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1,574 results for “atmospheres”
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Data of the paper: Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems
<p>Here you can find the data presented in the paper: <strong>Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems</strong></p> <p>The data include all variables included in the paper for the shallow-cloud and the deep-cloud dominated cases.</p> <p>The variable names are as in the paper (beside T_tot which is the 2m temperature). The numbers in the names of the variables represent the CDNC case.</p> <p>The time series variables are as a function of t. The vertical profiles are as a function of the pressure p. The maps are as a function of latitude and longitude. </p>
Collected data from the ATLASM5 Atmospheric Research Station from 01 January 2019 to 31 December 2019
<p>PI_NAME = Abdelwahid MELLOUKI<br> INSTITUTE = CNRS-ICARE<br> EMAIL=mellouki@cnrs-orleans.fr<br> ADDRESS=1C Av. de la Recherche Scientifique, CS 50060, 45071 Orleans cedex 2</p> <p>TITLE = Data from the ATLASM5 Research Station Year = 2019<br> DATA_CATEGORY=Field_ATLASM5 Station<br> Project=MARSU<br> TYPE_OF_DATA= FIELD MEASUREMENT<br> STATUS_OF_FILE=FINAL<br> VERSION=1.0<br> PLATFORM = ATLASM5<br> NAME_OF_PLATFORM=MARSU<br> DESCRIPTION=https://marsu-h2020.org/</p> <p> </p>
Supplemental material to manuscript "Enhancement of strength of adhesive bond between wood and metal using atmospheric plasma treatment"
<p>This dataset includes the supplementary information<strong> </strong>related to the manuscript:</p> <p>Authors: Žigon J, Kovač J, Zaplotnik R, Saražin J, Šernek M, Petrič M, Dahle S</p> <p>Title: <strong>Enhancement of strength of adhesive bond between wood and metal using atmospheric plasma treatment</strong></p>
A Machine-Learning-Based Global Atmospheric Forecast Model
<p>Data used in "A Machine-Learning-Based Global Atmospheric Forecast Model" 2020. Included in this dataset is the machine learning predictions and the truth for the year's worth of simulated forecast. </p>
Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1
<p>Data used in the work "Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1" - McNorton et al. (2020)</p> <p>All data generated using version 46R1 of the Integrated Forecast System based at the European Centre for Medium-Range Weather Forecasts, with work funded as part of the European Commission CO2 Human Emissions Project.</p> <p>Data includes global total standard errors for the total column CO2 mixing ratios at 3 hourly intervals for 2015 and both total column and surface transport errors at hourly intervals for January and July 2015, derived from a 50 member ensemble. It is suggested that the data are used by the inverse modelling community to account for transport model errors.</p> <p>Please view the README.txt file for a full description.</p> <p> </p> <p>###########################<br> ## EXPERIMENTAL SETUP ##<br> ###########################</p> <p># FLUXES #</p> <p>CHE-EDGAR-2015 EMISSIONS<br> CHE-TIER-2-FIRE/OCEAN<br> ONLINE CHTESSEL BIOGENIC FLUXES (FOR TRANSPORT ERROR THESE USE THE CONTROL MEMBER FLUXES)</p> <p># MODEL #</p> <p>IFS-CYCLE 46R1<br> RESOLUTION TCO399 (~25km)<br> 137 VERTICAL LEVELS<br> ALL DATA PROVIDED HERE ARE EITHER COLUMN INTEGRATED MIXING RATIO (XCO2) OR SURFACE (LEVEL 137)<br> ALL DATA PROVIDED HERE ARE STANDARD DEVIATION ACROSS 50 ENSEMBLE MEMBERS<br> </p>
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>
Near-global CFC-11 retrieved from Atmospheric Infrared Sounder (AIRS) from 2003 to 2018
<p>CFC-11 surface mole fractions were retrieved from monthly clear-sky nadir-view Atmospheric Infrared Sounder (AIRS) observations on 30-deg by 10-deg over 55S-55N. </p> <p>Dataset is available at https://github.com/Huang-Group-UMICH/CFC-11-retrievals-from-AIRS.</p>
Dataset of "Effects of latitude-dependent gravity wave source variations on the middle and upper atmosphere"
<p>This is a dataset for three 60-day simulations with the CMAT2-GCM for June-July 2010 conditions, using the Whole atmosphere gravity wave parameterization by Yiğit et al. (2008).</p> <p>Dimensions: 66 vertical levels, 24 longitudes, and 91 latitudes. The bottom level is at 100 mb. </p> <p>Variables: There are seven physical variables. Daily-averaged zonal wind, temperature, geopotential height, zonal drag, gravity wave total heating/cooling rate, gravity wave-induced temperature fluctuations, and gravity wave absolute momentum flux. </p> <p>Simulations: A benchmark run "00n", a run with latitude-dependent gravity wave source spectrum with 50% increased flux at the lower boundary in both hemispheres "51n"; same as "52n" but 100% increased flux in the Southern Hemisphere only. </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>
The oxygen isotope compositions of large numbers of small cosmic spherules: Implications for their sources and the isotopic composition of the upper atmosphere
<p>Cosmic spherules are micrometeorites that melt at high altitude as they enter Earth's atmosphere and their oxygen isotope compositions are partially or completely inherited from the upper atmosphere, depending on the heating experienced and the nature of their precursor materials. In this study, the <b>three oxygen isotope</b> compositions of 137 <b>cosmic spherules are</b> determined using 277 in-situ analyses by ion probe. Particles of each different type of cosmic spherule (scoriaceous, porphyritic, cryptocrystalline, barred, glass, calcium aluminium and titanium (CAT), G-type and I-type) in the diameter range ~52–480mm were analysed. The results confirm that the <b>three</b> oxygen isotope compositions of melted <b>micrometeorites</b> reflect a combination of their precursor composition, exchange with the atmosphere and mass fractionation owing to evaporation during entry heating. The data <b>appear to </b>reveal an increase in average δ<sup>18</sup>O values of silicate dominated (S-type) spherules in the series scoriaceous<porphyritic<barred<glass<CAT spherules (~20, 22, 25, 26 and 50‰) that is consistent with the evolution of oxygen isotopes by mass fractionation owing to increased average entry heating. The trend of δ<sup>17,18</sup>O is broadly parallel to the terrestrial fractionation line and thus suggests mass fractionation dominates changes in isotopic composition, with atmospheric exchange a less significant effect. The D<sup>17</sup>O values of spherules, therefore, are mostly preserved and suggest that ~80% of particles are related to the carbonaceous chondrites (CC) and are probably samples of C-type asteroids. The genetic relationships between different S-types can also be determined with scoriaceous, barred and cryptocrystalline spherules mostly having low D<sup>17</sup>O values <b>(≤0‰)</b> suggesting they are mainly derived from CC-like sources, whilst porphyritic mostly have positive D<sup>17</sup>O <b>(>0‰)</b> suggesting they are largely from ordinary chondrite (OC)-like sources related to S(IV)-type asteroids. Glassy and CAT-spherules have D<sup>17</sup>O values <b>indicating</b> they formed by intense entry heating of both CC and OC-like materials. I-type cosmic spherules have a narrow range of δ<sup>17</sup>O (~20–25‰) and δ<sup>18</sup>O (~38–48‰) values, with D<sup>17</sup>O (~0‰) <b>suggesting</b> their oxygen is obtained entirely from the Earth's atmosphere, albeit with significant mass fractionation owing to evaporation during entry heating. The observed range of δ<sup>18</sup>O with the size is suggested here to reflect entry angle with high values representing enhanced heating at high angle. Finally, G-type <b>cosmic spherules</b> have unexpected isotopic compositions suggesting little mass-fractionation from a CC-like source and are suggested to have sulphide-silicate precursors with relatively low melting temperatures. The results of this study provide a <b>vital assessment</b> of the <b>wider</b> population of extraterrestrial dust arriving <b>at the</b> Earth.</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>
Adjacency matrices of global atmospheric moisture networks during 2007-2016
<p>This dataset provides the adjacency matrices used to run Infomap to conduct community detection of global atmospheric moisture networks. A detailed description of file naming convention, dimension information and how to read the files can be found in 'Readme.txt'.</p>
Closure Study on Hygroscopic Properties of Water-soluble Matter in Atmospheric PM2.5 at a Rural Site in Northwest China
<p>The data supports the manuscript entitled “<strong>Closure Study on </strong><strong>Hygroscopic Properties</strong> <strong>of Water-soluble Matter in Atmospheric PM<sub>2.5</sub> at a Rural Site in Northwest China”</strong>. The data can be used freely for scientific purposes with the appropriate citation.</p>
Data and Software accompanying "Slope-aspect induced climate differences influence how water is exchanged between the land and atmosphere", submitted to JGR: Biogeosciences August 2020
<p>This is a collection of all of the data files and python notebooks used in creating the manuscript "Slope-aspect induced climate differences influence how water is exchanged between the land and atmosphere", submitted to JGR: Biogeosciences August 2020. </p>
Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2
<p>The dataset is the output of CESM that is used in the paper "Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2" submitted to <em>Geophysical Research Letters</em>.</p>
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>
Data from: Partitioning between atmospheric deposition and canopy microbial nitrification into throughfall nitrate fluxes in a Mediterranean forest
1. Microbial activity plays a central role in nitrogen (N) cycling, with effects on forest productivity. Though N bio-transformations, such as nitrification, are known to occur in the soil, here we investigate whether nitrifiers are present in tree canopies and actively process atmospheric N. 2. This study was conducted in a Mediterranean holm oak (Quercus ilex L.) forest in Spain during the transition from hot dry summer to cool wet winter. We quantified NH4+—N and NO3-—N fluxes for rainfall (RF) and throughfall (TF) and used δ15N, δ18O, and Δ17O to elucidate sources of NO3-. Finally, we characterized microbial communities and abundance of nitrifiers on foliage, RF and TF water through metabarcoding and quantitative Polymerase Chain Reaction, respectively. 3. NO3—N fluxes at the site were larger in TF than RF, suggesting a contribution from dry deposition, as also supported by δ15N and δ18O. However, Δ17O indicated that about 20% of NO3- in TF derived from canopies nitrification in August, after a severe drought, with a lower proportion in September (≈ 8%). This seasonal partitioning between biologically and atmospherically derived NO3- coincided with a decreasing trend of the abundance of archaeal nitrifiers. Tree canopies and TF had more diverse microbial communities than RF. Yet, RF showed higher variability in microbial composition, likely associated to the origin of air masses. 4. Synthesis. Atmospheric N deposition is significantly altered after passing through tree canopies. While nitrification has been proposed as one of the mechanisms responsible for these changes, very few studies directly investigate its occurrence. Here, we showed that nitrification by epiphytic leaf microbes contributed to increasing NO3 in TF and that nitrifiers' activity was reduced going from the dry and hot summer to the cool winter. Overall, these results highlight the power of coupling microbial community analysis, functional gene amplification and stable isotope approaches to examine ecosystem-scale processes.
Data for "Aerosol invigoration of atmospheric convection through increases in humidity"
<p>Codes, simulation input files, and simulation output data supporting “Aerosol invigoration of atmospheric convection through increases in humidity”. Enclosed README files provide detailed descriptions of the archive contents.</p>
Atmosphere of HAT-P-41b Revealed - Supplemental Figures
<p>Supplemental Figures for 'Into the UV: The Atmosphere of the Hot Jupiter HAT-P-41b Revealed" (Lewis et al. 2020, ApJL 902:L19).</p> <p><br> Contains retrieval posteriors, spectra, abundances, comparisons between retrieved pressure-temperature profiles, and the lightcurve analysis for Hubble's WFC3 G141 data of HAT-P-41b. These figures support the analysis of HAT-P-41b's atmosphere presented in the main manuscript.</p>
ScienceDex guides
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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.