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1,574 results for “atmospheres”
SSP5-8.5 Data for paper "When will humanity notice its impacts on atmospheric rivers?"
<p>The repository contains the SSP5-8.5 scenario simulation (one ensemble member) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.</p>
Datasets for Yue et al. (2022), Atmospheric Measurement Techniques: "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"
<p>Pixel scaled collocated AIRS, Aqua MODIS, SNPP CrIS, and SNPP VIIRS cloud retrieval products. These datasets are used to generate analyses and results presented in Yue et al. (2022, AMT): "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"</p>
Atmospheric data measured in Qvidja, Finland, from Apr to Jun 2019
<p>Time series data of gaseous and particle components and environmental variables measured at the Qvidja farm from Apr to Jun 2019.</p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models
<p>Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."</p>
Northern hemispheric atmospheric ethane trends in the upper troposphere and lower stratosphere (2006-2016) with reference to methane and propane
<p>Datasets for northern hemispheric atmospheric ethane, propane and methane (2006-2016) from airborne measurements by IAGOS-CARIBIC project. </p>
Nighttime Atmospheric Scattering Phase Function Derived from the Scattered Light of a Laser Beam - supplementary material
<p>The ZIP-package contains:</p> <ul> <li>Canon RAW (CR2) and DCRAW pre-processed (PGM) pictures of the sky with the green laser switched on/off, elevations 10 and 20 degrees</li> <li>Calibration data for Canon EOS 6D Mark II + Fish-Eye lens EF8-16mm at 8mm (wignetting, geometrical distortion)</li> <li>C/C++ software 'green_laser.cpp' for extracting the data from the pictures (incl. Windows-executable). Version 2 has improved median filtering and built-in correction if the laser beam does not go exactly overhead. </li> <li>Detailed description of the software and the method of taking and processing the RAW pictures in PDF format</li> <li>Extracted data in EXCEL workbooks</li> </ul>
Thermal Structure of the Martian Upper Atmosphere from MAVEN NGIMS
<p>This repository contains data to accompany the publication of our manuscript, "Thermal Structure of the Martian Upper Atmosphere from MAVEN NGIMS" which appears in the Journal of Geophysical Research: Planets.</p> <p>The repository contains .mat files of NGIMS densities and temperatures, as well as associated ephemerides. The filenames have the following structure,</p> <p> </p> <p>mvn_ngi_s3_BIN#_YYYYMMDDtoYYYYMMDD_TID##toTID##_ORBITtoORBIT.mat.</p> <p> </p> <p>Each .mat file contains a MATLAB struct named s3. Each s3 struct contains arrays of TIDs, orbits, dates, filenames of data products used to construct the s3, and periapsis information for each orbit. Mean profiles of detector counts and count rates, Ar, CO2, and N2 densities, density corrections, temperatures derived from the densities, altitude, latitude, longitude, local time, solar zenith angle, Ls, heliocentric distance, spacecraft position and velocity, solar latitude and longitude, and NGIMS ram angle are also included. In the "pass" struct within each s3 struct, there are data for each individual orbit in the s3 file. That is, the pass structure contains the data from each orbit which has been binned to obtain the mean profiles in the s3 struct. The pass struct is accessed by commands of the form,</p> <p> </p> <pre><code>s3.pass(n).FIELD</code></pre> <p> </p> <p>where n is an integer between 1 and length(s3.orbit) indicating the orbit in s3.orbit or TID in s3.tid to be accessed and FIELD the field, such as den_Ar (Ar density) or tmp_Ar (Ar temperature), to be accessed.</p> <p> </p> <p>For more information, please contact the corresponding author of the manuscript.</p>
Monthly-Mean Model Output for Paper Titled "Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?"
<p>These tarballs contains monthly-mean model output, which was primarily what was presented in the AGU JAMES paper titled "Do nudging tendencies depend on the nudging timescale chosen in atmospheric models?". Also included are the scripts used to set up these simulations, allowing reproducibility of the portion of the paper that used 3-hourly output. The 3-hourly output was not included, as it totaled ~7TB.</p>
Atmospheric resonant production for light dark sectors
<p>This is the companion dataset to the publication "<em>Atmospheric resonant production for light dark sectors</em>" by the same author. It includes the track length fluxes for electrons, positrons and photons generated by cosmic ray showers, as well as light mesons fluxes obtained by the same process. An example python loader is provided.</p>
Introducing new metrics for the atmospheric pressure adjustment to thermal structures in the ocean - instantaneous numerical outputs
<p>The data set contains the netcdf files with the instantaneous fields produced by the WRF V3.6.1 numerical model at 2014-10-06_01:00:00 over the Ligurian Sea analyzed in Meroni, A. N., F. Desbiolles and C. Pasquero "Introducing new metrics for the atmospheric pressure adjustment to thermal structures in the ocean".</p>
Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2
<p>The modified code as well as the CAM5 output for all the simulations in this study (V0 for the CTL run, CON1 for the EXP run, and PCON1R for EXP_COR run).</p> <p>The CESM1.2.1-CAM5.3 source code can be downloaded through the CESM official website https://www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code. Its output files are named in V0*.nc.</p> <p>The modified code for the EXP run in the study is in CON1.tar, with its CAM5 output files named in CON1*.nc.</p> <p>The modified code for the EXP_COR run in the study is in PCON1R.tar, with its CAM5 output files named in PCON1R*.nc</p>
Diurnal evolution of negative atmospheric ions above the boreal forest: From ground level to the free troposphere
<p>The data used in the publication is available for download. The unit of the data can be found in the header of the csv files. Please note, that the mass spectrometer data is in UTC+3 time, the rest of the data in UTC+2 time.</p>
Top of Atmospheric Reflectance (ToA) Landsat 8 OLI
<p>The data set presented here are the Top of Atmopsheric reflectance (ToA) images calculated from the Landsat 8 OLI level 2. covering path : 147 and row:47 for the cloud free images captured during year 2016. The ToA is converted from the digital numbers using Semi-Automatic Classification plugin of QGIS. The data can be readily used for the classification and feature extraction as per the use of the research. The dataset includes all the visible and near infrared bands of the Landsat (B1 to B7 Bands).. </p>
CM1 files for JGR-Atmospheres Paper Sensitivities of Cross-Tropopause Transport in Midlatitude Overshooting Convection to the Lower Stratosphere Environment
<p>Initial conditions to run the CM1 model simulations used in the JGR-Atmospheres Paper <em>Sensitivities of Cross-Tropopause Transport in Midlatitude Overshooting Convection to the Lower Stratosphere Environment</em>. The namelist.input file contains the initial conditions used to run the simulations, and the input sounding files (input_sounding_st_plume, input_sounding_st_noplume, input_sounding_dt_plume, input_sounding_dt_noplume) are the initial soundings used to run the CM1 model for each of the four different stratospheric environments. </p>
Atmospheric hydroxyl distribution from the EMAC model (MOM kinetic chemistry mechanism)
<p>This dataset contains the output from the simulations with the EMAC model implementing the MOM kinetic chemistry mechanism presented in <a href="https://doi.org/10.5194/acp-16-12477-2016">Lelieveld et al. (2016)</a> study. In addition to the computed atmospheric hydroxyl (OH) and hydroperoxyl (HO2) radicals abundance distributions, we add related model fields facilitating usage/comparison of these results with other estimates.</p><p>The data containers format is netCDF v.4 (compressed); please refer to the container variables/attributes for the extended information. We present here the actual model output (weekly averages for the 2013−2014 period, in EMAC-MOM__*.nc) and the monthly "climatology" fields (average, SD, minima and maxima of the time steps falling in particular month, in EMAC-MOM__*--clim.nc, respectively).</p><p>See the .README.pdf file for additional notes.</p><p>Changes w.r.t. initial version from 2020/09/22:</p><p>2020/10/30: Updated attributes and DOIs in .nc/.jnl files, added OH "climatology", updated README.</p><p>2020/10/31: Updated description and "climatology" (SD fields were missing).</p><p>2022/06/19: Added hydroperoxyl (HO2) fields, updated species average concentration plot sample script.</p><p>2023/10/26: Dataset title adjusted for clarity</p>
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES)
<p>Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.</p>
Ocean-atmosphere changes in the midlatitude North Pacific over the last 330 ka: Dust, biogenic sediment and authigenic uranium accumulation at Shatsky Rise — Dataset
<p><strong>Title</strong>: Ocean-atmosphere changes in the midlatitude North Pacific over the last 330 ka: Dust, biogenic sediment and authigenic uranium accumulation at Shatsky Rise — Dataset</p> <p><strong>Version</strong>: 1.0</p> <p><strong>Date of Release</strong>: July 03, 2022</p> <p><strong>Last Update</strong>: July 03, 2022</p> <p><strong>Identifier</strong>: 10.5281/zenodo.6791726</p> <p><strong>Permalink</strong>: <a href="https://doi.org/10.5281/zenodo.6791725">https://doi.org/10.5281/zenodo.6791725</a></p> <p><strong>Associated publication</strong>: </p> <p><strong>Link to publication preprint</strong>: </p> <p><strong>Suggested citation</strong>: Please reference the associated publication above when using any datasets or materials in this repository.</p> <p><strong>Contact information</strong>: Christopher W. Kinsley, ckinsley@mit.edu OR cwkinsley@gmail.com</p> <p><strong>Dates of data collection and generation</strong>: </p> <p>---------------</p> <p><strong>DESCRIPTION OF DATA</strong></p> <p>This data repository contains the following dataset. We refer the user to the original manuscript (see above) and the text of the Supporting Information published alongside this manuscript for additional general information regarding the collection and generation of these data.</p> <p>DATA TABLES FOR ALL CORE SITES</p> <ul> <li> <p><strong>Kinsley et al. (2022) P&P - Data Tables for ODP 198-1208A</strong><strong> core - v1</strong>: This Excel workbook contains all data used in the study for the ODP 198-1208A core site, taken by the R/V JOIDES Resolution close to the center of the Central High of Shatsky Rise in the western North Pacific Ocean during Ocean Drilling Program Leg 198. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> </ul>
Correlated k coefficients for H2-He atmospheres; 622 spectral windows and 1460 pressure-temperature points
<p>There are 10 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.622.tar.gz (5 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.622.tar.gz (5 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_100 should be read as 1x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere.</p> <p>The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 5 Fe/H values: 0.0, 0.5, 0.7, 1.0, and -0.3; and 1 C/O value: 1.0x solar. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 622_windows.txt (intervals defined as starting at lambda1 and ending at lambda2).</p> <p>The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper.</p> <p>Each dataset contains the following files:</p> <p>ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code.</p> <p>binary_data: the correlated k coefficients file in binary format</p> <p>full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid</p> <p>sum_in_atoms: relative abundances for the alkali metals</p> <p>sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations</p> <p><em>Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</em></p>
Dataset for Cox et al. (2022), "Short communication: Mechanism and prevention of irreversible trapping of atmospheric He during mineral crushing," Geochronology 2021-42
<p>Dataset for Cox, S. E., Miller, H. B. D., Hofmann, F., and Farley, K. A.: Short Communication: Mechanism and Prevention of Irreversible Trapping of Atmospheric He During Mineral Crushing, Geochronology, https://doi.org/10.5194/gchron-2021-42, 2022.</p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</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.