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535 results for “dust”
GPR data collected near the Chepeta Weather Station and the DUST-1 sampler, Uinta Mountains, Utah
Ground penetrating radar data collected on September 9, 2021 in the Uinta Mountains at the Chepeta Remote Automated Weather Station (RAWS) and the DUST-1 passive dust sampler. Data were collected with a GSSI SIR-4000 control unit and a 350HS antenna connected to an Emlid Reach RS2 GPS receiver. Data files have been distance normalized and field-applied range gains have been removed before being exported in .sgy format. Files were also exported in .kml format for viewing the transect locations in Google Earth. Two long transects (780 feet each) were collected. The "West" transect passed to the west of the Chepta RAWS; the "East" transect passed to the east. The transects started at different points along the northern lip of the summit upland and came together at a common point at their southern ends. Marks were made in the data file every 60 feet while surveying; these marks were used to distance normalize the results. The system collected 334 scans/second with 512 samples/scan while surveying. Two 30-foot perpendicular transects were also surveyed (north to south, and west to east) with their intersection adjacent to a soil pit excavated to a depth of 92 cm. The location of the soil pit was noted in each transect with a mark near 16 feet. Data were used to evaluate spatial variations in the thickness of regolith overlying the bedrock beneath this gently sloping summit flat.
Concentrations of the rare earth elements (REE) and Thorium-232 (232Th) in glacial dust from the northern Gulf of Alaska region
Open the record for dataset details and reuse information.
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</p>
Model simulation data used in "Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54" (Beer et al., Geosci. Model Dev., 2020)
<p>This dataset contains the output and the namelist setups of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. (<em>Geosci. Model Dev.</em>, 2020).</p>
Supplement to : Accelerated Snow Melt in the Russian Caucasus Mountains After the Saharan Dust Outbreak in March 2018
<p>These datasets contains all the data used in Accelerated Snow Melt in the Russian Caucasus Mountains After the Saharan Dust Outbreak in March 2018 by Dumont et al., in Journal of Geophysical Research.<br> This dataset includes : Sentinel-2 cloud masks, snow depth measurements, snow surface impurity content estimated from Sentinel-2, Sentinel-2 surface reflectances and digital elevation models.</p> <p>Dumont, M., Tuzet, F., Gascoin, S., Picard, G., Kutuzov, S., Lafaysse, M., et al. (2020). Accelerated snow melt in the Russian Caucasus mountains after the Saharan dust outbreak in March 2018. Journal of Geophysical Research: Earth Surface, 125, e2020JF005641. <a href="https://doi.org/10.1029/2020JF005641">https://doi.org/10.1029/2020JF005641</a></p>
Data Set of Interviews with Industry Representatives on Smart Dust
<p>Data Set of Interviews with Industry Representatives on Smart Dust</p> <p>This data set comprises the following documents:</p> <ul> <li>Coding scheme</li> <li>Model development</li> <li>Interview guideline</li> </ul>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (1/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file with the name starting 'data' contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) (unit: hPa) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T) (unit: K), zonal wind velocity (u) (unit: m/s), meridional wind velocity (v) (unit: m/s) and vertical wind velocity (w) (unit: m/s), in snapshots of every 1/6 Sol for the periods of 30 degrees in Ls per a file as described below. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020), which is based on the observed dust opacity in Mars Year 24 (MY34).</p> <p>data180rdc-my34.tar.xz: for Ls=180-210 (49 Sols)</p> <p>data210rdc-my34.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc-my34.tar.xz: for Ls=240-270 (46 Sols)</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p>The file 'flux61ls5-my34.tar.xz' contains the three-dimensional fluxes and physical parameters calculated from the model output with the MY34 dust scenario. The contents are (T')^2, (u')^2, (v')^2, u'v', u'w', v'w' T(bar), u(bar), v(bar), squared Brunt-Vaisala frequency, and geopotential height. (bar) denotes the sum of the total wavenumber s=0-60 components, and the dash denotes the deviation from (bar), i.e. sum of the total wavenumber s=61-106 components. There are 36 time grids between Ls=182.5 and Ls=357.5 with the step of Ls=5 degrees. Kinetic and potential energies can be derived from these values using the formulae in the paper.</p> <p>The file 'flux61ls5-lowdust.tar.xz' is the same as 'flux61ls5-my34.tar.xz', except the model output with the 'low-dust' scenario (Kuroda et al., 2019; Kuroda, 2019a, 2019b).</p> <p>The file 'scripts.zip' contains the FORTRAN scripts to derive the fluxes and physical parameters equivalent to the file 'flux61ls5-my34.tar.xz' from the model outputs in this dataset and Kuroda (2020), i.e. data180rdc-my34.tar.xz, data210rdc-my34.tar.xz, data240rdc-my34.tar.xz, data270rdc-my34.tar.xz, data300rdc-my34.tar.xz and data330rdc-my34.tar.xz. Also, the fluxes and physical parameters equivalent to the file 'flux61ls5-lowdust.tar.xz' can be derived with those scripts from the model outputs data180rdc.tar.xz, data210rdc.tar.xz, data240rdc.tar.xz, data270rdc.tar.xz, data300rdc.tar.xz and data330rdc.tar.xz which are available in Kuroda (2019a, 2019b).</p>
Global High Resolution Dust Emission Inventory for Chemical Transport Models
<p><strong>Overview:</strong><br> ==================================================================================</p> <p>Offline dust emissions in 2016 are now available at 0.25° x 0.3125° resolution. This dataset is calculated using the native resolution <a href="http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-FP">GMAO meteorology (GEOS-FP) fields</a>. </p> <p>Codes and Instructions (README file in the GitHub repository) to generate these offline emissions can be found on <a href="https://github.com/Jun-Meng/geos-chem/tree/v11-01-Patches-UniCF-vegetation">GitHub</a>.</p> <p>The offline emissions in this database have no scale factor applied, so users should apply the required scale factor in their application. Suggested scale factor to make the global total annual dust emission to 2000 Tg is 5.7141e-4. </p> <p><br> <strong>Zip File Details:</strong><br> ===============================================================================</p> <p>2016.zip contains daily (366 in total) netCDF files (stored in monthly folders) of global gridded hourly mineral dust emission flux rate. </p> <p> </p> <p>Individual file: </p> <p>/YYYY/MM/dust_emissions_025x0.3125.YYYYMMDD.nc</p> <p> Resolution : 0.25 x 0.3125 grid (721 x 1152 boxes)<br> Units : kg m-2 s-1<br> Timestamps : Hourly, 2016<br> Compression : Level 1 (nccopy -d1)<br> Chunking : nccopy -c lon/1152,lat/721,time/24</p> <p> </p> <p>Variables in each file: </p> <p>EMIS_DST1, EMIS_DST2, EMIS_DST3 and EMIS_DST4 represent dust emission flux rate in four size bins (0.1-1.0, 1.0-1.8, 1.8-3.0, and 3.0-6.0 micro in radius). </p> <p> </p> <p>*<em>Version 2020_v1.0 of this dataset was produced to accompany the following manuscript:<br> Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted</em></p>
Dust source activation frequency dataset
<p>The data set contains information on the frequency of dust emission events, named dust source activation (DSA), on a regular longitude-latitude grid of 1°x1° resolution. The frequency is defined as number of observed DSA during the period March 2006 to February 2010. </p> <p>Dust source activation events are inferred from 15-minute MSG SEVIRI IR dust index images as described in Schepanski et al. (2007, 2009) by back-tracing dust plumes to their place of origin. </p> <p>As discussed in Schepanski et al. (2007, 2009, 2012), the DSA data provides information on the location of dust sources and their relevance.</p>
Toward a Physics based Model of Hypervelocity dust Impacts,
<p>Data used in the preparations of certain figures of the manuscript</p>
Spectral data used in the paper "Intense Zonal Wind in the Martian Mesosphere During the 2018 Planet-Encircling Dust Event Observed by Ground-based IR Heterodyne Spectroscopy"
<p>This data contains the spectral data used in the paper "Intense Zonal Wind in the Martian Mesosphere During the 2018 Planet-Encircling Dust Event Observed by Ground-based IR Heterodyne Spectroscopy". </p> <p>"MILAHI_2018PEDE_Spectral_Data" is the spectral data and you can find the detailed information of the data in "README".</p> <p> </p> <p> </p>
Assessing sedimentary detrital Pb isotopes as a dust tracer in the Pacific Ocean
<p>Dataset in Support of Manuscript "Assessing sedimentary detrital Pb isotopes as a dust tracer in the Pacific Ocean", in review at Paleoceanography and Paleoclimatology. Data includes Pb isotopes of detrital fractions from ocean sediments and source regions.</p>
The dust source points from the west coast of South Africa from 2000 to 2021
<p>This data set describes the location and land use of the dust source points from 2000 and 2021 on the west coast of South Africa from succulent Karoo shrubland, bare areas, and dry pans. The data has been derived daily from MODIS images and the land cover was determined by the South African National Land Cover (SANLC) provided by the Department of Forestry, Fisheries and the Environment.</p>
Data from "Compact Disks in a High-resolution ALMA Survey of Dust Structures in the Taurus Molecular Cloud"
<p>Continuum fits images for all disks in our ALMA Cycle 4 Taurus disk survey - see details in Long et al., 2018, ApJ, 869, 17 and Long et al., 2019, ApJ, 882, 49</p>
Spatial source contribution and interannual variation in deposition of dust aerosols over the Chinese Loess Plateau (Haugvaldstad et al 2024)
<h2>Spatial source contribution and interannual variation in deposition of dust aerosols over the Chinese Loess Plateau</h2> <h4>Description:</h4> <p>The data.zip file contains processed FLEXPART/FLEXDUST and ERA5 data. ERA5 is used for meteorological analysis. FLEXDUST is the dust emission inventory used for calculating the source contribution. Combined source contribution is separated for each receptor site. The "clay" suffix in the file name refers the fine dust size-bin, the "silt" suffix refers to the super-coarse size-bin. For more information look at the published manuscript. The MERRA2.zip contain the data used in the FLEXPART MERRA2 intercomparison.</p> <p>The complete 3hourly FLEXPART source contribution and Emissions sensitivity are available from the Norwegian research data archive (DOI: <a href="https://doi.org/10.11582/2023.00135">10.11582/2023.00135</a>). The simulated FLEXDUST 3 hourly dust emission are also available from the Norwegian research data archive (DOI: <a href="https://doi.org/10.11582/2023.00134">10.11582/2023.00134</a>)</p> <h4>Data Format:</h4> <p>NetCDF4, CSV</p> <h4>Usage Notes:</h4> <p>The content of the zip file is used to generate the figures shown in Haugvaldstad et al 2023. To processes and recreate the figures in the manuscript there is a provided docker image that can be pulled with the following command: <em>docker pull ovewh/thesisdocker:build1.1.8</em>. More info <a href="https://hub.docker.com/r/ovewh/thesisdocker">here. </a></p> <p>The following GitHub repositories are used to analyse and plot the data, if the docker image is used codes should be downloaded automatically. </p> <ul> <li>Snakemake workflow: <a href="https://github.com/MasterOnDust/AGU_JGR_CLP_SOURCE_WORKFLOW/tree/AGU-Haugvaldstad-et-al-2023">https://github.com/MasterOnDust/AGU_JGR_CLP_SOURCE_WORKFLOW/tree/AGU-Haugvaldstad-et-al-2023</a></li> <li>Python library with plotting functionality and analysis utilities: <a href="https://github.com/MasterOnDust/Thesis_toolbox/tree/AGU-Haugvaldstad-et-al-2023">https://github.com/MasterOnDust/Thesis_toolbox/tree/AGU-Haugvaldstad-et-al-2023</a></li> <li>Python library for trajectory analysis: <a href="https://github.com/MasterOnDust/flexpart_cluster/tree/AGU-Haugvaldstad-et-al-2023">https://github.com/MasterOnDust/flexpart_cluster/tree/AGU-Haugvaldstad-et-al-2023</a></li> <li>Python library for reading and post processing for FLEXPART and FLEXDUST raw output: <a href="https://github.com/MasterOnDust/DUST/tree/AGU-Haugvaldstad-et-al-2023">https://github.com/MasterOnDust/DUST/tree/AGU-Haugvaldstad-et-al-2023</a></li> </ul>
Orbital-controlled mid-latitude North Pacific dust flux during the late Quaternary
<p>Airborne mineral dust is sensitive to climatic changes, but its response to orbital forcing is still not fully understood. Here, we present a reconstruction of dust input to the Subarctic Pacific Ocean covering the past 190 kyr. The dust composition record is indicative of source moisture conditions, which are dominated by precessional variations. In contrast, the dust flux in marine sediments is dominated by obliquity variations, and display an out-of-phase relationship with a dust record from the mid latitude North Pacific Ocean. Climate model simulations suggest a precessional forcing likely affected the aridity and extent of the dust source regions. Meanwhile, the obliquity variations can be explained by meridional shifts in the North Pacific westerly jet, driven by changes in the meridional atmospheric temperature gradient. Our findings suggest that North Pacific dust input were primarily modulated by orbital-controlled source aridity and westerly during the late Quaternary.</p>
Data For "Impact of dust and temperature on primary productivity in Late Miocene oceans"
<p><span>Impact of dust and temperature on primary productivity in Late Miocene oceans</span></p> <p><span> </span></p> <p><span>This dataset contains marine biogeochemical outputs (NetCDF files) from modeling experiments with realistic late Miocene paleogeography, different CO2 levels and dust concentrations. The simulations focus on the evolution of primary productivity in response to aridification and global cooling. The simulations were carried out using the IPSL-CM5A2 general circulation model (Sepulchre et al. 2020 - IPSL-CM5A2 - an Earth system model designed for multimillennial climate simulations, GMD) and the PISCES-v2 biogeochemistry model (Aumont et al. 2015). It includes four simulations: Mio300Dust (300 ppm, dust concentration equal to pre-industrial level), Mio420Dust2 (420 ppm, dust concentration equal to pre-industrial level divided by 2), Mio420Dust10 (420 ppm, dust concentration equal to pre-industrial level divided by 10) and Mio420NoDust (420 ppm, dust concentration equal to pre-industrial level divided by 1000). The data are monthly averages over the last 100 years of the simulations. </span></p> <p><span> </span></p> <p><span>Contact: quentin.pillot@gmail.com</span></p> <p><span> </span></p> <p><span>Experiments , see Pillot et al. (2024), Methods ans supplementary Informations for details.</span></p> <p><span> </span></p> <p><span>INTPP : Vertically integrated primary production by phyto (mol/m2/s)</span></p> <p><span>EPC100 : Export of carbon particles at 100 m (mol/m2/s)</span></p> <p><span>LNlight : Light limitation term in Nanophyto (between 0 and 1)</span></p> <p><span>LNnut : Nutrient limitation term in Nanophyto (between 0 and 1)</span></p> <p><span>PPPHY : Primary production of nanophyto (mol/m3/s)</span></p> <p><span>PPPHY2 : Primary production of diatoms (mol/m3/s)</span></p> <p><span>Ndep : Nitrogen deposition from dust (mol/m2/s)</span></p> <p><span>Pdep : Phosphorus deposition from dust (mol/m2/s)</span></p> <p><span>Sidep : Silice deposition from dust (mol/m2/s)</span></p> <p><span>Irondep : Iron deposition from dust (mol/m2/s)</span></p> <p><span> </span></p> <p><span>Keywords: Late Miocene, marine primary productivity, aridification, dust, CO2, cooling, oceans, modelling, IPSL-CM5A2, PISCES-v2</span></p>
Shark-dust: Application of high-throughput DNA sequencing of processing residues for trade monitoring of threatened sharks and rays
<p>Data repository accompanying manuscript titled of "Shark-dust: Application of high-throughput DNA sequencing of processing residues for trade monitoring of threatened sharks and rays."</p> <p>Prasetyo, A. P., Murray, J. M., Kurniawan, M. F. A. K., Sales, N. G., McDevitt, A. D., & Mariani, S. (2023). Shark-dust: Application of high-throughput DNA sequencing of processing residues for trade monitoring of threatened sharks and rays. Conservation Letters, 16, e12971. https://doi.org/10.1111/conl.12971</p>
Dumps and Data for 'Impact of aeolian erosion on dust evolution in protoplanetary discs'
<div>This dataset contains the files required to recreate the Phantom simulations run in the publication Michoulier et al. 2024, A&A 686, A32.</div> <div>We have also included the dumps required to re-create Figure 4.</div> <div> </div>
Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert
<p>Deserts play an important role in the climate system, which is closely associated with the emission and transport of dust aerosols. Based on the intensive observation experiment in the Taklimakan Desert, the potential physical processes between the deep convective boundary layer (CBL) and dust emission are revealed in this study. Deep CBL enables the formation of clouds in the late afternoon, leading to significant cooling of surface. Large-scale buoyant coherent structures thereby transform into the mechanical coherent structures confined near the surface. The responses promote the earlier occurrence of low-level jet (LLJ) than in cloudless conditions, which allows the downward transport of LLJ momentum and substantially increases surface wind. Therefore, dust emission is initiated by strong wind at dusk and lasts for several hours. The results are useful to predict dust emissions and improve our understanding of distinctive boundary-layer processes in desert regions.</p>
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