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85 results for “thermosphere”
Data files for A "Boreing" Night of Observations of the Upper Mesosphere and Lower Thermosphere Over the Andes Lidar Observatory
<p>The files in this set are data obtained from the ANI2 airglow imager located at the Andes Lidar Observatory.in Chile (30.23S, 70.73W, 2530 m). The files are named for a JGR paper by J. Hecht et al. entitled A "Boreing" Night of Observations of the Uppe rMesosphere and Lower Thermosphere Over the Andes Lidar Observatory. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that are text files are meant to be read with IDL as discussed in the readme file. </p>
Specular Meteor Radar wind estimates from Tirupati, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to specular meteor radars" by Harding et al. (2021)"
<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are u0, v0 (the zonal and meridional wind profiles observed by the meteor radar). Dimensions are "time" and "alt" (in km). Velocity units are m/s, and lat/lon are in degrees. More information can be found in the paper. Please contact and get permission from the data providers (M. Venkat Ratnam and S. Vijaya Bhaskara Rao) before using the data in any publications or presentations.</pre>
Seasonal Variation of Thermospheric Composition Observed by NASA GOLD
We examine characteristics of the seasonal variation of thermospheric composition using column number density ratio ∑O/N2 observed by the NASA Global Observations of Limb and Disk (GOLD) mission from low-mid to mid-high latitudes. We found that the ∑O/N2 seasonal variation is hemispherically asymmetric: in the southern hemisphere, it exhibits the well-known annual and seminal pattern, with highs near the equinoxes, and primary and secondary lows near the solstices. In the northern hemisphere, it is dominated by an annual variation, with a minor semiannual component with the highs shifting towards the wintertime. We also found that the durations of the December and June solstice seasons in terms of thermospheric composition are highly variable with longitude. Our hypothesis is that ion-neutral collisional heating in the equatorial ionization anomaly region and auroral Joule heating play substantial roles in this longitudinal dependency.
HIWIND Balloon and Antarctica Jang Bogo FPI High Latitude Conjugate Thermospheric Wind Observations and Simulations
The data files contain thermospheric wind observations and simulations at JBS observatory and HIWIND.
Ionosphere-Thermosphere Data Published in "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms"
<p>This dataset supports the Journal of Geophysical Research publication "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms" by Qian et al., 2019. The data files are selected output and related analyses from the thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM). The format of the data files are either IDL save files or NetCDF files or ASCII.</p>
Data for "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
GITM Simulation results for the paper "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
Static and Dynamic Model Calibration for Upper Thermosphere Determination (2023SW003810)
<p>The dataset contains the background and static calibration models, associated with the figure data and routines. </p>
Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method
<p>This dataset supports the article "Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method" . The data are provided in MatLab format.</p>
Thermospheric density form GRACE-FO satellite
Open the record for dataset details and reuse information.
Impact of Arctic and Antarctic Sudden Stratospheric Warmings on Thermospheric Composition
<p>WACCM-X output files</p> <p>SABER Geostrophic Wind</p> <p>F10.7 cm solar flux and Kp index</p> <p>GOLD <span>Σ</span>O/N2</p> <p>GUVI <span>Σ</span>O/N2</p>
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
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
Data for Magnetosphere-Ionosphere-Thermosphere Coupling Study at Jupiter Based on Juno's First 30 Orbits and Modeling Tools
<p>Data used in the code associated to the manuscript "Magnetosphere-Ionosphere-Thermosphere Coupling Study at Jupiter Based on Juno’s First 30 Orbits and Modeling Tools", by Al Saati et al. (2022, Journal of Geophysical Research - Space Physics, https://doi.org/10.1029/2022JA030586). Please read the documentation associated with the corresponding code.</p>
An index description of the general characteristics of thermospheric density based on the Two-Line-Element datasets and the Spectral Whitening Method
<p>Data and code used in "An index description of the general characteristics of thermospheric density based on the Two-Line-Element datasets and the Spectral Whitening Method"</p>
Diurnal- and zonal-mean (DZM), and solar-synchronous tidal components, of Ar and N2 densities in Mars thermosphere
<p>This data set consists of diurnal- and zonal-mean (DZM) amplitudes, and DW1 and SW2 amplitudes and phases, of Ar and N2 densities derived from multi-year binning, averaging and sinusoidal decomposition of measurements made by the NGIMS instrument on the MAVEN satellite at Mars.</p> <p>The data are provided as a function of height (every 5 km, 160-250 km), latitude (60S-60N) and Ls (0-360) in 6 IDL .sav files.</p> <p>More details are found in the README_AllC_DZM_MIG.txt file, including information on how to read the files.</p> <p>This data set is made available in connection with the following publication: </p> <p>Forbes, J.M., Zhang, X., Fang, X., and Benna, M. (2024). Zonal-mean N2 and Ar Densities and Temperatures in Mars Thermosphere from MAVEN. Journal of Geophysical Research: Space Physics, 129, e2024JA032979. https://doi.org/10.1029/2024JA032979</p>
Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves
<div>---------------------</div> <div>GENERAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>1. Title of Dataset: Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves</div> <div> </div> <div>2. Authors: Nakagawa, England, et al.</div> <div> </div> <div>3. Contact information: hiromu.nakagawa.c1@tohoku.ac.jp</div> <div> </div> <div>4. Date of data collection: April 2024</div> <div> </div> <div> </div> <div>---------------------</div> <div>DATA & FILE OVERVIEW</div> <div>---------------------</div> <div> </div> <div>1. Original MAVEN data access:</div> <div> </div> <div>The MAVEN/NGIMS (level-2, version-8, revision-1) are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://atmos.nmsu.edu/data_and_services/atmospheres_data/MAVEN/ngims.html.</div> <div> </div> <div>As for references in Figures 2-4, the MAVEN/MAG Calibrated data are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://pds-ppi.igpp.ucla.edu/search/view/?id=pds://PPI/maven.mag.calibrated.</div> <div> </div> <div> </div> <div>2. File List:</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div> </div> <div>---------------------</div> <div>DATA-SPECIFIC INFORMATION</div> <div>---------------------</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>This includes the geometric information and the calculated correlate coefficient between neutrals and ions in all profiles applied in this study. The first header provides the information in the file: Orbit, Year, Month, Day, Hour, Minute, Unix time, SZA(deg), Lon(deg), Lat(deg), LST(hr), CC(CO2-N2), CC(CO2-CO2+). The geometric information corresponds to those at altitude around 190 km. The last two columns represents the correlate coefficients between CO2 and N2 and between CO2 and CO2+. </div> <div> </div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-specific case. </div> <div> </div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-specific case. </div> <div> </div> <div>---------------------</div> <div>METHODOLOGICAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>Period to analysis: from March 2015 to August 2020 (orbit number from 713 to 11881)</div> <div> </div> <div>Number of files (CO2): 8,911</div> <div> </div> <div>Number of files (N2): 8,913</div> <div> </div> <div>Number of files (CO2+): 7,053(?)</div> <div> </div> <div>Data selection:</div> <div> </div> <div>1. Neutral species with inbound valid (IV)</div> <div> </div> <div>2. Ion species with SCP (quality flag=0)</div> <div> </div> <div>3. Simultaneous observations of CO2, N2, and CO2+ at altitudes 170-210 km</div> <div> </div> <div>4. All three species CO2, N2, and CO2+ are valid with data sample >10 points in a single orbit.</div> <div> </div> <div>Total number of files after data selection = 3,018</div> <div> </div> <div>Fitting:</div> <div> </div> <div>4th-order polynomial fit to extract the perturbation components of density in the range between 160 and 220 km.</div> <div> </div> <div> </div> <div>Correlation coefficient:</div> <div> </div> <div>The correlation coefficients between perturbations are calculated in the range between 170 and 210 km.</div> <div> </div> <div>Ion-neutral coupling cases whose correlation coefficient between CO2 and CO2+ larger than 0.7: Total number = 839</div> <div> </div> <div>Ion-specific case whose correlation coefficient between CO2 and CO2+ smaller than 0.2: Total number = 823</div> <div> </div> <div>#We also define CME cases based on Lee et al. (2017): Total number = 31 cases</div> <div> </div>
Simulation data of 'Effects of Nonmigrating Diurnal Tides on the Na Layer in the Mesosphere and Lower Thermosphere'
<p>The simulation data of the manuscript 'Effects of Nonmigrating Diurnal Tides on the Na Layer in the Mesosphere and Lower Thermosphere'.</p>
Data Products for "The Upper Atmosphere of Uranus from Stellar Occultations II: Revised Temperatures in the Upper Stratosphere and Lower Thermosphere"
<p>From the README file:</p> <p>Organization of data products connected to Saunders et al. (2023, PSJ) and Saunders et al. (2024, PSJ).</p> <p>/forward_modeling_results.csv -- Anderson-Darling test values and critical values for each comparison between Voyager 2 profiles and observed stellar occultation light curves. Voyager 2 profiles were forward modeled into stellar occultation light curves to enable a direct comparison to observed, Earth-based stellar occultation profiles using the Anderson-Darling test of normality. Critical values are provided. See Section 3 of Saunders+23 and Section 2 of Saunders+24.</p> <p>/original_occultation_profiles/ -- Previously published atmospheric profiles from Earth-based stellar occultations. Data were extracted using a data extraction software on published papers. The citation for each data source is provided below. <br>/original_occultation_profiles/1977* -- Elliot et al. (1979)<br>/original_occultation_profiles/1981* -- French et al. (1983)<br>/original_occultation_profiles/1982-04* -- Sicardy et al. (1985)<br>/original_occultation_profiles/1982-05* -- French et al. (1987)<br>/original_occultation_profiles/1983* -- Elliot et al. (1987)</p> <p>/reprocessed_occultation_profiles/ -- All atmospheric profiles resulting from reprocessing the 26 occultation profiles.<br>/reprocessed_occultation_profiles/profiles/ -- Only the atmospheric profiles.<br>/reprocessed_occultation_profiles/profiles/* -- Each individual profile, in original vertical resolution. Columns: radius [km] (from center of Uranus), temperature [k], pressure [microbar], number density [m^-3], refractivity, scale_height [km] (pressure scale height H = kT/mg), y [km] (close-approach distance of the line connecting the viewer and the occulted star to the center of Uranus, see Saunders+23 for description). Units are provided in column headers.<br>/reprocessed_occultation_profiles/errors/ -- Only the errors for the atmospheric profiles.<br>/reprocessed_occultation_profiles/errors/* -- 1-sigma srrors for each individual profile.</p> <p>/atmospheric_models/ -- One-dimensional atmospheric model products. See Section 5 of Saunders+24.<br>/atmospheric_models/model_parameters/model_constants -- Values of constants used in the models. See Table 3 of Saunders+24.<br>/atmospheric_models/model_parameters/model_parameters -- Values and uncertainties of model parameters. See Table 3 of Saunders+24.<br>/atmospheric_models/model_profiles/* -- Profiles for the 9 models provided in Appendix C of Saunders+24. "Average" profiles are fit to the average of the 26 reprocessed stellar occultations; "cool" profiles are fit to the lower bound of the reprocessed occultations; "warm" profiles are fit to the upper bound of the reprocessed occultations. "Best-fit" profiles are the best fit model results; "lower" profiles are the lower bound of the family of generated profiles, meant to serve as a range of uncertainty; "upper" profiles are the upper bound. See Table 3 and Appendix C in Saunders+24 for more information. Units are provided in column headers.</p> <p> </p>
Effects of Energetic Electron and Proton Precipitations on Thermospheric Nitric Oxide Cooling during shock-led Interplanetary Coronal Mass Ejections
<p>Satellite measurements have revealed significant enhancement of 5.3-µm nitric oxide (NO) emission during shock-led interplanetary coronal mass ejections (ICMEs). Great discrepancies in modeled neutral density occur during these events, and may be attributed to the abnormally high NO cooling. Meanwhile, the relative significance of protons, soft electrons, and keV-electrons to NO emission is yet to be well determined. The goal of this study is to identify the contribution of electron and proton precipitations to the thermospheric NO cooling by using the Defense Meteorological Satellite Program (DMSP) data. The observed energetic electrons and protons (0.1–30.2 keV) during 36 shock-led ICME events in 2002–2010 are binned into geomagnetic grids to provide statistical distributions of the particle precipitation for polar regions. The distributions are incorporated into the Global Ionosphere-Thermosphere Model. The results show that electrons play a dominant role to NO cooling, but protons are also important and contribute to up to a quarter of NO cooling by electrons and ions combined. NO cooling enhancement during the events is proportional to the level of energy flux and is dominated by the electrons in the energy band of 1.4–3.1 keV. Both total electron content (TEC) and NO cooling enhance at the source regions, but they have different lifetime and correlation with the particle precipitations. Generally, NO cooling and TEC enhancements have a positive correlation with the precipitating energy. Cross correlation shows that particle precipitations have more direct and instantaneous impact on TEC while it takes longer for the atmosphere to heat up for cooling to proceed.</p>
2005 August 24 MAGE thermosphere-ionosphere results
<p>Results for the thermosphere-ionosphere component of the Multiscale Atmosphere Geospace Environment (MAGE) model at 0.625 degree resolution for 2005 August 24 geomagnetic storm.</p>
2005 August 24 TIEGCM-Weimer thermosphere-ionosphere results
<p>Results for the thermosphere-ionosphere from the Thermosphere-Ionosphere Electrodynamic General Circulation Model (TIEGCM) for 2005 August 24 geomagnetic storm.</p>
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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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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.