.Hill@bristol.ac.uk</div> <h3>Sequences</h3> <ul> <li>The sequences were captured to give a range of</li> <li>Distances</li> <li>Turbulence levels</li> <li>Content (mainly aligning with discussed content of interest e.g. vehicles, buildings, roads etc.)</li> <li>The camera used was a Cannon R5, with a Canon 100-400mm zoom lens</li> <li>The sequences were captured over a period of three days in different locations</li> </ul> <h3>Preprocessing</h3> <ul> <li>All the sequences were cropped in time to. </li> <li>Get manageable / easily processable sequences of length of approximately 20 seconds</li> <li>To remove unwanted artefacts (hands in front of lenses, people walking through the shot etc.)</li> <li>Cropped referred (in the Excel file) that they were spatially cropped from higher resolution sequences (to focus on more interesting / coherent content)</li> </ul> <div> </div> <h3>Sequence Descriptions</h3> <div> <ul> <li>The included Excel file: Heathaze_datasets_descriptions_2024.xlsx gives a detailed descriptiong each sequence.</li> </ul> </div> <div>Directory Structure</div> <div>.</div> <div>├── CLEAR1 # Static PNG image results of the CLEAR1 method</div> <div>├── CLEAR2 # Example AVI CLEAR2 sliding window processed sequences</div> <div>├── Original_Seqs # All Original Sequences in MP4 format</div> <div> │ └── PNGs # The raw original sequences in directories named after the MP4 seq files. </div> <div> </div> <ul> <li>CLEAR1: Region and Pixel refer to the pixel by pixel and region-based fusion defined in the original CLEAR paper: Anantrasirichai et al. "Atmospheric Turbulence Mitigation Using Complex Wavelet-Based Fusion," Image Processing, IEEE Transactions on , vol.22, no.6, pp.2398-2408, June 2013 </li> <li>CLEAR2: The outputs of an example CLEAR2 method output on the input sequences: Anantrasirichai et al. Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion", ICIP, pp. 2895-2899 2018.</li> <li>Original_Seqs: The PNGs are numbered in temporal order in each directory (named as per the MP4 seq files).</li> </ul> <p> </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>
Data - Sensitive optical free-space receiver architecture for coherent combining of deep-space communication signals through atmospheric turbulence
<p>This dataset contains simulation code (MATLAB), generated fiber-field data and plotting scripts to generate the results in "Sensitive optical free-space receiver architecture for coherent combining of deep-space communication signals through atmospheric turbulence". Measured data and noise signal generation scripts are also included.</p> <p>This is version 2 of the dataset which features minor changes to make the scripts simpler to run (compared to the previous version) as well as detailed instructions on how to run them and generate the figures from the paper (see README.txt).</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
High frequency wind recored from sonic anemometers: Impact of swell waves on atmospheric surface turbulence
<p>Wind-wave interaction affects the fluxes of exchange processes across the air-sea wavy interface. For example, several measurements and modelling experiments have suggested that during the swell waves the wind shear and veer may significantly vary (depending on thermal stratification among other environmental factors). This dataset shows interesting wave energy penetration observed from sonic anemometer mounted at an offshore met-mast at 15m height above the mean sea level. More codes will be provided in Github</p> <p>https://github.com/MostafaBakhoda/JGRL\_2022\_WindWaveDecomposition.git</p> <p>The data are used in following submitted research:</p> <p>"Impact of swell waves on atmospheric surface turbulence: A wave-turbulence decomposition method"</p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.