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122 results for “Emission Modelling”
Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"
<h1>Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"</h1> <p>See http://arxiv.org/abs/2411.04543.</p> <p>This data set contains the .fits files envolved in our work, from <a href="https://irsa.ipac.caltech.edu/data/Planck/" target="_blank" rel="noopener">Planck release</a> and <a href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/" target="_blank" rel="noopener">Irfan et. al., 2019</a>: </p> <p>In order to use these data files, </p> <p>please follow: (github readme)</p> <h2>Data from <em>Planck</em> release</h2> <h3><em>Planck</em> Release 1, 2013</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2013</strong></p> <p>HFI_CompMap_ThermalDustModel_2048_R1.20.fits</p> <p><a title="HFI_CompMap_ThermalDustModel_2048_R1.20.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: full-sky maps at 217 GHz with zodiacal light and without zodiacal light</strong></p> <p><strong>Relation to this work: used to filter out regions with strong zodiacal emission</strong></p> <p>HFI_SkyMap_217_2048_R1.10_nominal.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits</a></p> <p>HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 2, 2015</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of synchrotron emission</strong></p> <p><strong>Relation to this work: used to remove synchrotron emission from full-sky maps</strong></p> <p>COM_CompMap_Synchrotron-commander_0256_R2.00.fits<br><a title="COM_CompMap_Synchrotron-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of free-free emission</strong></p> <p><strong>Relation to this work: used to remove free-free emission from full-sky maps</strong></p> <p>COM_CompMap_freefree-commander_0256_R2.00.fits<br><a title="COM_CompMap_freefree-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of carbon monoxide</strong></p> <p><strong>Relation to this work: used to remove carbon monoxide emission from full-sky maps</strong></p> <p>COM_CompMap_CO21-commander_2048_R2.00.fits<br><a title="COM_CompMap_CO21-commander_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of 94/100 GHz molecular emission lines</strong></p> <p><strong>Relation to this work: used to remove 94/100 GHz emission lines from full-sky maps</strong></p> <p>COM_CompMap_xline-commander_0256_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: Galactic plane masks with no apodization</strong></p> <p><strong>Relation to this work: used to mask Galactic plane</strong></p> <p>HFI_Mask_GalPlane-apo0_2048_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_GalPlane-apo0_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: point source masks</strong></p> <p><strong>Relation to this work: used to mask point sources in full-sky maps and inpaint them </strong></p> <p>HFI_Mask_PointSrc_2048_R2.00.fits<br><a title="HFI_Mask_PointSrc_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2015 (GNILC pipeline, without CIB contamination)</strong></p> <p>COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits</a></p> <p><strong>Format: .FITS file (table)</strong></p> <p><strong>Type: <em>Planck</em> catalogue of compact sources at 30, 44, 70, 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: to mask compact sources</strong></p> <p>COM_PCCS_030_R2.04.fits<br><a title="COM_PCCS_030_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits</a></p> <p>COM_PCCS_044_R2.04.fits<br><a title="COM_PCCS_044_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits</a></p> <p>COM_PCCS_070_R2.04.fits<br><a title="COM_PCCS_070_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits</a></p> <p>COM_PCCS_100-excluded_R2.01.fits<br><a title="COM_PCCS_100-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits</a></p> <p>COM_PCCS_100_R2.01.fits<br><a title="COM_PCCS_100_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits</a></p> <p>COM_PCCS_143-excluded_R2.01.fits<br><a title="COM_PCCS_143-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits</a></p> <p>COM_PCCS_143_R2.01.fits<br><a title="COM_PCCS_143_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits</a></p> <p>COM_PCCS_217-excluded_R2.01.fits<br><a title="COM_PCCS_217-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits</a></p> <p>COM_PCCS_217_R2.01.fits<br><a title="COM_PCCS_217_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits</a></p> <p>COM_PCCS_353-excluded_R2.01.fits<br><a title="COM_PCCS_353-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits</a></p> <p>COM_PCCS_353_R2.01.fits<br><a title="COM_PCCS_353_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits</a></p> <p>COM_PCCS_545-excluded_R2.01.fits<br><a title="COM_PCCS_545-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits</a></p> <p>COM_PCCS_545_R2.01.fits<br><a title="COM_PCCS_545_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits</a></p> <p>COM_PCCS_857-excluded_R2.01.fits<br><a title="COM_PCCS_857-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits</a></p> <p>COM_PCCS_857_R2.01.fits<br><a title="COM_PCCS_857_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 3, 2018</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of CMB anisotropies (SMICA from <em>Planck</em> 2018)</strong></p> <p><strong>Relation to this work: used to remove CMB anisotropies from full-sky maps</strong></p> <p>COM_CMB_IQU-smica_2048_R3.00_full.fits<br><a title="COM_CMB_IQU-smica_2048_R3.00_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: <em>Planck</em> 2018 full-sky maps at 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: used to obtain dust data maps at these bands</strong></p> <p>HFI_SkyMap_100_2048_R3.01_full.fits<br><a title="HFI_SkyMap_100_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_143_2048_R3.01_full.fits<br><a title="HFI_SkyMap_143_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_217_2048_R3.01_full.fits<br><a title="HFI_SkyMap_217_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_353_2048_R3.01_full.fits<br><a title="HFI_SkyMap_353_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_545_2048_R3.01_full.fits<br><a title="HFI_SkyMap_545_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_857_2048_R3.01_full.fits<br><a title="HFI_SkyMap_857_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits</a></p> <p>HFI_RIMO_R3.00.fits<br><a title="HFI_RIMO_R3.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits</a></p> <h2> </h2> <h2>Thermal dust model from Melis O. Irfan et al. <a href="https://www.aanda.org/articles/aa/abs/2019/03/aa34394-18/aa34394-18.html" target="_blank" rel="noopener">A&A 623, A21 (2019)</a></h2> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Melis O. Irfan et al. 2019</strong></p> <p>beta.fits<br><a title="beta.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits</a></p> <p>tau.fits<br><a title="tau.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits</a></p> <p>temp.fits<br><a title="temp.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits</a></p> <p> </p>
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
Datasets for greenhouse gasses emissions and removals from inventories and global models over Africa
<p>This file includes the data from Mostefaoui et al. (ESSD, under submission), for 54 countries African countries</p> <p> The data includes: </p> <p>(1) CO2 fluxes from global models - satellite inversions and Dynamic Global Vegetation Models (DGVM) -, and from a collection of national inventories for LULUCF, GFEDv4 and FAO data.</p> <p> DGVM values are the median of 14 models, consistent with the Global Carbon Budget 2020 (https://essd.copernicus.org/articles/12/3269/2020/) LULUCF UNFCCC corrected values are from Grassi <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a> </p> <p>(2) CH4 fluxes from global models consistent with the Global Methane Budget 2020 (https://essd.copernicus.org/articles/12/1561/2020/)</p> <p>(3 N2O fluxes from global models (three inversions)</p> <p>For further methodological details, see Mostefaoui et al. (ESSD, under submission):</p> <p>Mounia Mostefaoui, Philippe Ciais, Matthew J. McGrath, Philippe Peylin, Prabir Patra. Greenhouse gasses emissions and their trends over the last three decades across Africa, ESSD (under submission)</p>
MESA model files and data for: 'Stellar Neutrino Emission Across The Mass-Metallicity Plane'
<p>Example MESA model files and stellar evolution tracks for download from "Stellar Neutrino Emission Across The Mass-Metallicity Plane".</p>
Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results
<p>This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.</p>
`hectordata` inputs: Reduced Complexity Model Intercomparison (RCMIP) Phase 1 Emissions and Concentrations
<p>The attached data was downloaded on April 30, 2020 from <a href="https://www.rcmip.org/">https://www.rcmip.org/</a>. These data have no formal citation or DOI. Since we use this specific version as inputs to the `hectordata` R package (<a href="https://github.com/JGCRI/hectordata">https://github.com/JGCRI/hectordata</a>), we offer these as an archived resource. The `hectordata` package is used to prepare the inputs used in the simple climate model Hector (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>).</p> <p>The datasets contained were listed as "Version 4.0.0, 31st December 2019" for both Emissions and Concentrations. Units are described in the files.</p>
Carbonyl Sulfide (OCS/COS) and Carbon Disulfide (CS2): global modelled marine surface concentrations and emissions, 2000-2019
<p>This dataset contains a global ocean emission inventory of the sulfur-containing trace gases carbonyl sulfide (OCS/COS) and carbon disulfide (CS2). It covers the period 2000-2019, and includes a monthly average and an average diel cycle for each month for sea surface concentrations and emissions to the atmosphere. The spatial resolution is 2.8° x 2.8° at the equator (T42 grid), the depth extends from the surface to the mixed layer depth.</p> <p>Carbonyl sulfide (OCS) is the most abundant, long-lived sulphur gas in the atmosphere and a major supplier of sulfur to the stratospheric sulfate aerosol layer. The short-lived gas carbon disulfide (CS<sub>2</sub>) is oxidized to OCS and constitutes a major indirect source to the atmospheric budget of OCS. We encourage the use of the data provided here as input for atmospheric modelling studies to further assess the atmospheric OCS budget and the role of OCS in climate.</p>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity
<p><strong>Overview</strong></p> <p>The HDF5 file contains primary measurement data and secondary processing data that was used to assess clear sky effective emissivity and transmissivity estimates and generate the results in the associated manuscript (accepted and forthcoming).</p> <p>Data is indexed by solar time and provided per site for years 2010 through 2015. Sample Python code is provided to reconstruct training and validation sets by concatenating all 'tra' or 'val' samples across sites. Results can be explored by modifying choice of filters and constructing new training and validation sets.</p> <p><strong>Data usage</strong></p> <p>The usage of the data presented here is intended for research and development purposes only and implies explicit reference to the paper:<br><em>Matsunobu, L. M., & Coimbra, C. F. M. (2024). On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity. Journal of Geophysical Research: Atmospheres, 129, e2023JD039798. https://doi.org/10.1029/2023JD039798</em></p> <p><strong>Data description</strong></p> <p>Column names and descriptions are as follows:<br>- dlw_m: measured downwelling longwave [W/m^2]<br>- ghi_m: measured global horizontal irradiance [W/m^2]<br>- dni_m: measured direct normal irradiance [W/m^2]<br>- dhi_m: measured diffuse horizontal irradiance [W/m^2]<br>- rh_m: measured relative humidity [%]<br>- pa_m: measured atmospheric pressure [hPa]<br>- t_m: measured temperature [K]<br>- sza: solar zenith angle [deg]<br>- ghi_c: clear sky global horizontal irradiance [W/m^2]<br>- dni_c: clear sky direct normal irradiance [W/m^2]<br>- dhi_c: clear sky diffuse horizontal irradiance [W/m^2]<br>- cs1: clear sky filter 1<br>- cs2: clear sky filter 2<br>- site_elev: station elevation [m]<br>- clr_pct: fraction of samples identified as clear for the given site and day<br>- clr_num: number of samples identified as clear for the given site and day<br>- pw_hpa: water vapor partial pressure [hPa]<br>- alt_correction: altitude correction<br>- tra: indicate if sample is included in training set<br>- val: indicate if sample is included in validation set<br>- sqrt_pw: square root of non-dimensional water vapor partial pressure<br>- e_sky: effective clear sky emissivity</p> <p>The last two columns, 'sqrt_pw' and 'e_sky' represent the input and target for linear regression, i.e. e_sky = c_1 + (c_2 * sqrt_pw).<br>Altitude corrected sky emissivity, or expected emissivity for a station at sea-level, is found by e_sky - alt_correction.</p> <p><strong>Sample code (Python v3.8)</strong></p> <pre>import pandas as pd site = "GWC" # or other station code df = pd.read_hdf("data.h5", key=site) # import single site</pre> <p>Training and validation sets can be reconstructed as below. Linear regression on 'sqrt_pw' to predict 'e_sky' - 'alt_correction' in the resultant training set will reproduce results in the associated manuscript.</p> <pre>training = [] validation = [] surfrad_sites = ['BON', 'DRA', 'FPK', 'GWC', 'PSU', 'SXF', 'TBL'] for site in surfrad_sites: # loop through sites df = pd.read_hdf("data.h5", key=site) df["site"] = site # add site name training.append(df.loc[df.tra]) # append samples marked as training validation.append(df.loc[df.val]) # append samples marked as validation # join respective set samples across sites training = pd.concat(training, ignore_index=False) validation = pd.concat(validation, ignore_index=False)</pre> <p>Reproduce regression results</p> <pre>from sklearn.linear_model import LinearRegression c1 = 0.6 # set intercept (c1 constant) x = training.sqrt_pw.to_numpy().reshape(-1, 1) y = training.e_sky - training.alt_correction - c1 # adjust for altitude and c1 y = y.to_numpy().reshape(-1, 1) model = LinearRegression(fit_intercept=False) model.fit(x, y) c2 = model.coef_[0][0] print(f"c1={c1:.3f}, c2={c2:.3f}") # output: c1=0.600, c2=1.652</pre>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Monthly methane emissions estimated with the atmospheric inversion model CarbonTracker Europe - CH4
<p>Monthly estimates of global methane emissions from CarbonTracker Europe - CH4 (CTE-CH4). CTE-CH4 is a Bayesian inversion framework based on an ensemble Kalman filter algorithm using the Eulerian global atmospheric transport model TM5. The gridded fluxes are available with a resolution of 1.0x1.0 degrees and in units of kgCH4/m2/month. The gridded flux file contains variables for posterior fluxes from soils (bio_flux_opt) and anthropogenic sources (anth_flux_opt) and the total posterior flux (total_flux_opt). Priors used: Anthropogenic: EDGAR v6, biosphere/wetlands (soils): LPX-Bern DYPTOP v1.4, Ocean: Weber et al. (2019), Biomass burning: GFED v4.1, Termites: VISIT. A more detailed setup of the inversion is documented in Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. Note: Fluxes are optimised at 1.0x1.0 degrees in northern high latitudes (USA, Canada, Europe and Russia), but are also provided here at the same resolution for other regions.</p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
Dataset for: "Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star"
<p><strong>Summary: </strong>This deposit supplements the manuscript, "<em>Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star</em>", accepted to the Astrophysical Journal. The tar.gz archive file contains an example Cloudy script and associated data. The complete listing of the files included in this archive file are given here: </p> <p> ReadMe Documentation file<br> example_cloudy_input_file.txt Cloudy script<br> spica_i=100_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=110_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=116_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=120_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=130_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=140_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=150_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=160_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=170_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=180_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=90_V5_solar.ascii Spica input stellar spectral energy distribution<br> </p> <p><strong>System requirements:</strong> The input script and SED files correspond to Cloudy code version 17.02:<br> Codebase: <a href="https://trac.nublado.org/">https://trac.nublado.org/</a><br> Primary documentation: Ferland et al. (<a href="https://ui.adsabs.harvard.edu/abs/2017RMxAA..53..385F/abstract">2017RMxAA..53..385F</a>; arXiv:<a href="https://arxiv.org/abs/1705.10877">1705.10877</a>)<br> </p> <p>Additional documentation provided in the ReadMe file.</p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M⊙ one-dimensional neutrino-driven explosion
<p>Model spectra of the 9 Msun iron-core model, and the pure H toy model, 200-600d. Distance 10 Mpc assumed.</p>
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling
<p>The set consists of 5 files that constitute the main calculations of ammonia emissions over Europe for the years 2013-2020. <br> The detailed description of variables follows:</p> <p>1) PriorEmission.nc<br> - Pall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia prior emissions used in the study [ng/m2/s]</p> <p>2) PosteriorEmission.nc<br> - Xall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia posterior emissions [ng/m2/s]</p> <p>3) UncertaintyEmission.nc<br> - Uall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with uncertainty of posterior emissions [ng/m2/s]</p> <p>4) stations_vmodVSobs.mat <br> - st_list: list of stations identifiers, 53 stations in total<br> - st_coord: stations coordinates [lot,lat]<br> - st_OBSdays: matrix of the size 53 x (366*8) with observations in daily resolution [ug/m3]<br> - st_ind_obs: logical matrix of the size 53 x (366*8) with indicators when each station provides observation (1) and when not (0)<br> - st_prior_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with prior emission [ug/m3]- st_post_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with posterior emission [ug/m3]<br> - st_prior1_vmod_days: same as st_prior_vmod_days for EC6G4 prior<br> - st_prior2_vmod_days: same as st_prior_vmod_days for EGG prior<br> - st_prior3_vmod_days: same as st_prior_vmod_days for NE prior<br> - st_prior4_vmod_days: same as st_prior_vmod_days for VD prior</p>
Model run and scenario data for study "Bioenergy-induced land-use change emissions with sectorally fragmented policies"
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Bioenergy-induced land-use change emissions with sectorally fragmented policies</strong></p> <p>by <em>Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer, Elmar Kriegler</em></p> <p>published in <em>Nature Climate Change </em>(2023).</p> <p><em><strong>ModelRuns_remind </strong></em>(directory) contains all REMIND model runs of the scenarios underlying the paper.</p> <p><em><strong>ModelRuns_magpie </strong></em>(directory) contains all MAgPIE model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains an RStudio Project that was used for the data analysis and the generation of the figures of the paper. It additionally contains all figures and figure data that are shown in the paper.</p> <p><em><strong>ScenarioMapping.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the model run directories).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.