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85 results for “thermosphere”
Results of "Storm Time Data Assimilation in the Thermosphere Ionosphere with TIDA" CHAMP, GRACE-A, and GRACE-B neutral density data assimilation into CTIPe for 2003 Halloween Storms
<p># README</p> <p>Results for the article "Storm Time Neutral Density Assimilation in the Thermosphere Ionosphere with<br> TIDA".</p> <p>There are three storms presented here:</p> <p>1. 2003 storm: October 26-30, 2003<br> 2. 2004 storm: July 26-30, 2004<br> 3. 2002 storm: September 27 - October 2, 2002.</p> <p>For each of these three storms, there are four runs. For each storm, we've done a run<br> assimilating all satellites, and then three more assimilating each satellite individually and<br> comparing against the others.</p> <p>Each directory name before underscore identifies the date the run was<br> started. After the underscore identifies the date assimilated.</p> <p>This readme uses the notation that in curly brackets the satellites assimilated are given.</p> <p>- a stands for GRACE-A<br> - b stands for GRACE-B<br> - c stands for CHAMP</p> <p>The runs are summarized below:</p> <p>1. 2003 storm: {a, b, c}: 2021-12-29T1259_...<br> 3. 2003 storm: {a}: 2022-01-01T1654_...<br> 4. 2003 storm: {b}: 2022-01-02T1423_...<br> 2. 2003 storm: {c}: 2021-12-29T2342_...</p> <p>5. 2004 storm: {a, b, c}: 2022-01-03T1614_...<br> 6. 2004 storm: {a}: 2022-01-04T1027_...<br> 7. 2004 storm: {b}: 2022-01-04T2144_...<br> 8. 2004 storm: {c}: 2022-01-05T0502_...</p> <p>9. 2002 storm: {a, b, c}: 2022-01-02T2025_...<br> 10. 2002 storm: {a}: 2022-01-05T1056_...<br> 11. 2002 storm: {b}: 2022-01-05T1854_...<br> 12. 2002 storm: {c}: 2022-01-06T1017_...</p> <p>## Example result directory</p> <p>2021-12-29T1259_d2003-10-27<br> ├── density_champ_density.csv<br> ├── density_grace-a_density.csv<br> ├── density_grace-b_density.csv<br> └── inputs<br> ├── reference_2003-10-27_input.txt<br> └── special_2003-10-27_input.txt<br> <br> 1 directory, 5 files</p> <p>## Zenodo doesn't support directories</p> <p>So, the file structure has been flattened in the following way:</p> <p>Before: ./aaa/bbb/ccc.png</p> <p>After: ./aaa-bbb-ccc.png</p> <p>https://unix.stackexchange.com/~/45659</p> <p> </p>
Results of "Ensemble Kalman Filter for the Thermosphere Ionosphere", CHAMP neutral density assimilation into CTIPe for March 20, 2007
<p>These data are the result of assimilating neutral density measurements from the CHAMP satellite on March 20, 2007 into the CTIPe model and and comparison of results with observations made by the GRACE satellite. Data assimilation is performed in three configurations: Configuration (i) is ds, state correction. Configuration (ii) is dfds, both input estimatation and state correction. Configuration (iii) is df, estimation of model inputs only.</p> <p>This data is associated with the following publication:</p> <blockquote> <p>Codrescu S., M.V. Codrescu, and M. Fedrizzi (2018), An Ensemble Kalman Filter for the Thermosphere-Ionosphere, Space Weather, 16, doi:<a href="http://dx.doi.org/10.1002/2017SW001752" title="Link to external resource: 10.1002/2017SW001752">10.1002/2017SW001752</a>.</p> </blockquote> <p> </p>
Dataset of the paper Zeitler et al. (2021) : Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions
<p>The dataset consists of two .h5 files. "Dataset_DOGSOC_GROOPS.h5" contains the 12-hour thermospheric density scale factors of the satellites Starlette, Stella, and Larets of Chapter 4.2. Each path includes a file with three columns. The first column contains the time vector in JD2000.0. The second column and third column contain the scale factor time series (unfiltered, smoothed with a 10-day moving average filter). The following scale factor time series are available:</p> <ul> <li>DOGSOC/starlette</li> <li>DOGSOC/stella</li> <li>DOGSOC/larets</li> <li>GROOPS/starlette</li> <li>GROOPS/stella</li> <li>GROOPS/larets</li> </ul> <p> </p> <p>"Dataset_SLR_ACC.h5" contains the 12-hour thermospheric density scale factors from SLR measurements (DOGS-OC) to the satellites Starlette, WESTPAC, Stella, and Larets and from accelerometer measurements of the satellites GRACE and CHAMP of Chapter 4.1. Each path includes a file with three columns. Again, the first column contains the time vector in JD2000.0, and columns 2 and 3 contain the thermospheric density scale factors (unfiltered, smoothed with a 10-day moving average fitler). The following scale factor time series are available:</p> <ul> <li>ACC/CHAMP</li> <li>ACC/GRACE</li> <li>SLR/starlette</li> <li>SLR/westpac</li> <li>SLR/stella</li> <li>SLR/larets</li> </ul> <p>Further information about the data can be found in the file "description_of_datasets_v1.txt" or in the paper Zeitler et al. (2021): Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions. Journal of Geophysical Research: Space Physics.</p>
Nitric oxide (NO) data set (60--160 km) from SCIAMACHY mesosphere--lower thermosphere limb scans
<p><strong>Overview</strong><br> Contains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY mesosphere--lower thermosphere (MLT, 50--150 km) limb scans.</p> <p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The Mesosphere--Lower Thermosphere (MLT) measurement mode was carried out from 07/2008 until the end of the mission for one day every 15 days. This data set comprises 84 days of SCIAMACHY MLT NO measurements, each<br> containing about 15 orbits.</p> <p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br> https://earth.esa.int/web/guest/data-access/browse-data-products.<br> The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br> https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p> <p>The SCIAMACHY NO data were compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties.</p> <p><strong>Acknowledgements</strong><br> The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products.</p> <p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various international institutions: University of Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL), University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground segment. Support with respect to mission planning and operations is given by the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>
Climatological Tidal Model of the Thermosphere - CTMT
<p>For detailed description, see <a href="http://dx.doi.org/10.1029/2011JA016784">Oberheide et al., 2011</a> and the <a href="http://globaldynamics.sites.clemson.edu/articles/ctmt.html">CTMT webpage</a>.</p> <p>Briefly, CTMT is based on tidal temperature and wind observations made in the MLT region by SABER and TIDI on TIMED that are extended into the thermosphere using Hough Mode Extension (HME) modeling. The latter can be thought of as constraining a tidal model with observations and produces self-consistent tidal fields in temperature, neutral density, and zonal, meridional and vertical winds from pol-to-pole and from 80-400 km. A monthly tidal climatology is compiled from averaged 2002-2008 TIMED observations. CTMT accounts for contributions from solar radiation absorption in the troposphere and stratosphere, tropospheric latent heat release, and non-linear wave-wave interactions occurring in the MLT or below. It is valid for a solar radio flux of F10.7 = 110 sfu and includes the 6 (8) most important migrating and nonmigrating diurnal (semidiurnal) tidal components. As such it is suitable for driving upper atmosphere models that require self-consistent tidal fields in the MLT region as a lower boundary condition or to study the effects of tidal density variations in the re-entry region, to name just a few examples. Thermospheric tidal forcing occurring above the MLT is not accounted for. CTMT, therefore, does not capture (i) migrating tides forced in-situ by the absorption of solar EUV radiation, and (ii) nonmigrating tides forced in the thermosphere.</p>
Mars Thermospheric Water Abundance in Mars Years 32-36
<p>This file contains the derived thermospheric water abundances and mixing ratios along with the associated solar longitude, latitude, solar zenith angle, local time and global dust optical depth.</p>
Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations
<p>SD-WACCM data used in "<strong>Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations"</strong></p>
Data archive accompanying "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, doi:10.1002/2017SW001785]
<p>This archive contains the data used to create the plots presented in "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, SWx, doi:10.1002/2017SW001785].</p> <p>Format: MATLAB save file</p> <p>Contents:</p> <p>1. CHAMP and GRACE-A accelerometer-derived densities and ephemeris;</p> <p>2. TIE-GCM GPI model output sampled on both satellites;</p> <p>3. IRIDEA prior and posterior model output sampled on both satellites;</p> <p>4. Short description and units for all variables</p>
WACCM-X simulation output in support of publication "Impact of upward propagating migrating diurnal and semidiurnal tides on the ionosphere-thermosphere seasonal variation"
<p>This dataset contains simulation output from the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X) in support of the publication "Impact of upward propagating migrating diurnal and semidiurnal tides on the ionosphere-thermosphere seasonal variation". Data files include the simulation results for a five-member ensemble of free-running simulations, simulations without the upward propagating diurnal migrating tide (DW1), and simulations without the upward propagating semidiurnal migrating tide (SW2). </p>
Evaluating F10.7 and F30 Radio Fluxes as Long-Term Solar Proxies of Energy Deposition in the Thermosphere
<p><span>We use model simulations and observations to examine how well the F10.7 and F30 solar radio fluxes represent solar forcing in the thermosphere during the last 60 years of weakening solar activity. We found that increased saturation of F10.7 during the last two extended solar minima leads to an overestimation of solar energy deposition, which manifests as a change in the linear relation between thermospheric parameters and F10.7. On the other hand, the linear relation between thermospheric parameters and F30 remains nearly the same throughout the whole studied period because of a recently found relative increase of F30 with respect to F10.7. Therefore, F30 is a more consistent proxy than F10.7 during the last 60 years. We note that continued evaluation is needed to see how well F10.7 and F30 will serve as solar proxies in the future when solar activity may start increasing toward the next grand maximum.</span></p>
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 UpperMesosphere 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>
Conjunctions between ICON-MIGHTI and 4 meteor radars, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to meteor radars" by Harding et al. (2020, Submitted)
<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 los_wind (the line of sight wind profiles observed by ICON-MIGHTI) and los_wind_r (the wind profiles observed by the meteor radar, interpolated in time and altitude to the MIGHTI sample, and projected onto the MIGHTI line of sight). Dimensions are "time" and "row" (which refers to the row of the MIGHTI CCD, roughly equivalent to altitude. Velocity units are m/s, distances are km, and lat/lon are in degrees. More information can be found in the paper.</pre>
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>
Improving estimates of the ionosphere during geomagnetic storm conditions through assimilation of thermospheric mass density
<p>Swarm A/B/C neutral mass density normalized to 400 km to be assimilated by the CTIPe physics based model coupled with the thermosphere ionosphere data assimilation scheme (TIDA). Swarm-A is assimilated and B/C are used for validation purposes. The selected period is March 2015 that contains the St. Patrick's Day storm 2015 between 16-18 of that month.</p>
CEDAR Project: A Whole-Atmospheric Perspective on Connections between Intra-Seasonal Variations in the Troposphere and Thermosphere
<p>This collaborative award is aimed at studying the relationship between the variability of thermospheric winds to the variability caused by wave structures generated in the tropical troposphere. This coupling is driven by wave excitation by deep convection in the tropical troposphere that can propagate vertically into the thermosphere. Tropospheric convection associated with the Madden‐Julian Oscillation (MJO), the dominant mode of intra-seasonal variability in tropical convection and circulation, is known to modulate the intensity of upward‐propagating gravity and Kelvin waves. Previous work demonstrated that a 90-day oscillation in tropospheric convection during 2009-2010 was imprinted on both thermospheric mean winds and the eastward propagating wavenumber 3 diurnal (DE3) tidal amplitudes. This modulation was observed by the GOCE and CHAMP satellites and modeled with the TIME-GCM. The research effort would broaden participation by involving and training two undergraduate student interns through the University of Colorado BOLD internship program that focuses on promoting the recruitment, retention, and development of traditionally underrepresented engineering students.<br> <br> The new research will follow up on the results obtained in recent studies that demonstrated that strong coupling between the troposphere and the thermosphere occurs on intra-seasonal timescales. The award will address the following questions:<br> Q1: How frequent, prevalent, and persistent are correlations between 30 to 100-day variations in the three regions of troposphere, mesosphere, and thermosphere, during the past two decades?<br> Q2: What plausible roles do large-scale upward propagating waves play in dynamically coupling tropical tropospheric intra-seasonal variability into the thermosphere?<br> Q3: Is there any observational evidence suggesting a connection between this troposphere-thermosphere intra-seasonal coupling and MJO, Quasi-Biennial Oscillation (QBO) and El Niño-Southern Oscillation (ENSO)?<br> The combination of available upper atmosphere satellite data with ground-, and model-based datasets would be studied to provide insight into whether the intra-seasonal variations in the waves are caused by variability in the tropospheric sources or by wave-mean flow interactions. In the case of the latter, the study would determine at which heights these interactions are occurring. This study will determine the contribution of global-scale wave coupling between the troposphere and the thermosphere, thus addressing outstanding issues of fundamental importance to the CEDAR community.</p> <p>This research primarily involves performing correlation analyses and extracting wave information from satellite (CHAMP, GOCE, Swarm-C, TIMED, OLR), ground (Kauai, Christmas Island, and Adelaide, Maui, Urbana, and Chile), and model (MERRA-2, TIE-GCM, and WACCM-X) -based datasets and processing, plotting, data produced in standard ways to draw scientific conclusions. </p> <p>This project does not generate any new physical or observational data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles are followed by making data resources (e.g. code/software and metadata) resulting from this project publicly available.</p> <p>GOCE, CHAMP, Swarm-C data (V01) are available at ftp://anonymous@thermosphere.tudelft.nl/. SABER data (V2.0, L2B) are available at http://saber.gats-inc.com/data.php. Tl DI data (V3.7) are available at http:// timed.hao.ucar.edu/tidi/. OLR data are available at https://psl.noaa.gov/data/gridded/ data.interp_OLR.html. F10.7 data are available at http://www.swpc.noaa.gov/content/data-access. kp/ap data are available at ftp:// ftp.gfz-potsdam.de/pub/home/obs/ kp-ap/.</p>
Investigation of the southern hemisphere mid-high latitude thermospheric ∑O/N2 responses to the Space-X storm
<p>This data sets are the data used to plot the figures in the above mentioned paper (Figure 3 to 6)</p> <p>All files are in dimension 288*144*6, 288 stands for longitudes number from -180 to 180 with a resolution of 1.25</p> <p>144 stands for latitude numbers fro -88.75 to 88.75 with a resolution of 1.25. 6 stands for the time, 0:20, 2:20, 4:20, 7:20, 10:20 and 13:20 UT on DOY 34.</p> <p>dON2 stand for the percentage diff of column density ratio of O to N2 between DOY 34 and 32</p> <p>UN stands for zonal wind, VN stands for meridional wind, TN stands for neutral temperature</p> <p>QJO stands for Joule heating rate per unit mass near 160 km</p> <p>POTEN stands for ionosphere potential</p>
Simulation data for Response of the Thermosphere-Ionosphere System to an X-Class Solar Flare: March 30, 2022 Case Study
<p>GITM simulation results for the research article titled "Response of the Thermosphere-Ionosphere System to an X-Class Solar Flare: March 30, 2022 Case Study" submitted to JGR: Space Weather </p>
Data for publication "The Thermosphere was Poorly Predictable not Only During but also Before and After the Starlink Storm on 3-4 February 2022"
<p>This dataset are used to plot figures in the article "The Thermosphere was Poorly Predictable not Only During but also Before and After the Starlink Storm on February 3-4, 2022". Data files in CSV (comma-separated values) format contain modeling and observational values. Modeling values obtained from the Field Line Interhemispheric Plasma (FLIP) model and Arctic MERRA-2 Wind model. Observational values consist the ionosonde measurments, planetary geomagnetic (Kp) and solar activity indices (F10.7), variations of the solar wind parameters. Data files contain data for the period from February 1 to 9, 2022 and from December 21 to 23, 2021. </p>
Thermosphere Hough Mode Extensions (HMEs) for Solar Tides in Earth's Atmosphere
<p>The scientific basis for the atmospheric solar tide HME files contained here is described in Forbes et al. (2022). The directory "HME2022-Hough-UV functions-Tides" contains solutions to Laplace's Tidal Equation for the eastward(westward) propagating diurnal tides with zonal wavenumbers s = -1, -2, -3(+1, +2); the eastward(westward) propagating semidiurnal tides with zonal wavenumbers s = -1, -2, -3(+1, +2, +3, +4, +6); the zonally-symmetric (s = 0) diurnal and semidiurnal tides; and the westward-propagating terdiurnal tide with s = +3. In a widely-used abbreviated notation, these are, respectively: DE1, DE2, DE3, DW1, DW2; SE1, SE2, SE3, SW1, SW2, SW3, SW4, SW6; D0, S0; and TW3. These tides correspond to those measured in Earth's mesosphere and thermosphere. The directory "HME2022-output files-Tides" provides the amplitudes and phases (UT hour of maximum at 0 longitude) of eastward, southward, and vertical winds (m/s), temperatures (K), density perturbations relative to mean, and geopotential height (m) as a function of height (z, every ~4 km) and latitude (deg, every 3 deg), corresponding to several (2 to 7) HMEs for each of the aforementioned tides. Generally the number of HMEs corresponding to each tide is determined by the vertical wavelength Lz of each HME; HMEs with Lz < 30 km are excluded due to their inability to effectively penetrate into the thermosphere above 100 km altitude. The directory "HME_Legacy output files-Tides" includes a prior version of these HMEs that were used in several papers in the literature, but not publicly distributed. The data for each HME extend from pole to pole and from 0 to 400 km altitude. Each directory contains a README file containing further information on the data files.</p>
Thermosphere Hough Mode Extensions (HMEs) for Ultra-Fast Kelvin Waves (UFKWs)
<p>The scientific basis for the UFKW HME files contained here is described in Forbes et al. (2023). The directory "Hough-UV functions" contains solutions to Laplace's Tidal Equation for the s = -1 eastward-propagating first symmetric (Kelvin) modes with periods 2.0-5.0d (48hr-120hr), and a README file containing further information on the data files. The directory "UFKW output files" provides UFKW amplitudes and phases (UT hour of maximum at 0 longitude) of eastward, southward, and vertical winds (m/s), temperatures (K), density perturbations relative to mean, and geopotential height (m) as a function of height (z, every ~4 km) and latitude (deg, every 3 deg), corresponding to wave periods between 2.0d and 5.0d, and a README file containing further information on the data files. The data for each HME extend from pole to pole and from 0 to 400 km altitude.</p> <p>Forbes, J.M., Zhang, X., & Palo, S.E. (2023). UFKW propagation in the dissipative thermosphere. Journal of Geophysical Research: Space Physics, 128, e2022JA030921. https://doi.org/10.1029/2022JA030921</p>
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