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238 results for “Atmosphere modeling”
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>
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>
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>
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>
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>
Example 6-hour directory for FV3GFS atmospheric model
<p>This dataset serves as an example run directory for the FV3GFS atmospheric model, used in publications to show execution of the Python-wrapped FV3GFS atmospheric model.</p> <p>Files in rundir/grb provided by the National Oceanic and Atmospheric Administration (NOAA) Environmental Modeling Center (EMC) are public domain.</p> <p>All other data and configuration files are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License.</p> <p>SHiELD/FV3 model data and input files produced by the Geophysical Fluid Dynamics Laboratory is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
Data and plotting scripts used in "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"
<p>Raw performance data and plotting scripts used to generate the figures in the paper "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"</p>
Data and plotting scripts used in "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"
<p>Raw performance data and plotting scripts used to generate the figures in the paper "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"</p> <p>A previous version of this dataset (corresponding to an earlier revision of the paper) is available as https://doi.org/10.5281/zenodo.50533.</p>
The Sonora Substellar Atmosphere Models. IV. Elf Owl: Atmospheric Mixing and Chemical Disequilibrium with Varying Metallicity and C/O Ratios (Y- type Models)
<ul> <li><strong>Overview of V2: "The Sonora Substellar Atmosphere Models. V: A Correction to the Disequilibrium Abundance of CO2 for Sonora Elf Owl"</strong></li> </ul> <p> Version 2 of the Sonora Elf Owl Models updates the CO2 and PH3 abundances and spectra. As described in the Wogan et al. (2024) research note (URL OF NOTE GOES HERE), Version 1 of the models did not apply the CO2 quench approximation properly resulting in predicted CO2 abundances that were too small by several orders of magintude in some cases. Version 2 fixes this mistake, updating CO2 abundances and the emission spectra to reflect the new CO2 abundances. Version 2 also removes all spectra contributions of PH3 because Version 1 consistently contained too much PH3 absorption when compared to JWST data (Veiler et al. 2024, <a href="http://doi.org/10.3847/1538-4357/ad6759" target="_blank" rel="noopener noreferrer">http://doi.org/10.3847/1538-4357/ad6759</a>).</p> <ul> <li><strong>Overview of V1</strong></li> </ul> <p> The Sonora Elf Owl Models is a successor to the <a href="../records/5063476#:~:text=This%20particular%20set%20of%20model,g%20are%200.25%20or%200.5.">Sonora Bobcat</a> and <a href="../records/4450269">Sonora Cholla</a> models. The Sonora Elf Owl model grid includes cloud-free radiative-convective equilibrium model atmospheres with vertical mixing induced disequilibrium chemistry with sub-solar to super-solar atmospheric metallicities and Carbon-to-Oxygen ratio. The atmospheric models have been computed using the open-source radiative-convective equilibrium model <a href="https://natashabatalha.github.io/picaso/">PICASO</a>. The parameters included within this grid are effective temperature (<strong><em>Teff</em></strong>), gravity (<strong><em>log(g)</em></strong>), vertical eddy diffusion coefficient (<strong><em>log(Kzz)</em></strong>), atmospheric metallicity (<strong><em>[M/H]</em></strong>), and Carbon-to-Oxygen ratio (<strong><em>C/O</em></strong>).</p> <p>The ranges and increments of these parameters are described in the published paper.<br><br></p> <ul> <li><strong>Three grids available on three links</strong></li> </ul> <p><strong> The model grid has been presented using three Zenodo repositories. This repository has all the models between Teff of 275 to 550 K (applicable to Y-type objects). The models for Teff between 575 to 1200 K (applicable for T- type objects) are available in the Zenodo DOI :- <a href="../records/10385821">https://zenodo.org/records/10385821</a>. The models for Teff between 1300 to 2400 K (applicable for L- type objects) are available in the Zenodo DOI :- <a href="../records/10385987">https://zenodo.org/records/10385987</a>.</strong></p> <p> </p> <ul> <li><strong> File types and how to use them</strong></li> </ul> <p> The models have been presented in the Xarray format so that all the atmospheric properties including the T(P) profile, atmospheric chemistry, and thermal emission spectra can be accessed within the same files. A python based Jupyter notebook named "Reading and plotting Elf Owl Models.ipynb" has been also supplied which demonstrates how to open and use these files.</p> <ul> <li> <strong>Spectra</strong></li> </ul> <p> The emission spectra for each atmospheric model has been computed between 0.6 to 15 microns. The reported flux is in the units of erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here.</p> <div> </div> <div> <ul> <li><strong>Note on CH4</strong></li> </ul> </div> <p> As stated in <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...942...71M/abstract">Mukherjee et al. 2023 </a>our CH4 opacity is derived using the <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab7a1a">Hargreaves et al. 2020</a> HITEMP line list and computed using the HAPI code (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0022407315302466">Kochanov et al. 2016</a>). HAPI automatically pre-weights the isotopologues according to earth abundances that are listed on the HITRAN website (<a href="https://hitran.org/lbl/2?6=on" target="_blank" rel="noopener noreferrer">see here for CH4</a>). Therefore, users should note that there will be minor features of CH3D included in the models. Given the general absence of deuterated molecules in brown dwarfs (Teff>~300) we will include a second posting of models which includes the Elf Owl grid with <strong>only</strong> the major CH4 isotopologue (12C-H4).</p> <div> <ul> <li><strong>Note on PH3</strong></li> </ul> </div> <p> PH3 abundance is treated separately from the general disequilibrium scheme. This is because of the current non-detection of PH3 in many brown dwarf atmospheres (see citations in paper). The current PH3 treatment uses the chemical equilibrium treatment described in Visscher et al. However, after publishing this grid and using the model for analysis of high precision JWST data, we noticed that even the simple chemical equilibrium treatment which reduces the abundance, introduces a noticeable PH3 feature. Therefore in our v2 of this model grid we will further diminish the abundance.</p>
CFS model monthly mean diurnal cycles of ocean and atmosphere variables at TAO mooring locations
<p>v0.1.3</p> <p>cfsm501_ocn_2002_2006_TAOpoints_hourly.tgz -- contains netCDF files of hourly ocean variables: one ocean file per month over 4 years (2002-2006).</p> <p>Each file contains water temperature with dimensions (time, depth, lat, lon) at TAO locations. If joining multiple files together, concatenate along the time axis.</p> <p>-------------------------</p> <p>v0.1.2</p> <p>cfsm501_atmo_2002_2006_TAOpoints_3D_monthlyMeanDiurnalCycle.tgz -- contains netCDF files of monthly mean diurnal cycle of atmosphere variables: one atmosphere file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, hour, plev, lat, lon) at TAO locations. The dimension "time" is of length 1 in all files. If joining multiple files together, concatenate along the time axis. The "hour" dimension represents the 24 hours of the diurnal cycle.</p> <p>Note that this version of the data has NOT had the 3-day high pass filter applied before the diurnal cycle calculation.</p> <p>-------------------------</p> <p>v0.1.1</p> <p>cfsm501_atmo_2002_2006_TAOpoints_hourly.tgz -- contains netCDF files of hourly atmosphere variables: one atmosphere file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, lat, lon) at TAO locations. If joining multiple files together, concatenate along the time axis.</p> <p>-------------------------</p> <p>v 0.1.0</p> <p>cfsm501_atmo_ocn_2002_2006_TAOpoints_monthlyMeanDiurnalCycle.tgz -- contains netCDF files of monthly mean diurnal cycle of ocean and atmosphere variables: one atmosphere and one ocean file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, hour, [depth,] lat, lon) at TAO locations. The dimension "time" is of length 1 in all files. If joining multiple files together, concatenate along the time axis. The "hour" dimension represents the 24 hours of the diurnal cycle.</p> <p>Note that this version of the data has NOT had the 3-day high pass filter applied before the diurnal cycle calculation.</p>
Models (atmospheric and atomic) for the P-CORONA code together with some sample runs.
<p>The dataset comprises some sample example runs with all necessary input parameters and corresponding outputs expected from P-CORONA, along with a few atomic and atmospheric models. Description of the files included is given in the README.txt file.</p> <p>The P-CORONA code can be obtained at <a href="https://gitlab.com/polmag/P-CORONA">https://gitlab.com/polmag/P-CORONA</a> and its documentation at <a href="https://polmag.gitlab.io/P-CORONA/">https://polmag.gitlab.io/P-CORONA/</a></p> <p>The version of P-CORONA used to generate this dataset (which corresponds to the commit #85d7552 in <a href="https://gitlab.com/polmag/P-CORONA">https://gitlab.com/polmag/P-CORONA</a>) can be found at: <a href="https://doi.org/10.5281/zenodo.15195460">https://doi.org/10.5281/zenodo.15195460</a></p>
Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves -> Model outputs
<p>This repository contains MITgcm outputs associated with the paper "Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves", submitted to the Journal of Geophysical Research: Oceans. The outputs are presented in netCDF format and come from two simulations - <em><strong>GREEN</strong></em>, with chlorophyll, and <em><strong>BLUE</strong></em>, without chlorophyll affecting shortwave heating.</p> <ul> <li>Temperature and salinity <ul> <li>green_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>green_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>green_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> <li>blue_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>BLUE</strong><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>blue_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>blue_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> </ul> </li> <li>Sea ice <ul> <li>ice_green.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>GREEN</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li>sit_green.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> <li>ice_blue.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li> sit_blue.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> <li>Ice shelves <ul> <li>green_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> <li>blue_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> </ul> </li> <li>Surface heat fluxes <ul> <li>tflux_green.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_green.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_green.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_green.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_green.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>tflux_blue.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_blue.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>BLUE</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_blue.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>BLUE </strong></em>experiment </li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_blue.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_blue.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> </ul> </li> <li>BLING <ul> <li>chlorophyll.nc <ul> <li>2-dimensional monthly fields of surface chlorophyll in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: mg/m^3</li> </ul> </li> <li>euphotic_depth.nc <ul> <li>2-dimensional monthly fields of euphotic depth in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> </ul>
The data for Time-dependent Stellar Flare Models of Deep Atmospheric Heating
<p>This repository contains model output (within two .tar.gz files) and a pdf document (analysis_tools_mdwarfradyngrid-v1.0.pdf) that explains the contents and use. The models are described in Kowalski, A.F., Allred, J.C., & Carlsson, M. <em>Time-dependent Stellar Flare Models of Deep Atmospheric Heating, </em>published 2024 July 5 in <em>The Astrophysical Journal</em> Volume 969, Number 2 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad4148">10.3847/1538-4357/ad4148</a>).</p> <p>The Jupyter notebook demo, radyn_xtools_Demo.ipynb is included in the PyPI package installation that is described in analysis_tools_mdwarfradyngrid-1.0.pdf.</p>
Model Datafiles for Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice
<p>Datafiles produced by the 1-D photochemistry model used to create the publication "Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice" - Taysum et al. 2024.</p> <p> </p> <p>matching_orbits_v2.txt : Lists the orbital parameters and water vapour / aerosol file names corresponding to each of the 77 ACS MIR HCl observations that we study.</p> <p> </p> <p><strong>MCD_GlobaMaps.zip</strong> : .nc files containing model output where the model is ran at Longitude 0 degrees, across Ls 0 --> 360 in intervals of 30 degrees, and latitudes 60 S to 60 N in intervals of 15 degrees.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li> </li> </ul> </li> </ul> <p><strong>ACS_Comparisons.zip </strong>: .nc files containing the model output for specific ACS MIR HCl observations in Mars Year 34.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, and TGO measured water ice, dust, and H2O vapor profiles, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> </ul>
" Description and evaluation of a new contrail cirrus 2 parameterization in the ARPEGE-Climat atmospheric 3 model " datasets
<p>This repository contains the data files used for the analyses presented in the paper. The dataset includes variables of interest for the two main simulations (CONTFREE and CONTNUDGED) for the year 2019. </p> <ul> <li>"totcon" variable represents the integrated contrail cirrus coverage.</li> <li>"rst", respectively "rstcotra", represent the net downward shortwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net downward shortwave radiation contribution. </li> <li>"rlut", respectively "rlutcotra", represent the net upward longwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net upward longwave radiation contribution. </li> <li>"pissr" represents the probability of the gridbox being ice supersaturated. This variable is provided for pressure levels 200,225, and 250hPa.</li> <li>"clhcalipso" represents the integrated coverage of "high clouds" (>400hPa). </li> </ul>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
Code and data for Porting the Meso-NH Atmospheric Model on Different GPU Architectures for the Next Generation of Supercomputers (version MESONH-v55-OpenACC)
<p>GeometricMG.pdf (source: https://bitbucket.org/em459/tensorproductmultigrid/src/master/Documentation/)<br>MESONH_Bench_HECTOR_ADASTRA_LEONARDO.tar.gz: code and data for Meso-NH bench<br>Performance.zip: code and data for figures related to performance<br>WeatherApplications.zip: namelists for running weather applications<br>OASIS3_WW3.tar.gz: OASIS and WW3 codes for running the Meso-NH WWW3 coupled simulation</p>
Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux
<p>This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021)</p> <p>Followings are the explanation of data file.</p> <p>*** File naming rule ***<br> {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc</p> <p> data_group_name<br> - atm<br> - track</p> <p> experiment_name<br> - Wind<br> - Wave<br> - SlabO</p> <p> tropical_cyclone_case_number<br> - 001<br> - 002<br> ...<br> - 099<br> - 100</p> <p>*** Description on each data group ***<br> <br> atm: three dimentional atmospheric velocity data<br> - level: pressure levels for vertical atmospheric data<br> - longitude: Longitude<br> - latitude: Latitude<br> - velocity_u_component: averaged atmospheric eastward velocity</p> <p> track: data around tropical cyclone track<br> - time: UTC time (YYYYMMDDHH)<br> - longitude_center: Longitude of typhoon center<br> - latitude_center: Latitude of typhoon center<br> - central_pressure: typhoon central pressure<br> - maximum_surface_wind: typhoon maximum surface wind speed<br> - longitude_sfc: Longitude for surface data around typhoon center<br> - latitude_sfc: Latitude for surface data around typhoon center<br> - surface_wind_u_component: surface eastward wind around typhoon<br> - surface_wind_v_component: surface northward wind around typhoon<br> - sea_level_pressure: sea level pressure around typhoon<br> - latent_heat_flux: surface upward latent heat flux<br> - sensible_heat_flux: surface upward sensible heat flux<br> - time_atm: UTC time (YYYYMMDDHH) for atmospheric data<br> - level: pressure levels for atmospheric data<br> - longitude_atm: Longitude for atmospheric data around typhoon center<br> - latitude_atm: Latitude for atmospheric data around typhoon center<br> - velocity_u_component: 3d eastward velocity around typhoon<br> - velocity_v_component: 3d northward velocity around typhoon</p> <p> </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)
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.