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85 results for “thermosphere”
Retrieval of Ar, N2, O, and CO in the Martian thermosphere using dayglow limb observations by EMM EMUS
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Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
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Meteor-ablated Aluminum in the Mesosphere-Lower Thermosphere
<p>Meteor-ablated Aluminum in the Mesosphere-Lower Thermosphere</p> <p>by John Plane, Shane Daly, Wuhu Feng, Michael Gerding and Juan Carlos Gómez Martín.</p> <p>The repository contains the data used in the above paper.</p>
Ionosphere-thermosphere data published in 'The 2D evolution of thermospheric ∑O/N2 response to weak geomagnetic activity during solar-minimum observed by GOLD'
This dataset was used to generate plots for a paper in the Geophysical Research Letter, an AGU journal. It includes the percentage difference of column density ratio of O/N2 between quiet time (DOY 153 in 2019) and weak geomagnetic activity time (DOY 156 in 2019), together with the meridional wind, zonal wind, meridional component and the zonal component of E cross B drift at pressure level -1.375 (160-170 km).
Data repository for Lin et al. (2022) "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling"
This dataset contains the necessary data and plotting tools supporting the paper titled "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling", by Lin et al., 2022. The data set contains thermospheric mass density simulated by MAGE, TIEGCM, DTM, and MSIS for the 1-6 February 2022 geomagnetic storm event.
Data for the figures in paper 'Variations in thermosphere composition and ionosphere total electron content under extremely weak geomagnetic activity conditions at solar-minimum'
This data set include the simulated percentage difference of O to N2 column density ratio between DOY 111 and 110 (Figure 3 of paper), and the absolute difference of neutral wind vector at pressure level -1.375 (Figure 3 of paper).They are from 0:10 UT to 23:55 UT on DOY 111 in 2019 with a temporal resolution of 15-min. And the absolute difference of diagnostic terms (horizontal advection, vertical advection and molecular diffusion) of O at pressure level -1.375 at 13:10 UT (Figure 4 of the paper)
Ionosphere-Thermosphere Data Published in "Challenges to Understanding the Earth's Ionosphere and Thermosphere"
<p>These data sets accompany an AGU Centennial publication "Challenges to Understanding the Earth's Ionosphere and Thermosphere" by R.A. Heelis and A. Maute. The data file tiegcm_totTgcm.s_181_12UT_GCpaper.nc is output from a thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) simulation described in the publication (DOI:10.1007/s11214-017-0330-3). The simulation is for 2009 solar minimum conditions. The ionospheric current and neutral wind in file "heelis_empirical_f130_12UT_extracted.nc" is based on a stand-alone dynamo model (described in doi:10.1002/2017JA024841) driven by the Horizontal Wind Model and ionospheric conductivities based on empirical models of the ionosphere (IRI) and the neutral atmosphere (MSIS). The calculation is for solar medium condition (F10.7=130) and DOY 181 under geomagnetic quiescent times.</p>
TIEGCM output associated with the publication "Planetary wave (PW) generation in the thermosphere driven by the PW-modulated tidal spectrum"
<p>This data set consists of simulation results associated with the publication "Planetary wave (PW) generation in the thermosphere driven by the PW-modulated tidal spectrum" by Forbes et al. The simulation output is from the thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) driven by various forcing at its lower boundary of 97 km based on a thermosphere-ionosphere-mesosphere-electrodynamics GCM (TIMEGCM) simulation for 2009. There are the following lower boundary cases defining the 97km zonal and meridional winds, neutral temperature and geopotential height: a. zonal and diurnal mean of wind, temperature and geopotential height (called S0) b. based on hourly values of TIMEGCM simulation c. S0 forcing and tidal forcing of wind, temperature and geopotential height d. S0 forcing and tidal forcing and planetary waves with periods of 2-7 days The output is for day of year 259 to 319, 2009 zonal and meridional neutral wind and neutral temperature between 97 to 200 km altitude. The format is netCDF and the dataset is described in the accompanying publication.</p>
TIEGCM results associated with "Dynamics and electrodynamics of an UFKW packet in the ionosphere and thermosphere"
<p>The data set consist of thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) simulation results used in the publication "Dynamics and electrodynamics of an ultra-fast Kelvin wave (UFKW) packet in the ionosphere and thermosphere" by Forbes et al. Focus is on an eastward-propagating ultra-fast Kelvin wave (UFKW) packet with periods between 2-4 days and zonal wavenumber s = -1 during DOY 266-281, 2009. Two sets of simulations are provided with different lower boundary (97km) conditions: 1. forced by daily and zonal mean (S0) 2. forced by S0 and 2-7days with s=-1 perturbations. For these simulations the solar flux and geomagnetic forcing is held constant. Hourly output is provided of the neutral wind, neutral temperature, NmF2, hmF2, ExB drift and electron density.</p>
Coupled Thermosphere-Ionosphere Tongue-like Structure During the Recovery Phase of the Geomagnetic Storm on May 12, 2021
<p>The file named 'Indices' includes Kp, F10.7p, By and Bz indices, AE and Dst indices, which are used to plot Figure 1. The file named 'GOLD_131', 'GOLD_132' and 'GOLD_133' include the parameters of O/N2 and temperature from GOLD observations, which are used to plot for Figures 2 and S1. The file named 'GPS_131', 'GPS_132' and 'GPS_133' include the parameters of TEC from GPS observations, which are used to plot for Figures 4 and S2. The file named 'TIEGCM_131' and 'TIEGCM_133' includes O/N2, Temperature, horizontal winds, TEC and diagnostic analysis terms of O+ density from the TIEGCM simulations on DOY 131 and 133 in 2021, which are used to plot for Figures 3 and 4, Movie S1 and S2.</p>
Ion friction and quantification of the geomagnetic influence on gravity wave propagation and dissipation in the thermosphere-ionosphere
<p>Data supporting figures 2, 3 and 4 of the manuscript doi:10.1002/2017JA024785<br> </p> <p> </p>
Dataset for Investigation of Thermospheric Response to Geomagnetic Storms Using GITM-OVATION Prime and -FTA model With Comparison to GOLD and SABER Observations
<div> <div> <div> <div> <h4>Dataset Overview</h4> <p>This dataset accompanies the research paper titled " Investigation of Thermospheric Response to Geomagnetic Storms Using GITM-OVATION Prime and -FTA model With Comparison to GOLD and SABER Observations" and contains all the necessary data and scripts required to reproduce the results presented in the paper. The dataset is organized by figure numbers, corresponding directly to the figures in the paper, making it straightforward to locate and generate the specific results.</p> <h4>Structure of the Dataset</h4> <p>The dataset is divided into multiple folders, each named according to the figure numbers in the paper (e.g., Figure_1, Figure_2, etc.). Inside each of these folders, you will find:</p> <ul> <li><strong>Data Files</strong>: These files contain the raw and processed data used to generate the figures.</li> <li><strong>Scripts</strong>: MATLAB scripts (e.g., Figure_2*.m) that process the data and generate the respective figures.</li> <li><strong>Readme.txt</strong>: A text file providing detailed instructions on how to use the data and scripts, including any dependencies or specific steps required.</li> </ul> <h4>Instructions for Reproducing Figures</h4> <ol> <li> <p><strong>Download the Dataset</strong>:</p> <ul> <li>Download the entire dataset or specific figure folders as needed.</li> </ul> </li> <li> <p><strong>Prepare Your Environment</strong>:</p> <ul> <li>Ensure that MATLAB is installed on your local machine.</li> <li>Verify that all necessary MATLAB toolboxes and dependencies are installed, as specified in the Readme.txt files within each figure folder.</li> </ul> </li> <li> <p><strong>Generate Figures</strong>:</p> <ul> <li>Navigate to the directory where the dataset is saved.</li> <li>Open MATLAB and set the current directory to the folder containing the downloaded data and scripts.</li> <li>Run the script corresponding to the figure you wish to generate. For example, to generate Figure 1, navigate to the Figure_1 folder and run the <code>Figure_1*.m.</code></li> </ul> </li> <li> <p><strong>Refer to Readme.txt for Further Details</strong>:</p> <ul> <li>Each figure folder contains a Readme.txt file with additional details, including specific instructions, data descriptions, and any figure-specific requirements or notes.</li> </ul> </li> </ol> <p>By following these steps, you can successfully reproduce the figures and results presented in the paper "Paper 1 vs Paper 2" using the provided dataset and scripts. If you encounter any issues or have questions, refer to the Readme.txt files or contact the authors for further assistance.</p> </div> </div> </div> </div>
Influence of solar activity on penetration of travelling planetary-scale waves from the troposphere into the thermosphere
<p>This dataset contains model output files and examples of scripts for depicting figures related to the article "Influence of solar activity on penetration of travelling planetary-scale waves from the troposphere into the thermosphere" by A.V. Koval, N. M. Gavrilov, A. I. Pogoreltsev, N. O. Shevchuk.</p> <p>Contents:<br> averh.zip - Output data from model simulations averaged over 12 runs with high solar activity;<br> /averh/uvt400_Jan_ogw_volf.dx - Zonal, meridional wind components, temperature (structure corresponds to uvt.ctl);<br> /averh/phi.dx - Geopotential height in gpm;<br> /averh/gh_m1_1.dx, /averh/gh_m2_1.dx - Estimated PSW amplitudes and phases after the longitude-time Fourier transform of the MUAM solutions with the least squares fitting of the geopotential height (zonal wavenumber 1 and 2, respectively)<br> /averh/zw_m1_1.dx, /averh/zw_m2_1.dx, /averh/mw_m1_1.dx, /averh/mw_m2_1.dx, /averh/tp_m1_1.dx, /averh/tp_m2_1.dx - The same but for wind components and temperature, respectively<br> averl.zip - Output data from model simulations averaged over 12 runs with low solar activity; contents are similar to "averh.zip"<br> files *.gs, *.ctl - Examples of the scripts and file descriptions for GRADS system to build figures shown in the manuscript.<br> Additional data including outputs from separate model runs are available from the authors upon request. </p>
Data for the paper "Impacts of multiscale field-aligned currents (FACs) on the ionosphere-thermosphere system: GITM simulation"
<p>This dataset serves for the paper "Impacts of multiscale field-aligned currents (FACs) on the ionosphere-thermosphere system: GITM simulation".</p> <p> The uploaded zip file contains 6 directories, each directory contains 1 CDF file, detailed description for each file are listed as follows:</p> <p><strong>fig1: </strong></p> <p>vars: 'mlt', 'mlat', 'avg_lg', 'std_lg', 'avg_ms', 'std_ms', 'avg_sm', 'std_sm'</p> <p>avg_lg, std_lg: large-scale mean FAC and FAC variability </p> <p>avg_ms, std_ms: meso-scale mean FAC and FAC variability </p> <p>avg_sm, std_sm: small-scale mean FAC and FAC variability </p> <p><strong>fig2: </strong></p> <p>vars: 'mlt', 'mlat', 'avg_lg', 'std_lg', 'avg_ms', 'std_ms', 'avg_sm', 'std_sm'</p> <p>avg_lg, std_lg: Magnitudes of the large-scale mean electric field and electric field variability </p> <p>avg_ms, std_ms: Magnitudes of the meso-scale mean electric field and electric field variability </p> <p>avg_sm, std_sm: Magnitudes of the small-scale mean electric field and electric field variability </p> <p><strong>fig3:</strong></p> <p>vars: 'fac', 'dBy'</p> <p>fac: FAC along the track</p> <p>dBy: East-westward magnetic perturbation along the same track</p> <p><strong>fig4:</strong></p> <p>vars: 'glon', 'glat', 'Emag', 'int_jh'</p> <p>glon, glat: geographic longitude and latitude</p> <p>Emag: electric field magnitude </p> <p>int_jh: height-integrated Joule heating </p> <p><strong>fig6:</strong></p> <p>vars: 'glon', 'glat', 'gitm_evar_lg', 'gitm_evar_ms', 'de2_evar_lg', 'de2_evar_ms'</p> <p>glon, glat: geographic longitude and latitude</p> <p>'gitm_evar_lg', 'gitm_evar_ms': magnitudes of the simulated large-scale and mess-scale electric field variabilities</p> <p>'de2_evar_lg', 'de2_evar_ms': magnitudes of the large-scale and mess-scale electric field variabilities from the data in geographic coordinates</p> <p><strong>fig7:</strong></p> <p>vars: 'glon', 'glat', 'gitm_jh_lg', 'gitm_jh_ms'</p> <p>glon, glat: geographic longitude and latitude</p> <p>'gitm_jh_lg', 'gitm_jh_ms': Increase of the height-integrated JH related to the large- and mess-scale FAC variabilities </p>
Data for "Multi-instrument studies of thermospheric weather above Alaska"
<p>These files contain the original data used to generate figures presented in "Multi-instrument studies of thermospheric weather above Alaska" submitted to the Journal of Geophysical Research - Space Physics in 2018 by authors M. G. Conde, W. A. Bristow, D. L. Hampton, and J. Elliott.</p> <p>The main figures in this work were generated by the IDL program named "sdi3k_newmapper.pro", which has also been uploaded here.</p> <p>Data providers were:</p> <p>Scanning Doppler Imager data (files with names ending in "NZ0115": M. G. Conde, University of Alaska Fairbanks</p> <p>SuperDARN ion convection (".map.vec.geo" or ".geo.dat" files): W. A. Bristow, University of Alaska Fairbanks</p> <p>PFISR ion convection (.h5 files): Roger Varney, SRI International</p> <p>SSUSI auroral images (".nc"files with the word "SSUSI" in the filename): Larry Paxton, John's Hopkins University Applied Physics Lab</p> <p>All-sky camera images (".FIT" or ".jpg" files): D. L. Hampton, University of Alaska Fairbanks</p> <p>Magnetometer data (".csv files): D. L. Hampton, University of Alaska Fairbanks</p> <p> </p> <p> </p>
Influence of thermospheric impacts of solar activity on the general circulation and long-period planetary waves in the middle atmosphere
<p>This dataset contains model output files and examples of scripts for depicting figures related to the article "Influence of thermospheric impacts of solar activity on the general circulation and long-period planetary waves in the middle atmosphere " by A.V. Koval, N. M. Gavrilov, A. I. Pogoreltsev, N. O. Shevchuk.</p> <p><br> Contents:<br> Output data from model simulations averaged over 16 pairs of model runs with high and low solar activity:</p> <p><br> gh_m1_hsa5.dx, gh_m1_lsa5.dx – amplitudes of the geopotential height variations in g.p.m. at high and low solar activity caused by long-period PW modes with zonal wavenumber 1 averaged over 80 time subintervals selected from pairs of the MUAM runs (structure described in w1_tp1_hsa.ctl, w1_tp1_lsa.ctl).<br> gh_m2_hsa5.dx, gh_m2_lsa5.dx, gh_m3_hsa5.dx, gh_m3_lsa5.dx, gh_m4_hsa5.dx, gh_m4_lsa5.dx – the same but for zonal wavenumbers 2,3,4.<br> gh_m1_disp_dif5.dx – dispersion of differences in corresponding wave amplitudes between high and low solar activitiy (w1_tp1_disp.ctl).<br> gh_m2_disp_dif5.dx, gh_m3_disp_dif5.dx, gh_m4_disp_dif5.dx – the same but for zonal wavenumbers 2,3,4.<br> index_refr1_1_5.dx, index_refr1_2_5.dx, index_refr1_3_5.dx, index_refr1_4_5.dx – the zonal-mean quasi-geostrophic complex refractivity index squared (RI2) for PW modes with zonal wavenumbers 1-4 (ind_refr1_1.ctl).<br> uq_vwres1_pv_Jan_1_5.dx, uq_vwres1_pv_Jan_2_5.dx, uq_vwres1_pv_Jan_3_5.dx, uq_vwres1_pv_Jan_4_5.dx – the Eliassen-Palm flux for PW modes with zonal wavenumbers 1-4 (uqep1_vwres_1.ctl).<br> zw_a0_grads5_hsa.dx, tp_a0_grads5_hsa.dx – zonal-mean zonal wind in m/s and temperature in K for December – February averaged over 16-member ensemble of model runs for high solar activity (m0_zw5_hsa.ctl, m0_tp5_hsa.ctl).<br> zw_a0_grads5_lsa.dx, tp_a0_grads5_lsa.dx – the same but for the low solar activity<br> disp_U_dif5_.dx, disp_T_dif5_.dx – dispersion of differences in zonal wind and temperature between high and low solar activitiy for December – February averaged over 16-member ensemble of model runs (tp1_disp.ctl).<br> Additional data including outputs from separate model runs are available from the authors upon request.</p>
A Case Study of Thermospheric Exospheric Temperature Responses during the G-condition at Mohe and Beijing Stations
<pre>The Data presents the data shown in Figure 1-3.<br><br>Geomagnetic_condition.mat includes the data of IMF By, IMF Bz, Dst index, Kp index and F107 index presented in Figure 1.<br><br>observations.mat includes the data of peak Ne and Peak Ne height at Mohe and Beijing presented in Figure 2a and 2b.<br><br>Retrieved_Tex.mat includes the data of retrieved Tex at Mohe and Beijing presented in Figure 2c and 2d.<br><br>Chemiccal_reaction_rate_and_O_to_N2.mat includes the data of chemical reaction rate and O/N2 at Mohe and Beijing presented in Figure 3.<br><br>ionogram.rar includes ionograms at Beijing and Mohe during the G-condition. Title of each ionogram is named as 'XXXXUT(XXXXLT)', representing hour and second in universal time and in local time.<br>For instance, '0015UT(0800LT)' corresponds to 00:15 UT and 8:00 LT.</pre>
A Case Study of Thermospheric Exospheric Temperature Responses during the G-condition at Mohe and Beijing Stations
<pre>The Data presents the data shown in Figure 1-3.<br><br>Geomagnetic_condition.mat includes the data of IMF By, IMF Bz, Dst index, Kp index and F107 index presented in Figure 1.<br><br>observations.mat includes the data of peak Ne and Peak Ne height at Mohe and Beijing presented in Figure 2a and 2b.<br><br>Retrieved_Tex.mat includes the data of retrieved Tex at Mohe and Beijing presented in Figure 2c and 2d.<br><br>Chemiccal_reaction_rate_and_O_to_N2.mat includes the data of chemical reaction rate and O/N2 at Mohe and Beijing presented in Figure 3.<br><br>ionogram.rar includes ionograms at Beijing and Mohe during the G-condition. Title of each ionogram is named as 'XXXXUT(XXXXLT)', representing hour and second in universal time and in local time.<br>For instance, '0015UT(0800LT)' corresponds to 00:15 UT and 8:00 LT.</pre>
Dataset for "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model"
<p>This repository contains all the datasets and scripts used to generate the figures in the paper tilted "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model". The data and scripts are organized according to the figure numbers to facilitate ease of use and reproducibility.</p> <p><strong>Directory Structure</strong><br>Each figure has its own dedicated folder. Inside each folder, you will find:</p> <p><strong>Data files: </strong>These files contain the dataset used to generate the corresponding figure.<br><strong>Scripts: </strong>Python or other relevant scripts needed to process the data and create the figure.<br><strong>README.txt: </strong>Each figure folder includes a separate README.txt file that provides detailed instructions on how to run the scripts.<br><strong>How to Use</strong><br>Navigate to a Figure's Folder: Locate the folder corresponding to the figure number you are interested in (e.g., Fig_01, Fig_02, etc.).</p> <p>Read the README.txt: Open the README.txt file in that folder. It contains specific instructions on how to execute the code and generate the figure, along with any necessary setup details.</p> <p><strong>Run the Scripts:</strong> Follow the instructions in the README.txt file to run the script(s) and generate the figure.</p>
Seasonal and altitude dependence of thermospheric metastable helium densities measured by fluorescence lidar: lidar dataset
<p>These are NetCDF files containing lidar return data from the helium fluorescence lidar described in the referenced paper (doi to be added). The data contain the following variables:</p> <p>The data have one of two possible formats. For files beginning '2023', the instrument was operated in single frequency mode, and the files contain the following fields:</p> <p> 'photons_chX' with X between 0 and 11 - A Nx3000 array of detected photon counts binned into N 10-second intervals in time and 3000 2-km bins in altitude. Note that only the first 1000 bins in the second axis contain data, up to an altitude of 2000 km; the arrays are then padded with zeros.</p> <p> 'shots_chX' with X between 0 and 11 - An array of length N with the number of laser pulses fired during the corresponding interval.</p> <p> 'time_offset' - A single value, giving the unix timestamp relative to which the 'time' variable is measured.</p> <p>'time' - An array of length N with the midpoint time in milliseconds of the corresponding interval, measured relative to the 'time_offset'.</p> <p>'altitude' - An array of length 3000 with the lower edges of the corresponding altitude bins, in meters.</p> <p>For files beginning '2024', the instrument was operated in multi-frequency mode, and the files contain the following fields:</p> <p>'photons_chX' with X between 0 and 11 - A 41xNx3000 array of detected photon counts binned into 41 1-pm bins in wavelength, N 10-s intervals in time, and 3000 2-km bins in altitude. Note that only the first 600 bins in the third axis contain data, up to an altitude of 1200 km; the arrays are then padded with zeros.</p> <p> 'shots_chX' with X between 0 and 11 - A 41xN array with the number of laser pulses fired with the corresponding wavelength during the corresponding interval.</p> <p>'wavelength' - An array of length 41 giving the central wavelength of each wavelength bin.</p> <p> 'time_offset' - A single value, giving the unix timestamp relative to which the 'time' variable is measured.</p> <p>'time' - An array of length N with the midpoint time in milliseconds of the corresponding interval, measured relative to the 'time_offset'.</p> <p>'altitude' - An array of length 3000 with the lower edges of the corresponding altitude bins, in meters.</p> <p> </p> <p>Note that this is a standardized data format used by many instruments; it contains several other variable fields that are not used by the present instrument and are therefore omitted from this description.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.