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25 results for “Velocity Distribution”
Data: Dynamics of star clusters with tangentially anisotropic velocity distribution (Pavlik+ 2024)
<p>This dataset represents the results of our <em>N</em>-body simulations of star clusters (SCs). The initial conditions of the models are fully described in the referenced journal article. In short, the SCs start from isotropic, radially anisotropic or tangentially anisotropic initial velocity distributions, and each model is evolved in an external Galactic tidal field, for two different choices of the filling factor.</p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
Regional velocity anomalies and slip distribution of the 2023 Kahramanmaraş, Türkiye Earthquakes
<p>dvp.dat contains columns of longitude, latitude, depth (km), and dVp (%) relative to the 1-D reference model used in https://www.mdpi.com/2076-3263/11/2/91.</p> <p>dvs.dat contains columns of longitude, latitude, depth (km) and dVs (%) relative to the 1-D reference model used in https://www.mdpi.com/2076-3263/11/2/91.</p> <p>slip_model.csv contains columns of subfault node ID, UTM Zone 37 easting (m), UTM Zone 37 northing (m), depth (m), strike (o), dip (o), strike slip (m),dip slip (m) and associated primary fault (i.e. <span>Southern East Anatolian fault (SEAF) and Savrun-Çardak-Sürgü fault (SCSF)). A</span><span> positive strike slip refers to a right-lateral slip and a positive dip slip refers to a normal slip.</span></p>
Spatial distribution of random velocity inhomogeneities in the southern Aegean from inversion of S‐wave peak delay times - Dataset
<p>This dataset contains supplementary files uploaded as part of the above journal article.</p> <p>5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few<br> records contain negative peak delay times due to change in waveform shape from filtering, these<br> were excluded during further calculations. The other 4 sub-datasets contain files as well as the script<br> to generate the results of Δlog <em>t<sub>p</sub></em> , κ, ε<sub>param</sub> and P(<em>m<sub>l </sub></em>) as shown in figures 7, 8, 9 and 10 respectively<br> of the main article.</p> <p>Sub-Dataset S1: File “ds01.csv” contains the list of peak delay times (<em>t<sub>p</sub> </em>) in 2-4 Hz, 4-8 Hz, 8-16 Hz<br> and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent<br> origin time (in year-month-day’T’hour:minute:seconds.microseconds format), event latitude,<br> event longitude, event depth, station code, station latitude, station longitude, <em>t<sub>p</sub></em> in 2-4 Hz, <em>t<sub>p</sub></em> in 4-<br> 8 Hz, <em>t<sub>p</sub></em> in 8-16 Hz and <em>t<sub>p</sub></em> in 16-32 Hz in a sequential manner.</p> <p><br> Sub-Dataset S2: File “ds02.zip” contains four text files (nodes_24e.txt, nodes_48e.txt, and<br> nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt)<br> written in BASH. The text files contain Δlog <em>t<sub>p</sub></em> values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands<br> respectively. The columns in the text files represent node index, node latitude, node longitude,<br> node depth and Δlog <em>t<sub>p</sub></em> value in a sequential manner. The GMT script uses GSHHG coastline<br> data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ .<br> Once downloaded and extracted its path can be added to the variable “GDIR” at the beginning of<br> the script. The GMT script file can be run to see the spatial distribution of Δlog t p using GMT-5<br> (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S3: File “ds03.zip” contains four text files (kappa_f10.txt, kappa_f30.txt,<br> kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The<br> text files contain κ values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively.<br> The columns in the text files represent node latitude, node longitude and κ value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of κ using GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S4: File “ds04.zip” contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt,<br> aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain<br> ε<sub>param</sub> values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in<br> the text files represent node latitude, node longitude and ε<sub>param</sub> value of the node sequentially.<br> This GMT script also uses GSHHG coastline data whose path can be added to the script, same as<br> in data set S2 case. The GMT script file can be run to see the spatial distribution of ε<sub>param</sub> using<br> GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S5: File “ds05.zip” contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt,<br> psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain<br> psdf (P(<em>m<sub>l</sub></em><sub> </sub>)) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The<br> columns in the text files represent node latitude, node longitude and P(<em>m<sub>l</sub></em> ) value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of P(<em>m</em><sub><em>l </em></sub>) using GMT-5 (Wessel et al., 2013) and above.</p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
Laser Disdrometer Particle Size and Velocities Distributions Raw data (2014-2017) for Melbourne, Australia
<p>This dataset includes raw data obtained from Laser Disdrometers installed in the proximity of Melbourne, Australia. It includes data from 2 Thies LPM and 2 OTT Parsivel. The description of this dataset as well as a first step in the processing are described in Guyot et al. (under review) at Hydrology and Earth Sciences Systems (publisher EGU) under an open-source Discussion format. </p>
Distribution of halo peculiar velocities
<p>Example of the distribution of halo peculiar velocities in one reference (solid black histogram) and one BAM (solid blue and filled histogram) halo catalog, in different cosmic-web types.</p>
Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data"
<p>Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data</p> <p>", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Dataset of ``Plasma Distribution Solver: A Model for Field-Aligned Plasma Profiles Based on Spatial Variation of Velocity Distribution Functions"
<p>This dataset contains the plasma distribution data in the Jupiter–Io system, calculated from the Plasma Distribution Solver and used for figures in the paper “Plasma Distribution Solver: A model for field-aligned plasma profiles based on spatial variation of velocity distribution functions” by K. Saito et al. (2023).</p> <p> </p> <p>The contents of files ‘all_Case_1.csv’ and ‘all_Case_2.csv’ are as follows:</p> <ul> <li>Position along the magnetic field line (0 at the magnetic equator) [m] (column 1)</li> <li>Distance from the Jovian center [km] (column 2)</li> <li>Magnetic latitude [rad]([degree]) (column 3(4))</li> <li>Magnetic flux density [T] (column 5)</li> <li>The initial condition of electrostatic potential [V] (column 6)</li> <li>The result of electrostatic potential [V] (column 7)</li> <li>Number density profiles [m<sup>-3</sup>] (columns 8-17)</li> <li>Charge density profiles obtained from the integration of velocity distribution functions [C m<sup>-3</sup>] (column 18)</li> <li>Charge density profiles obtained from Poisson’s equation [C m<sup>-3</sup>] (column 19)</li> <li>Convergence value (column 20)</li> <li>Particle flux density [m<sup>-2</sup> s<sup>-1</sup>] (columns 21-30)</li> <li>Mean flow velocity parallel to the field line [m s<sup>-1</sup>] (columns 31-40)</li> <li>Plasma pressure perpendicular to the field line [Pa] (columns 41-50)</li> <li>Plasma pressure parallel to the field line [Pa] (columns 51-60)</li> <li>Plasma dynamic pressure [Pa] (columns 61-70)</li> <li>Perpendicular temperature [J] (columns 71-80)</li> <li>Parallel temperature [J] (columns 81-90)</li> <li>Alfvén speed considering the displacement current term in Ampère’s law [m s<sup>-1</sup>] (column 91)</li> <li>Alfvén speed per the speed of light (column 92)</li> <li>Ion inertial length using averaged mass [m] (column 93)</li> <li>Electron inertial length [m] (column 94)</li> <li>Ion Larmor radius using averaged mass [m] (column 95)</li> <li>Ion acoustic gyroradius using averaged mass [m] (column 96)</li> <li>Electron Larmor radius [m] (column 97)</li> <li>Current density [A m<sup>-2</sup>] (column 98)</li> </ul> <p>The Python codes ‘plot_all.py,’ ‘plot_plasma_beta_comparison.py,’ and ‘plot_Alfven_speed_comparison.py’ can plot Figures 5, 6, 7, and 9 of the paper using the above CSV files.</p> <p> </p> <p>The files ‘boundary_conditions_Case_1.csv’ and ‘boundary_conditions_Case_2.csv’ contain the boundary conditions for Cases 1 and 2.</p> <p> </p> <p>The zip files ‘probability_density_function_Case_1_H_Io.zip’ and ‘probability_density_function_Case_1_H_Jupiter_North.zip’ are zipped CSV files with the same name. The contents of these files are as follows:</p> <ul> <li>Magnetic latitude [degree] (column 1)</li> <li>Perpendicular velocity at the particle position [m s<sup>-1</sup>] (column 2)</li> <li>Parallel velocity at the particle position [m s<sup>-1</sup>] (column 3)</li> <li>Perpendicular velocity at the boundary [m s<sup>-1</sup>] (column 4)</li> <li>Parallel velocity at the boundary [m s<sup>-1</sup>] (column 5)</li> <li>Probability density function [s<sup>3</sup> m<sup>-3</sup>] (column 6)</li> <li>Differential flux per number density [cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> keV<sup>-1</sup>] (column 7)</li> </ul> <p>The Python code ‘plot_velocity_distribution_function.py’ can plot Figure 8 of the paper using this CSV file.</p>
Ion Velocity Distributions in Nine Thin Current Sheets Observed in the Magnetotail at Lunar Distances [Dataset]
<p>Each .zip archive contains ion velocity distribution data along with general plasma characteristics (e.g., magnetic field, plasma density, etc.). Ion velocity distributions are provided either as 1D reduced ion velocity distributions along the GSM y-direction over the entire time interval or as 2D cuts in the xz and xy planes at the current sheet center and in the lobes.</p>
Supplementary material for "Hybrid-Vlasov modelling of ion velocity distribution functions associated with the Kelvin-Helmholtz instability with a density and temperature asymmetry"
<p>Supplementary material for the article:</p> <p>"Hybrid-Vlasov modelling of ion velocity distribution functions associated with a density and temperature asymmetry" by</p> <p><strong>V. Tarvus</strong>, L. Turc, H. Zhou, T. Nakamura, A. Settino, K.Blasl, G. Cozzani, U. Ganse, Y. Pfau-Kempf, M. Alho, M. Battarbee, M. Bussov, M. Dubart, E. Gordeev, F. Tesema Kebede, K. Papadakis, J. Suni, I. Zaitsev and M. Palmroth</p> <p> </p> <p><strong>Supplementary video A</strong>:</p> <p>The development of the Kelvin-Helmholtz instability (KHI) in a purely transverse geometry (velocity shear perpendicular to the magnetic field), simulated using the hybrid-Vlasov model Vlasiator. The parameters shown are: Proton temperature (panel a), the non-Maxwellianity of the proton velocity distribution function (panel b), proton heat flux (panel c) and vorticity (panel d). A black contour in each panel shows the region where the magnitude of the proton temperature gradient is larger than the maximum gradient at the beginning of the simulation. Arrows in panel d) show the velocity field. The evolution of KHI proceeds from the formation of linear surface waves (t<50 Ω<sub>c,p</sub><sup>-1</sup>, with proton gyroperiod Ω<sub>c,p</sub><sup>-1</sup>) to the waves rolling up into vortices (t>50 Ω<sub>c,p</sub><sup>-1</sup>). Due to the steepening of the velocity shear layer, whose thickness tends towards the thermal proton Larmor radius, finite Larmor radius effects become active at the vortex edges, manifesting as enhanced non-Maxwellianity (panel b) and a heat flux (panel c), which originates from the temperature gradient according to the mechanism described by Braginskii (1965). At the end of the simulation (t=90-100 Ω<sub>c,p</sub><sup>-1</sup>), non-Maxwellianity increases also in the vortex interior, as protons from the two initial regions are mixed together.</p> <p> </p> <p><strong>Supplementary video B</strong>:</p> <p>The same as Supplementary video A, but with an added in-plane magnetic field of the form (<em>B</em><sub>0,z</sub>/5) tanh(x/a) <strong>y</strong>,<strong> </strong>where <em>B</em><sub>0,z</sub> is the magnitude of the background magnetic field perpendicular to the velocity shear. Analogous behavior is found compared to the simulation without an in-plane magnetic field (Supplementary video A), with the exception of the suppression of secondary instabilities by the added magnetic tension. This leads to less irregularities in the vortex structure during the non-linear stage (t>~50 Ω<sub>p</sub><sup>-1</sup>).</p>
Globally Distributed Subducted Materials along the Earth's Core-Mantle Boundary: Implications for Ultra-low Velocity Zones
<p>Supplemental movie files related to the geodynamic models in our manuscript "Globally Distributed Subducted Materials along the Earth’s Core-Mantle Boundary: Implications for Ultra-low Velocity Zones"</p>
Monitoring Spatiotemporal Seismic Velocity Changes Using Seismic Interferometry and Distributed Acoustic Sensing in Mexico City
<h2>Cross-Correlation Functions (CCFs) Data Files</h2> <p>The dataset consists of four zipped files:</p> <ul> <li> <p><strong>cc_25hz_das1_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das1_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> </ul> <h4> </h4>
Particle size and velocity distributions from an OTT Parsivel optical disdrometer located at Mario Zucchelli Station, aggregated to 5min, monthly netCDF archive
<p>Disdrometric data from an OTT Parsivel with 32 size classes and 32 velocity classes located at Mario Zucchelli Station (Antarctica, -74.6945 N, 164.1152 E, 15 m a.s.l.). Mid values and widths of the classes are provided. Spectra are sampled every 5 minutes (after merging five 1-minute measurements) and saved in monthly netCDF files. Note that this data has been processed regardless of precipitation type. Particle Size distribution (PSD) is calculated for every data entry. Monthly cumulated spectra and PSD are also provided.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/ed6ea5bf-3f59-467b-9914-f4db387bb55d">https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/ed6ea5bf-3f59-467b-9914-f4db387bb55d</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <p> </p> <h2>File "MZS_PAR_201612_5min.nc"</h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 32; <em>velocity </em>= 32; <em>time </em>= UNLIMITED; // (7488 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=7488); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=32); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=32); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=32); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=32); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=32, velocity=32, time=7488); :description = "Matrix of particle counts in each of the 32 diameter sizes and 32 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 32U, 32U, 1U; // uint float <em>PSD</em>(diameter=32, time=7488); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 32U, 1U; // uint double <em>monthlySpectrum</em>(diameter=32, velocity=32); :description = "Matrix of particle counts in each of the 32 diameter sizes and 32 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=32); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; // <strong>global attributes</strong>: :<em>title </em>= "OTT Parsivel disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 32 size classes and 32 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Dec 2016"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "OTT Parsivel disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "04-Nov-2024 15:40:53 UTC"; :<em>coverage </em>= "Monthly coverage (Dec 2016): 83.871 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw OTT telegram, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN.";</pre> <p> </p>
Azimuthal ion movement in HiPIMS plasmas - Part I: velocity distribution function
<p>Data affiliated to the submission of the research paper " Azimuthal ion movement in HiPIMS plasmas - Part I:<br> velocity distribution function" to the journal Plasma Sources Science and Technology. The data contains measurements, analytical calculations and computer simulation results.</p>
Data from: Nuclear magnetic resonance measurements of velocity distributions in an ultrasonically vibrated granular bed
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