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181 results for “meteors”
CONDOR Meteor Radar Horizontal Wind
<p><a href="http://alo.erau.edu/instrument/mr/index.php">CONDOR</a> is a multi-static meteor radar system with the main station at Cerro Pachón, Chile, next to the Andes Lidar Observatory (ALO) and two remote stations at <a href="https://www.lco.cl/">Las Campanas Observatory</a> (LCO) 137 km to the north and <a href="https://astroturismochile.travel/observatorio-cruz-del-sur/">Southern Cross Observatory</a> (SCO) near Combarbalá to the south. The system was built and installed by <a href="https://www.atrad.com.au/">ATRAD, Inc.</a> and funded by the U.S. National Science Foundation. It has been in operation since the summer of 2019. </p> <p>This dataset is the horizontal wind from <a href="http://alo.erau.edu/instrument/mr/index.php">CONDOR</a> meteor radar system at 1-hr temporal resolution from all three stations. It is one of the standard products of this meteor radar system. The data is converted from ATRAD native format into Matlab format that is self-explanatory when loaded into Matlab.</p>
Time-lapse of AARTFAAC detections of radio meteors in Perseids 2020
<p>LOFAR AARTFAAC images (integrated over all observing bands) during the Perseids meteor shower in the night 2020 August 12 --13. The large-scale diffuse emission is the Galactic plane. The brightest radio sources Cassiopeia~A and Cygnus~A have been subtracted, sometimes leaving some artefacts. In red, trajectories as computed from the CAMS BeNeLux optical observations are overlaid.</p> <p>Observing frequencies were in the range 30 to 60 MHz.</p>
Global Meteor Network observations of Starlink re-entry 2022-02-10
<p>This dataset contains video observations by some stations of the Global Meteor Network of the re-entry of the satellite StarLink-1668 above Spain on 2022-02-10 around 19:50 UTC.</p> <p>There are several types of files:</p> <ul> <li>FF files: these are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</li> <li>FR files: compressed video recordings of detected fireballs. These can be read with the RMS software.</li> <li>MP4 files: rendered movies of combined FF and FR files for selected stations.</li> <li>Platepar-files: these contain astrometry corresponding to the FITS files. These can be interpreted by the RMS software.</li> <li>ECSV files: these contain manually picked points (with SkyFit2.py from RMS) along the track of the reentry. For each point, time and apparent coordinates are recorded. These files can be interpreted by the WesternMeteorPyLib trajectory solver.</li> <li>trajectory-points.txt: solutions from the trajectory solver.</li> <li>reentry-map-v2.png: a rendered map of the trajectory (made in QGIS).</li> </ul> <p>The files can be processed with the software in https://github.com/CroatianMeteorNetwork/RMS and https://github.com/wmpg/WesternMeteorPyLib.</p>
Sprites observed with Global Meteor Network camera DE000C on 2022-06-30
<p>This dataset contains compressed video observations of sprites, made with one low-light video camera of the Global Meteor Network. The camera, DE000C, is located in Sörup, Northern Germany, and has pointing azimuth 217˚ (so South-West), elevation 39˚.</p> <p>The files are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</p> <p>The platepar files contain calibration data that can be read with the sofware at https://github.com/CroatianMeteorNetwork/RMS/. The astrometry contained in the fits files is derived from this and may be less accurate.</p> <p>A stack of the maxpixel images is also contained.</p>
SuperDARN meteor wind data for January 2019
<p>SuperDARN meteor wind data</p> <p>*.m.* - meridional</p> <p>*.z.* - zonal</p> <p>X/Y are in radar coordinates - most users can disregard. </p> <p> </p> <p>Supported by NSF #1934973</p> <p>Collaborative Research: Super Dual Auroral Radar Network (SuperDARN) Operations, Research and Community Support</p> <p> </p> <p> </p> <p>SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgment of data by its users. Guidelines for data acknowledgment are as follows:</p> <p>When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection.</p> <p>For all usage of SuperDARN data, users are asked to include the following standard acknowledgment text: “The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.”</p> <p>While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data.</p> <p>The SuperDARN Executive Council (see list in the README) must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.</p> <p> </p>
Seasonal and local time variation in the observed peak of the meteor altitude distributions by meteor radars
<p>These uploaded datasets support and appear in the the paper entitled "<strong>Seasonal and local time variation in the observed peak of the meteor altitude distributions by meteor radars</strong>" prepared by: </p> <p>E.C.M. Dawkins<sup>1,2</sup>, D. Janches<sup>1</sup>, G. Stober<sup>3</sup>, J.D. Carrillo-Sánchez<sup>1,2</sup>, R.S. Lieberman<sup>1</sup>, C. Jacobi<sup>4</sup>, T. Moffat-Griffin<sup>5</sup>, N.J Mitchell<sup>5,6</sup>, N. Cobbett<sup>5</sup>, P.P.Batista<sup>7</sup>, V.F. Andrioli<sup>7,8</sup>, R.A. Buriti<sup>9</sup>, D.J. Murphy<sup>10</sup>, J. Kero<sup>11</sup>, N. Gulbrandsen<sup>12</sup>, M. Tsutsumi<sup>13,14</sup>, A. Kozlovsky<sup>15</sup>, M. Lester<sup>16</sup>, J.-H. Kim<sup>17</sup>, C. Lee<sup>17</sup>, A. Liu<sup>18</sup>, B. Fuller<sup>19</sup>, D. O’Connor<sup>19</sup>, S.E. Palo<sup>20</sup>, M.J. Taylor<sup>21</sup>, J.Marino<sup>22</sup>, and N. Rainville<sup>20</sup>.</p> <p> </p> <p>1 ITM Physics Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, U.S.A.</p> <p>2 Department of Physics, Catholic University of America, DC, U.S.A.</p> <p>3 University Bern, Institute of Applied Physics, Microwave Physics, Bern, Switzerland</p> <p>4 Institute for Meteorology, Leipzig University, Germany</p> <p>5 British Antarctic Survey, Cambridge, U.K.</p> <p>6 University of Bath, Bath, U.K.</p> <p>7 National Institute for Space Research (INPE), São José dos Campos, SP, Brazil</p> <p>8 China-Brazil Joint Laboratory for Space Weather, NSSC/INPE, São José dos Campos, SP, Brazil</p> <p>9 Department of Physics, Federal University of Campina Grande, Campina Grande, PB, Brazil</p> <p>10 Australian Antarctic Division, Kingston, TAS, Australia</p> <p>11 Swedish Institute of Space Physics (IRF), Kiruna, Sweden</p> <p>12 Tromsø Geophysical Observatory, UiT - The Arctic University of Norway, Tromsø, Norway</p> <p>13 National Institute of Polar Research, Tachikawa, Japan</p> <p>14 The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan</p> <p>15 Sodankylä Geophysical Observatory, University of Oulu, Finland</p> <p>16 Department of Physics and Astronomy, University of Leicester, Leicester, U.K.</p> <p>17 Division of Atmospheric Sciences, Korea Polar Research Institute, Incheon, S. Korea</p> <p>18 Center for Space and Atmospheric Research and Department of Physical Sciences, Embry-Riddle Aeronautical University, Daytona Beach, Florida, U.S.A.</p> <p>19 Genesis Software, Pty Ltd., Adelaide, SA, Australia</p> <p>20 Colorado Center for Astrodynamics Research (CCAR), Ann and H.J. Smead Aerospace Engineering Sciences, College of Engineering and Applied Sciences, University of Colorado Boulder, Boulder, CO, U.S.A.</p> <p>21 Department of Physics, Utah State University, Logan, UT, U.S.A</p> <p>22 University of Colorado at Boulder, Boulder, CO, U.S.A</p> <p> </p> <p> </p> <p>The datasets below are titled according to the figure in which they are used (e.g. "Fig3" for Figure 3, "Fig4" for Figure 4).<br>All uploaded datasets comprised of ASCII files.<br><br>Dataset descriptions:</p> <ul> <li>Figure 3 datasets (<strong>18 files in total</strong>): Each of the 18 different files corresponds to a different meteor radar station (SVA, TRO, KIR, SOD, COL, BLO, CAR, ASI, LEA, CPa, SMa, CON, TdF, KEP, KSS, ROT, DAV, MCM). Within each file, the data comprise of peak meteor altitudes (km) as a function of local time (24) and day-of-year (DOY). </li> <li>Figure 4 datasets (<strong>18 files in total</strong>): As above, but the data now represent the weighted elevation angle in degrees.</li> <li>Figure 5 datasets (<strong>24 files in total</strong>): These data can be used to plot the residual seasonal variation in peak altitude for each of the 18 locations, organized by geographic clusters. There are 24 different Figure 5 datasets, with each including the normalized residual seasonal variation in peak altitude (km) for stations within one of six different geographic clusters (Nordic high-latitude, Northern mid-latitude, Near-equatorial, Southern low/mid-latitude, Southern Andes, Mainland Antarctica) for each local time (00:00 LT, 06:00 LT, 12:00 LT, or 18:00 LT). Each file includes the data for all stations within that given cluster (i.e., "Fig5__Mainland_Antarctica__06LT__Dawkins_et_al_2024.tex" includes data for the Mainland Antarctica cluster (both DAV and MCM) for 06:00 LT), as a function of day-of-year (365) and normalized altitue (km).</li> <li>Figure 6 datasets (<strong>4 files in total</strong>): These data represent the mean absolute deviation (MAD, km) of each of the different geographic clusters as function of DOY (365) for four different local times (00:00 LT, 06:00 LT, 12:00 LT, and<br>18:00 LT).</li> <li>Figure 7 datasets (<strong>14 files in total</strong>): These files present the kinetic gravity wave energy (KGWE) as a function of day-of-year and altitude (km). 12 of the files correspond to one of the following locations: SVA, TRO, KIR, SOD, COL, BLO, CON (ALO only), TdF, KEP, KSS, ROT or DAV. There are two additional files ("Fig7__KGWE__time__Dawkins_et_al_2024.txt" and "Fig7__KGWE__altitude__Dawkins_et_al_2024.txt") which include the time (day-of-year) and altitudes (km) used.</li> <li>Figure 8 datasets (<strong>2 files in total</strong>): These two files ("Fig8__CABMOD_profiles__data__Dawkins_et_al_2024.txt" and "Fig8__CABMOD_profiles__altitude__Dawkins_et_al_2024.txt") include the data necessary to reproduce all panels in Figure 8 which shows the vertical mass profiles from CABMOD for a meteoric particle with a fixed initial mass (178 μg) and velocity (31 kms−1), at a latitude of 60 deg S. The dataset (mass, μg) corresponds to 8 different month and entry angles (in order: March, June, September, December for particle entry angles of 5 deg and 25 deg, respectively) and 201 altitudes (km).</li> <li>Figure 9 datasets (<strong>8 files in total</strong>): These data represent the simulated and observed peak altitudes (km) as a function of day-of-year and LT for each of the four Southern Andes meteor radar station locations (TdF, KEP, KSS, ROT).</li> </ul>
Meteor Camera Images - Sony IMX29
<p>Meteor Camera Images taken from a stationary Sony IMX29 camera.<br>I collected this data to make a Meteor Camera. <br><br><br>Feel free to use, but please make sure I get credited.<br>I am using these cameras both with my own trained model and also with The Global Meteor Network's Software.<br><br></p>
Gemini meteor shower, by Hao Yin, China
<p>Third place in the 2021 IAU OAE Astrophotography Contest, category Meteor showers.</p> <p>As the Earth travels around the Sun, it may cross the path of debris left behind by a comet or, more rarely, by an asteroid. These debris enter the atmosphere at high speed, producing beautiful tracks as they burn in the sky due to friction with the atmosphere. The image captures the Geminid meteor shower, named because the radiant point is located on the sky in the constellation Gemini. The particles composing the meteor shower travel at similar speed and in parallel trajectories, which causes a perspective effect like if the stream radiates from one single point in the sky, which is known as the radiant point. This image, taken in December 2020 in China, clearly shows this perspective. This is a very prolific shower, in such a way that over one hundred meteorites could be seen per hour in recent appearances. This meteor shower is one of the few associated not with a comet, but with an asteroid – 3200 Phaeton, which might be a comet that lost all its volatile material. This image shows the large number of meteors that can be observed in this shower, which always happens in December every year. The image also shows one of the most prominent constellations in the night sky, Orion, easily seen by the three stars in a diagonal making up Orion’s Belt, and the red-orange star Betelgeuse. Right above the dish is a bright point and that is the Sirius, the brightest star in the night sky and part of the constellation Canis Major. The fuzzy bluish smudge at around 2 ‘o’ clock is the Pleiades star cluster.</p> <p>Credit: Hao Yin/IAU OAE</p>
Geminid Meteor Shower from China, by Dai Jianfeng, China
<p>First place in the 2021 IAU OAE Astrophotography Contest, category Meteor showers.</p> <p>A meteor shower occurs when the Earth in its orbit around the Sun, passes through a debris trail left previously by a comet on its approach around the Sun. As the Earth enters this debris (small sand grain sized), they enter the atmosphere at high speeds and on parallel trajectories, burning completely leaving beautiful tracks (streaks) in the sky. These streaks can appear and disappear in the blink of an eye, or last much longer. On rare occasions the debris originates from asteroids, as in the case of the Geminid meteor shower, shown in this image, picturing many streaks of debris captured in the sky of China in 2017. Due to relative motions and perspective, the shower appears to come from one single point, known as the radiant point, beautifully pictured in this image. This is similar to driving in a car on a rainy day without any wind, looking out the front window it seems that the rain is coming directly towards the window, when in fact the rain is falling vertically downwards.</p> <p>Credit: Dai Jianfeng/IAU OAE</p>
Image dataset for the creation of an automatic system for meteor fall detection
<p>Image dataset with sky photos showing the occurrence or non-occurrence of falling meteors. The database comprises 7,000 images in JPEG format -- 3,850 (55%) images show the event of falling meteors, and 3,150 (45%) images show no meteors. Different instruments captured the photos from 2014 to 2023. We used the images to train a deep-learning neural network for an automatic falling meteor detector.</p> <p>The primary image data sources were the Brazilian Meteor Observation Network (BRAMON -- <a href="http://www.bramonmeteor.org">http://www.bramonmeteor.org</a>), UK Meteor Network (UKMON -- <a href="https://ukmeteornetwork.co.uk">https://ukmeteornetwork.co.uk</a>), and <em>Base des Observateurs Amateurs de Météores</em> (BOAM -- <a href="http://boam.fr">http://boam.fr</a>) repositories.</p> <p><strong>Folders Structure</strong></p> <p>We divided the folder structure into two levels. In the first level, we have two folders: RawImages, which holds images with captions stored in the repositories; and CroppedImages, which contains images without the captions (we cropped a band of 24 pixels in the lower part of the image).</p> <p>In the second level, in each of the previous folders, we have another two folders: meteor, which has images with meteors; and non-meteors, with images without occurrences of meteors.</p> <p><strong>Naming pattern for the files</strong></p> <p>The naming pattern in the meteor folder follows the format <source>_<date>_<id>.jpg where:</p> <ul> <li><source> is one of the 3 data sources: bramon, ukmon, or boam.</li> <li><date> is the date-time the instrument captured the image in the format yyyymmdd_hhnnss (y:year, m:month, d:day, h:hours, n:minutes, s:seconds).</li> <li><id> is an identifier from a specific source to avoid date-time conflicts: <ul> <li>BRAMON: radiant identifier.</li> <li>UKMON: station identifier.</li> <li>BOAM: station identifier.</li> </ul> </li> </ul> <p>For the non-meteor folder, the naming pattern is <source>_<date>_nonmeteor.jpg to avoid homonyms (with the same date-time) and to identify that they are images of non-meteors.</p>
SuperDARN meteor winds product (1993 - 2023)
<p>SuperDARN meteor wind data covering 1993 - 2023. These observations represent an average over approximately 80 - 120 km. X - radar boresight, Y - perpendicular (right, viewed from above). Supported by NSF #1934973 Collaborative Research: Super Dual Auroral Radar Network (SuperDARN) Operations, Research and Community Support. SuperDARN is an international collaboration operating ~35 high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgment of data by its users. Guidelines for data acknowledgment are as follows: When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact superdarn at jhuapl dot edu for information about this collection. For all usage of SuperDARN data, users are asked to include the following standard acknowledgment text: “The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.” While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data. The SuperDARN Executive Council must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.</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>
BRAMS Radio Spectrograms and Spectrogram Samples for Automatic Detection of Meteor Echoes
<p>The files in this dataset are based of radio recordings taped by BRAMS (Belgian RAdio Meteor Stations), the Belgian meteor detection network.</p> <p>Included in the dataset are the original BRAMS radio recordings (stored as .wav audio files), the spectrogram data for each radio recording (stored as .csv files) and the meteor and non-meteor samples extracted from the radio spectrograms (stored as .csv files).</p> <p>It should be noted that the the spectrogram data was sampled using a sliding window of size 30x20 pixels and the samples extracted in this manner were further processed by calculating the vertical average of each column in the 30x20 matrixes. The result of this sampling procedure is a set of data vectors containing the average power of the signal found in the original 30x20 spectrogram sample.</p>
SS Meteor
SS Meteor whaleback laker located in Superior WI. Photo set shot with mavic air 2 in 4k movie mode. One pass around side of ship then over the top. My 3D asset generated with photogrammetry software 3DF Zephyr v4.530 processing 309 images auto selected from 4k video. Source: Objaverse 1.0 / Sketchfab
SS Meteor Superior WI
Arialphoto set taken with Mavic Air II of SS Meteor in Superior WI. 250 photo set parsed in Zephyr 3df Source: Objaverse 1.0 / Sketchfab
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>
SEG-Y Multichannel seismic data collected during RV METEOR expedition M199 and used for publication by Micallef et al., in prep.
<p><span>The dataset comprises 3 multichannel seismic profiles, which have been collected during RV METEOR expedition M199 in February 2024 by the University of Hamburg. Data format is SEG-Y. Trace headers follow SEG-Y Revision 1 standard.</span></p> <p><span>The profiles are:</span></p> <p><span>M199_MCS_HH24-03</span></p> <p><span>M199_MCS_HH24-05</span></p> <p><span>M199_MCS_HH24-29</span></p>
Zonal and meridional wind tides measured by Kunming meteor radar
<p>Meteor wind radar is operating at Kunming (25.6°N, 103.8°E) since January 2008. This data set represents monthly mean zonal and meridional diurnal tides from 2008 to 2022.</p>
Dataset of Effect of meteoric ions on ionospheric conductance at Jupiter
<p>This dataset contains the input parameters and the simulated results used for figures in the paper "Effects of meteoric ions on ionospheric conductance of Jupiter" by Y. Nakamura, K. Terada, C. Tao, N. Terada, Y. Kasaba, F. Leblanc, H. Kita, A. Nakamizo, A. Yoshikawa, S. Ohtani, F. Tsuchiya, M. Kagitani, T. Sakanoi, G. Murakami, K. Yoshioka, T. Kimura, A. Yamazaki and I. Yoshikawa. Detailed informations about the data files can be found in "README.txt".</p>
Zonal and meridional wind measured by meteor radar at Darwin
<p>Meteor wind radar is operating at Darwin, Australia (12.3° S, 130.8° E) since January 2005. This data set represents hourly zonal and meridional wind from 2005 to 2008.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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