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31 results for “Electron Precipitation”
Data for: Ring Current Electron Precipitation During the 17 March 2013 Geomagnetic Storm: Underlying Mechanisms and Their Effect on the Atmosphere
<p>All data are included as MATLAB figure files, png files and MATLAB MAT files.</p><p>File precipitated_flux.mat contains a 4-D array of values of precipitated electron flux in [1/(s cm^2 keV)] for 289 time points from 16 March 2013 to 19 March 2013, with a 15 min time step; 100 values of energy in a range from 10 keV to 1 MeV, with a 10 keV step; on a spatial grid of 28 by 49 (P, R).</p><p>netCDF data can be opened with a variety of software tools, including Matlab, Origin or Python.</p>
Dataset for Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis by T.P. O'Brien et al. submitted to J. Geophysical Res.
<p>Technical reports and data sets for the the paper Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis. Additional AC6 information can be found at rbspgwy.jhuapl.edu/ac6 and at spdf.gsfc.nasa.gov/pub/data/aaa_smallsats_cubesats/aerocube/aerocube-6/. AC6 data have also been ingested into the main CDAWeb database at cdaweb.gsfc.nasa.gov. Source code related to this data set can be found at https://github.com/tpoiii/dipole_tracer_ac6, or DOI: 10.5281/zenodo.6011631.</p>
FAST loss-cone electron precipitation database
<p>A FAST electron precipitation database derived from FAST EESA observations, covering beginning of mission (October 1996) through 2009.</p> <p>Number flux ('j') and energy flux ('je') moments are produced by integrating EESA measurements over all energies above 70 eV<br> up to the EESA detector limit (30 keV), and over all pitch angles within the earthward portion of the loss cone (see references). </p> <p>================================<br> EXPLANATION OF DATAFRAME COLUMNS<br> ================================</p> <p>'j' : units of #/cm^2-s (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> 'je' : units of mW/m^2 (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> <br> 'jerr' : units of #/cm^2-s (Number flux uncertainty, calculated using the Gershman et al (2015) method)<br> 'jeerr' : units of mW/m^2 (Energy flux uncertainty, calculated using the Gershman et al (2015) method)<br> <br> 'orbit' : FAST orbit number<br> 'alt' : FAST geodetic altitude, in km. (FAST altitude ranges from ~300-4180 km)<br> 'apexmlt' : Magnetic local time in Apex-110 coordinates (see Laundal and Richmond (2016))<br> 'apexmlat' : Magnetic latitude in Apex-110 coordinates (see Laundal and Richmond (2016))</p> <p>'shadowRegion110' : Integer indicator of the region of the Earth's shadow that FAST's field-line footpoint at 110-km altitude lands ind.<br> Takes on values [0,1,2], corresponding to ['Umbra','Penumbra','Sunlit']. <br> Calculated by mapping FAST's location to 110-km altitude in Apex coordinates and then following the methodology of Jia et al. (2015).</p> <p>'mono' = 0,1,2 : 'not monoenergetic','weak monoenergetic','strict monoenergetic'<br> 'broad' = 0,1,2 : 'not broadband','weak broadband','strict broadband'<br> 'diffuse' = 0,1 : 'not diffuse','diffuse'</p> <p>'mono', ' broad', and 'diffuse' follow the Hatch et al. (2016) FAST adaptation of the Newell et al. (2009) classification scheme<br> *NOTE: I do NOT force 'mono' and 'broad' to be exclusive categories! I consider it fine for precipitation to be identified as both 'broad' and 'mono'</p> <p>'drop' : Boolean indicating whether a row should be dropped from the DataFrame</p> <p>==========<br> REFERENCES<br> ==========<br> Gershman, D. J., Dorelli, J. C., F.-Viñas, A., & Pollock, C. J. (2015). The calculation of moment uncertainties from velocity distribution functions with random errors. Journal of Geophysical Research A: Space Physics, 120(8), 6633–6645. https://doi.org/10.1002/2014JA020775</p> <p>Hatch, S. M., Chaston, C. C., & LaBelle, J. (2016). Alfvén wave-driven ionospheric mass outflow and electron precipitation during storms. Journal of Geophysical Research: Space Physics, 121(8), 7828–7846. https://doi.org/10.1002/2016JA022805</p> <p>Hatch, S. M., Labelle, J., Lotko, W., Chaston, C. C., & Zhang, B. (2017). IMF control of Alfvénic energy transport and deposition at high latitudes. Journal of Geophysical Research: Space Physics, 122(12). https://doi.org/10.1002/2017JA024175</p> <p>Jia, X., Xu, M., Pan, X., & Mao, X. (2017). Eclipse Prediction Algorithms for Low-Earth-Orbiting Satellites. IEEE Transactions on Aerospace and Electronic Systems, 53(6), 2963–2975. https://doi.org/10.1109/TAES.2017.2722518</p> <p>Laundal, K. M., & Richmond, A. D. (2016). Magnetic Coordinate Systems. Space Science Reviews, 1–33. https://doi.org/10.1007/s11214-016-0275-y</p> <p>Newell, P. T., Sotirelis, T., & Wing, S. (2009). Diffuse, monoenergetic, and broadband aurora: The global precipitation budget. Journal of Geophysical Research, 114, A09207. https://doi.org/http://dx.doi.org/10.1029/2009JA014326<br> </p>
Nanobeam electron diffraction dataset from ion irradiated DIN 1.4970 austenitic stainless steel with G-phase precipitates collected on pixelated TVIPS detector
<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam mode (NBED) from a sample of ion irradiated austenitic (FCC) stainless steel of the DIN 1.4970 specification, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p>The main features in this dataset are:</p> <ul> <li>a single crystal of the matrix is sampled, close to a 110 zone axis</li> <li>inside the matrix, irradiation induced G-phase precipitates of 10-20 nm in size can be found which contribute weakly to some of the diffraction patterns. From these patterns it is possible to derive the orientation relationship of the precipitates with respect to the matrix.</li> <li>irradiation also resulted in the formation of faulted frank loops, which also show up in some diffraction patterns.</li> </ul> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from DIN 1.4970 steel (composition by weight: 15% Ni, 15% Cr, 1.8% Mn, 1.2% Mo, 0.5% Ti, 0.5% Si, 0.1% C, Fe Bal.) with the intended application of nuclear fuel cladding material. The material was originally in the shape of thin walled tubes and cold worked to 24% (measured by cross sectional area reduction). The material was aged for 2 hours at 800 °C. It was then irradiated to 40 dpa surface damage as calculated using the SRIM program and the Kinchin and Pease model with displacement energy of 40 eV, using 4.5 MeV Fe<sup>2+</sup> ions with a flux of arround 9x10<sup>11</sup> ions.s<sup>-1</sup>.cm<sup>-2</sup>. The irradiation was performed at 600 °C. Full details on the material, irradiation conditions, and context can be found in:</p> <p>Cautaerts, N., Delville, R., Stergar, E., Pakarinen, J., Verwerft, M., Yang, Y., Hofer, C., Schnitzer, R., Lamm, S., Felfer, P., & Schryvers, D. (2020). The role of Ti and TiC nanoprecipitates in radiation resistant austenitic steel : A nanoscale study. <em>Acta Materialia</em>, <em>197</em>, 184–197. https://doi.org/10.1016/j.actamat.2020.07.022</p> <p>A TEM sample was prepared by regular focused ion beam (FIB) lift-out techniques in a Ga-ion FIB. Additional details on the dataset can be found in the paper and supplementary materials of</p> <p>Cautaerts, N., Rauch, E. F., Jeong, J., Dehm, G., & Liebscher, C. H. (2021). Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector. <em>Scripta Materialia</em>, <em>201</em>, 113930. https://doi.org/10.1016/j.scriptamat.2021.113930</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>NBED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5). The probe diameter was ~ 1 nm with a semi-convergence angle of ~0.5 mrad. Data was collected on a TemCam-XF416 pixelated CMOS detector (TVIPS). The camera length as indicated in the operating software was 80 cm, and collected images were 1024 by 1024 in size (hardware binning of 4). The dataset comprises 260x200 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 50 GB in size and can be shared upon request to the author. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 256x256. A median filter was also applied to the data to remove pixel noise.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 260 x 200 pixels</p> <p>Image shape: 256 x 256 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01261 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation) and the pixel scale can be set through the axes manager. It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. The scan lines show "jitters" due to the unstable snake-scan pattern, hysteresis and instability.</p>
Chemical Effects Induced by Relativistic Precipitating Electrons
<p>Figures, data, and code used in my paper describing "Chemical Effects Induced by Relativistic Precipitating Electrons"</p>
Datasets for "A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation"
<p>These three datasets are supplemental material for the Geophysical Research Letters article, A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation.</p> <p> </p> <p>Data Set DS1. Te data plotted in Figure S1. This is the 3-beam averaged Te data described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS2. Te data errors plotted in Figure S1. This is the 3-beam averaged Te data propagated errors described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS3. THEMIS ASI data at the PFISR location used to calculate the heat flux plotted in Figure S1. The first row is the header. Time, the first column, is in the format: Year-Month-Day/Hour-Minute-Second. The following columns are: geographic longitude, geographic latitude, energy flux for a Gaussian distribution, mean energy for a Gaussian distribution, energy flux for a Maxwellian distribution, mean energy for a Maxwellian distribution. Units are included in the header.</p>
Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons
<p>Figures, data, and code used in my paper describing "Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons"</p>
Relativistic Electron Precipitation Events (driven by waves or field line scattering) from POES 2-second data
<p>This repository contains the list of relativistic electron precipitation from POES data since 2012.</p> <p>Please refer to the read_me file for further details.</p> <p> </p> <p><strong>You are free to use this for your research. However, before doing so, please contact Luisa Capannolo at luisacap@bu.edu.</strong></p> <p> </p> <p>This dataset is associated with the paper under review titled "Properties of Relativistic Electron Precipitation: A Comparative Analysis of Wave-Induced and Field Line Curvature Scattering Processes" by Capannolo, Staff, Li, Duderstadt, Sivadas, Petitt, Elliot, Qin, Shen, and Ma.</p>
Sensitive dependence of ultrarelativistic electron precipitation on EMIC wave frequency
<p>This is the data set used to create the figures in the paper by Lee (2021) in Journal of Geophysical Research as obtained from numerical calculations of equations of motion for ultrarelativistic electrons under electromagnetic ion cyclotron waves in the Earth's inner magnetosphere. </p>
Supporting data for Lin et al. "Diffuse Electron Precipitation Effects on Subauroral Polarization Streams"
<p>This dataset contains the necessary data supporting the paper titled "The role of diffuse electron precipitation in the formation of subauroral polarization streams", submitted by Lin et al., 2021. The dataset includes 1. the solar wind/IMF and SYMH indices used to drive the Multiscale Atmosphere-Geospace Environment (MAGE) model; 2. Electron precipitation energy flux (EnFlux), cross track ion drift, field-aligned current (FAC), and Pedersen conductance (SigmaP) sampled along three trajectories of DMSP F18 satellite during 17 March 2013 (08:28-08:38 UT, 11:18-11:28 UT, 13:00-13:10 UT), which are used to compare with the DMSP measurements; 3. Plasma pressure distribution in the magnetospheric equatorial plane, and EnFlux, FAC, westward ion drifts, at 06 UT, 07 UT, 09 UT, and 11 UT from the baseline run of MAGE simulation; 4. Two-dimensional distributions of EnFlux, FAC, zonal ion drifts and their latitudinal samplings at magnetic local time of 20 h from two MAGE simulation runs, one being the same from above mentioned, the other being a controlled run in which diffuse electron precipitation was turned off; 5. Zonal ion drifts, magnetospheric equatorial plane plasma density, and boundary locations of the EnFlux and downward FAC in both the ionosphere and magnetosphere; 6. Two-dimensional distributions of EnFlux, FAC, zonal ion drifts and their latitudinal samplings at magnetic local time of 20 h from another two MAGE simulation runs, one using the Fedder precipitation model, the other using the RCM+Zhang precipitation model with a loss rate of one-tenth.</p>
Precipitating electron fluxes of the magnetosphere-ionosphere coupling processes
<p>Numerical data for he precipitating electron fluxes, at altitude of 800 km, of the magnetosphere-ionosphere coupling processes, calculated for both Maxwell and Kappa distributions - used in Khazanov et al. (2018), in review.</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>
X-ray Signatures of Lightning-induced Electron Precipitation
<p>Figures, data, and code used in my paper describing X-ray signatures of lightning-induced electron precipitation (LEP)</p>
POES/MetOp Daily-Averaged Precipitating Electron Fluxes
<p>Daily-averaged precipitating electron fluxes as a function of energy and L-shell from the MPE data set by Pettit et al., 2021.</p>
Dataset for "Scanning precession electron diffraction data analysis approaches for phase mapping of precipitates in aluminium alloys"
<p>Data needed to reproduce the results in "Scanning precession electron diffraction data analysis approaches for phase mapping of precipitates in aluminium alloys" published in Ultramicroscopy. The codes and notebooks can be found at 10.5281/zenodo.8321258.</p>
Supporting data for "POES/MEPED angular response functions and the precipitating radiation belt electron flux"
<p>Data sets included as supporting information with the publication to appear in JGR - Space Physics. Numerical values are provided for the POES/MEPED energy and angular response functions, derived by Geant4 simulation, for application to electron and proton radiation belt data interpretation.</p>
APES electron precipitation data from the VISIONS-2 Sounding rocket flight
<p>This is a Matlab save file containting the data from the APES instrument from the VISIONS-2 sounding rocket flight. It contains the precipitating electron flux in both units of number flux and energy flux.</p>
Determining latitudinal extent of energetic electron precipitation using MEPED on-board NOAA/POES
<p>Supporting information for the paper titled: Determining latitudinal extent of energetic electron precipitation using MEPED on-board NOAA/POES</p>
Data for The principal role of chorus ducting for night-side relativistic electron precipitation
<p>Data for Figure 2~4 in paper "The principal role of chorus ducting for night-side relativistic electron precipitation".</p>
Model-based Investigation of Electron Precipitation-driven Density Structures and their Effects on Auroral Scintillation
<p>This folder contains the precipitation modeling results and the camera dataset used for this study. It also includes the details of the supplementary material cited in the text in sections 3 (table params), 3.1 (minimum total energy flux), 4.1 (minimum total energy flux), and 4.2 (density profiles for different characteristic energies). </p>
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