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61 results for “radar observation”
Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018
<p>This dataset includes meteor observations and wind estimates taken as part of the SIMONe 2018 campaign in northern Germany on November 5, 2018. The files are in netCDF-4 format and follow CF conventions (https://cfconventions.org/). We recommend loading the data using the xarray Python package.</p> <p>The SIMONe 2018 campaign ran from November 2, 2018 through November 9, 2018 in northern Germany. The radar network consisted of two pulsed transmitters in Juliusruh and Collm and a five-element interferometric MIMO-CW transmitter located in Kühlungsborn. Monostatic receiver stations co-located with the pulsed transmitters and six additional receiver stations located in Mechelsdorf, Breege, Neustrelitz, Guderup, Salzwedel, and Bornim were used to form a total of two monostatic and ten bistatic links. The data from these individual links were then processed to detect specular meteor echoes and estimate their parameters, including Doppler shift. The Doppler shifts, imposed by movement of the meteor trail due to the neutral winds, were then used to estimate the 4-D wind field. More details about the campaign can be found in Vierinen et al. (2019). Details for the wind field estimation can be found in Volz et al. (submitted).</p>
Poker Flat Incoherent Scatter Radar (PFISR) Observations of E-region Neutral Winds
<p>Updated: 12-15-2021</p> <p><strong>RULES OF THE ROAD:</strong></p> <p>You are welcome to use the data 'as is', however, please inform me via email if you plan to use the dataset. There are a number of small issues with the dataset that are best discussed. We are interested in publications that use the data and derived values that are presented within the dataset. <strong>If you plan to publish these results, please circulate a draft by me (SRK) and we would appreciate an offer of co-authorship or at minimum an acknowledgement. You should include the NSF funding numbers NSF AGS - 1853408</strong></p> <p> </p> <p>As a general warning, the data from PFISR are quite noisy and you may need to perform significant averaging to produce usable results. Again, please contact me and we can discuss this in more detail.</p> <p>Version v0.6.4.2021.07.12 - This was the final processed version at the time that the final report was submitted to the NSF.</p> <p> </p> <p><strong>--------------- Previous from before ------------------</strong></p> <p>This file contains Poker Flat Incoherent Scatter Radar (PFISR) E-region Neutral Winds Data. These data correspond to monthly data files that include the E-region neutral winds and other parameters for the from March 2013-June 2019.</p> <p><strong>Publications of the Joule Heating Results:</strong></p> <p>https://doi.org/10.1029/2021JA029371</p> <p>https://doi.org/10.1029/2021JA029719</p> <p> </p> <p><strong>Publication of Neutral Wind Results:</strong></p> <p>Hopefully we will have something in 2021. </p> <p> </p> <p><strong>RAW ISR Data:</strong> These data were processed from the following files found in: https://data.amisr.com/database/tmp/Kaeppler/winds/ and https://data.amisr.com/database/tmp/Kaeppler/missing_IPY.tar.gz Please note that the error on the line of sight velocities may have been overestimated in these data and we scaled them by a eVLOS/sqrt(10). Interested persons should contact Ashton Reimer or Roger Varney at SRI International for more information about these data, please see amisr.com</p> <p>Truthfully, the ISR data should eventually be reprocessed and then the winds algorithm run over it again. This is a step for future work.</p> <p> </p> <p><strong>Processing Code is available upon request via email.</strong></p> <p> </p> <p><strong>File Documentation:</strong></p> <p> </p> <p><strong>Please see the change log:</strong></p> <p>Purpose: This is the overarching program and functions which process the<br> E region neutral winds from the fitted AC and LP data from PFISR.<br> This is a conversion fo process_eregwinds_srk.py which was originally written by<br> Nicolls into a more formal python class structure.</p> <p>2017-10-05 - v0.2</p> <p>The ProcessEregionNeutralWinds.py file has been validated against process_eregwinds_srk.py<br> using 20161121.001_ac_3min-fitcal.h5, 20170301.013_ac_3min-fitcal.h5, 20170302.001_ac_3min-fitcal.h5.<br> The program to run these is ComparePrograms.py. At this point these program match.<br> I am going to start diverging the code base, first subtly in the Joule Heating<br> since I found that Mike just looped over Nbeams, which isn't quite right, you need to loop<br> over the beams that were selected.</p> <p>Changes from this point forward will produce different results.</p> <p>2017-10-10 - v0.3.2017.10.10</p> <p>Version v0.3, I made some IO changes but I may start processing some data with this version.</p> <p>Version v0.4 - lots of small edits made to the IO and the plotting software. It all seems to work<br> I have also included the SNR and Ne into the monthly plots and other information.<br> Made processing smoother.</p> <p>03 13 2018 - added solar local time converion</p> <p>v0.4.1 - 09 08 2018 added some ability to extract out the raw electron and SNR densities for each altitude bin<br> v0.4.2 - 10 15 2018 added in obtaining the F-region flows - want to check against the electric field.<br> v0.4.3 - 10 29 2018 added in some more altitude into the Joule Heating so I can make better figures<br> v0.4.4 - 11 20 2018 made some pretty major changes to IO to include consistent calculation of<br> Pedersen conductivity from FastConductivity.py. Made some changes to the Joule heating calculation and checked<br> formulas. It is worth checking again.</p> <p>v0.4.5 - 11 20 2018: added in Hall and Pedersen conductivities from fitted electron density data.<br> v0.4.6 - 12 03 2018: Tried to fix some of the double counting and time problems in testMakeMonthlyh5</p> <p>05 22 2019: added some statements to bypass the geophysical parameters. Also need in config file now.<br> Additionally wrote in IOEregionwinds a try except statement</p> <p>07 29 2019: Running the code for the 06 data reprocessed by Ashton</p> <p>v0.5.0 - 10-15-2019: put in some filtering on the LOS velocity discharging bad Chi square and bad error codes on the fit.</p> <p>v0.5.1 - 10-23-2019: changed chi square to 0.01 for lower boundary</p> <p>v0.5.3 - 12-02-2019: Added in that now passing in the Chi2 and Fitcode filtering by Config file<br> Bigger change that I am scaling the AC dVlos by some sort of factor while Ashton figures this out.<br> We decided that a conversative scaling would be to reduce the dVLOS by 1/sqrt(10).<br> The chi square produced in the data Ashton sent me typically was around 0.01, so the uncertaintiies on the LOS velocities<br> may be over estimated. So we are just changing this as a temporary fix while Ashton fixes the uncertainty estimation.</p> <p>v0.5.5 02 01 2020 - Added in calculation of Coriolis, Centrifugal, and Lorentz forcing<br> v0.5.5 02 10 2020 - Added a correction to qvert so that way I can calculate the lorentz term.<br> Found an error where qvert = 0 in the if statement goes to false.</p> <p>v0.5.5 02 15 2020 - Put in nuInscaler into the main program, scaling ALL kappas by the scaler number</p> <p>v0.5.6 02 28 2020 -- Added some more vlos diagostics and the calculation of the scale height. Added Altitude offset</p> <p>v0.5.6.2020.03.12_nuin_fracoff - testing putting in the Brekke formula for ion neutral collision frequency and took out frac</p> <p>v0.5.7.2020.04.10 - Put in Ashton's revised ion neutral collision frequency formulas into IO.<br> Also wrote a testscript and at least for the file I used was only different by 2.5%.<br> Revised where the mag data is being pulled from since the URL is deprecated<br> Added in Kappa which is now being interpolate - plan to see where kappa =1 is located for the paper.<br> commented out nuin scaler just so I am not chasing my tail</p> <p> test v0.5.7.2020.04.13_org commented back in original ion neutral collision frequency method<br> possible mistake that not summing up properly.</p> <p> test v0.5.7.2020.04.13_newnuin_orgsum_noTr800 - new formula for nuin except took off Tr>800.<br> I expect this should be almost the same as before since the formulas are basically the same.<br> did the original sum using frac[0] and frac[1] want to see if I am underestimating</p> <p> v0.5.7.2020.04.13_newnuin_orgsum_yesTr800 - same as above except now including Tr>800.</p> <p> 'v0.5.7.2020.04.13_newnuin_newsum_noTr800' - using the new sum now and new col freq</p> <p> v0.5.7.2020.04.13_updatedorg - updated original uses original method but including the NO term</p> <p>v0.6.0.2020.04.15 -- Now think I have the new ion neutral collision frequency working and validated.<br> Found a mistake in how I was calculating the ion neutral collision frequency that<br> the fraction weight I was using only included the O+ and O2+ terms and not NO+<br> Turns out I was basically weighting by about 0.5, so I was effectively reducing the<br> ion neutral collision frequency by about a factor of 0.5 or less...<br> From this point forward need to start using any results from > v0.6<br> This revision has changed previous results signi</p> <p>v0.6.0.2020.04.21 -- updated to now include the temperature correction for the O2+</p> <p>v0.6.1.2020.04.23 -- made a number of changes to the geomagnetic files and reprocessed from CDAweb.<br> Wrote new code to be able to process the files from CDAweb in the new format.<br> Also changed the geomagnetic data files</p> <p>v0.6.1.2020.06.07 -- changed the generation of Monthly files to hopefully be in order now<br> Added in missingIPY files given to me by ashton, maybe improve data covarege<br> Some work going to need to be done to make sure that all of the 10, 15, and 20 minute data are there.</p> <p>v0.6.1.2020.06.15_Weijia -- Updated the data for Weijia's study in particular since we are missing a lot of IPY data for 02-04 2013 and 2014.</p> <p>'v0.6.2.2020.07.01' -- Updated the data with new IPY27 mode for 2013 and 2014 Ashton processed. Also now put in mechanical Joule heating term.<br> Put in the conductance and conductivity now too.</p> <p>v0.6.2.2020.07.30 -- Made some changes to IO since Weijia noticed the mechanical heating terms were missing from the monthly files.</p> <p>v0.6.3.2020.10.19 -- Tried to elimated all extra instance of nuinscaler, and also output that variable. Added in variables<br> To get the Ti, Tn, ion neutral collision frequency along the vertical beam for diagnostic purposes<br> included dVest for F-region plasma drifts for Rafael</p> <p>v0.6.4.2020.11.20 -- Extracted some more parameters including F107 and the Hall and Pedersen Drags</p> <p>v0.6.4.2021.07.21 -- Final Run of data for NSF project</p> <p> </p>
Airborne radar observation dataset of sea surface height on 8 December 2016
<p>This dataset contains the results of time-series sea surface height (SSH) observation data of flight No.1, 2, 3, 4, 7, and 8 on 8 December 2016 by airborne altimeter measurement using a frequency modulated continuous wave (FM-CW) radar. The observation flight were carried out south of Japan passed over the Kuroshio Current. The data files are written in CSV format, the columns are UTC date, time, latitude, longitude, flight altitude, observed SSH, 1 min moving averaged SSH values, geoid height, and tide height. The geoid height and the tide height are derived by the EGM 2008 model (Pavlis et al. 2012) and the Nao.99Jb model (Matsumoto et al. 2000), respectively. The original data sampling rate of 800 microseconds is resampled by 80 milliseconds in each file. The data comes from a paper under review for Geophysical Research Letter.</p>
DATASET: A low elevation imaging radar using a non-uniform coplanar receiver array for E~region observations
<p>Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar 3-Dimensional (ICEBEAR-3D) dataset for validation of the receiver antenna array reconfiguration, Suppressed-Spherical Wave Harmonic Transform (Suppressed-SWHT), and proper geometry for vertical interferometry using the geocentral angle.</p>
Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars
<p>The datasets here correspond to a paper by Dawkins et al., “Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars”, originally submitted in November 2022.</p> <p>The following datasets are sufficient to produce Figure 2 and 3 in the main manuscript.</p> <p>Figure 2:</p> <ul> <li>Please use the 12 individual files with filenames, “Dawkins_et_al_2022__meteor_peak_altitude__*_data.txt”. Here the asterisk should be replaced by one of the following station abbreviations: CAR, COL, CPa, DAV, KIR, KSS, ROT, SMa, SOD, SVA, TdF, and TRO.</li> <li>Each file contains 5 columns: Column 1 is year (from 1999 to 2022), Column 2 is the time series of the annual peak altitude residuals (no units), Column 3 is the corresponding standard error, Column 4 is the multilinear model fit, and Column 5 is the normalized annual solar flux (F10.7) in arbitrary units.</li> </ul> <p>Figure 3:</p> <ul> <li>Please use “Dawkins_et_al_2022__meteor_peak_altitude_trends.txt”. For ease, a description of the different columns is included within this file.</li> </ul> <p> </p>
JRC Phased Array Weather Radar Observational Data
<p>These datasets were observed by Phased Array Weather Radar from 0755 to 0810 JST on October 12, 2019. Data type is NetCDF. Data variables are radar reflectivity and Doppler velocity.</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>
Dataset for A Statistical Survey of E-region Anomalous Electron Heating Using Poker Flat Incoherent Scatter Radar Observations
<p>This archive contains the complete list of anomalous electron heating (AEH) events in PFISR data between 2010 and 2023 identified by Zhang and Varney (2024), along with the code necessary to reproduce the results. The main list of AEH events is in the file AEH_event_list.csv, and the rest of this archive is supporting information for reproducibility.</p> <p>The files contained are:</p> <p>algo1.ipynb: Python notebook implementing algorithm 1.</p> <p>algo2.py: Python script implementing algorithm 2.</p> <p>algo3.ipynb: Python notebook implementing algorithm 3.</p> <p>algo4.ipynb: Python notebook implementing algorithm 4.</p> <p>cal_velo.py: Python function to calculate ion velocity.</p> <p>io_utils.py: Python functions for manipulating AMISR hdf5 files.</p> <p>Fig1.ipynb: Python notebook to recreate figure 1.</p> <p>Fig2,5.ipynb: Python notebook to recreate figures 2 and 5.</p> <p>Fig3,11.ipynb: Python notebook to recreate figures 3 and 11.</p> <p>Fig4.ipynb: Python notebook to recreate figure 4.</p> <p>Fig6.ipynb: Python notebook to recreate figure 6.</p> <p>Fig7,8,9,10.ipynb: Python notebook to recreate figures 7, 8, 9, and 10.</p> <p>PFISR_Data_Quality_Checker.ipynb: Python notebook with data preprocessing and quality checking.</p> <p>Table1.ipynb: Python notebook to extract the beamcode information needed for table 1.</p> <p>AEH_events_list.csv: Complete list of AEH events identified by algorithms 1, 3, and 4. The first column indicates the UT time of the start of the event, and 1 or 0 in the three columns denote whether the event was or was not detected by the algorithm, respectively.</p> <p>AEH_in_2010&2011.csv: Spreadsheet to facilitate direct comparisons with previous work on AEH in 2010 and 2011.</p> <p>f107.json: Smoothed F10.7 data used in this study.</p> <p>AEH_Detection_Outputs.zip: Archive of all of the raw output of the python scripts running the detection algorithms.</p> <p>AE&PAE.zip: Archive of all AE data used in this study.</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>
Lightning Observations with the1 Horus Polarimetric Phased Array Radar Supplemental Material
Open the record for dataset details and reuse information.
The TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation dataset
<p>The combined TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation (TRIPEx) was a joint field experiment of the University of Cologne, the University of Bonn, the Karlsruhe Institute of Technology (KIT), and the Research Center Jülich. TRIPEx took place at the Jülich Observatory for Cloud Evolution (JOYCE) from 11 November 2015 until 04 January 2016. During this experiment, the X-, Ka- and W-band ground-based Doppler radars were used vertically pointing to observe the clouds. Here we provide the level 2 of the dataset collected during this campaign. Several step processing were applied to minimize the radar offset and attenuation. In addition, we provide the attenuation correction and the quality flags for each processing steps to allow the user to retrieve the data without our correction. The raw and the level 1 of the dataset are available for the users on request from the corresponding author.<br> </p>
The TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation (version 2)
<p>The combined TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation (TRIPEx) was a joint field experiment of the University of Cologne, the University of Bonn, the Karlsruhe Institute of Technology (KIT), and the Research Center Jülich. TRIPEx took place at the Jülich Observatory for Cloud Evolution (JOYCE) from 11 November 2015 until 04 January 2016. During this experiment, the X, Ka and W Band ground-based Doppler radars were used vertically pointing to observe the clouds. Here we provide level 2 of the dataset collected during this campaign. Several step processing were applied to minimize the radar offset and attenuation. In addition, we provide the attenuation correction and the quality flags for each processing steps to allow the user to retrieve the data without our correction. The raw and the level 1 of the dataset are available for the users on request from the corresponding author.</p>
Dataset: Modeling the Dielectric Properties of Minerals from Crystals to Bulk Powders for Improved Interpretation of Asteroid Radar Observations
<p>Data (measurements of scattering parameters of samples) presented in: Hickson ,D.C., Boivin, A.L., Tsai, C.A., Daly, M.G. and Ghent, R.R. (2020) Modeling the Dielectric Properties of Minerals from Crystals to Bulk Powders for Improved Interpretation of Asteroid Radar Observations. <em>Journal of Geophysical Research: Planets, 125, </em>e2019JE006141. https://doi.org/10.1029/2019JE006141</p>
Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 2. Radar product RADFLOOD21
<p>From July 13th to 16th 2021, a long period of sustained and heavy rainfall affected Central Europe producing extreme rainfall amounts in western Germany, eastern Belgium, Luxembourg and The Netherlands. In Belgium, this unusual event induced massive flooding on a large part of the country and was responsible for 39 fatalities and strong damages to buildings and infrastructures.</p><p>Such extremely rare event needs to be documented as much as possible and data must be made available for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding all factors leading to such disaster.</p><p>The observational rainfall data available for Belgium during the period from July 13th to July 16th 2021 are here shared with the scientific community. These data are twofold and provided in 2 parts:</p><p><br><strong>Part 1. </strong><a href="https://doi.org/10.5281/zenodo.7739983"><strong>Observations from high-quality rain gauges</strong></a></p><p>The dataset includes daily precipitation accumulation recorded by 323 weighing and manual rain gauges in Belgium as well as 5-min precipitation data recorded by 168 weighing rain gauges. These data were checked for possible errors and inconsistencies.</p><p>The rain gauges observations are provided in csv format in 2 files:</p><ul><li>RainGaugesData_FLOOD21_daily.csv</li><li>RainGaugesData_FLOOD21_5min.csv</li></ul><p><br><strong>Part 2. </strong><a href="https://doi.org/10.5281/zenodo.7740059"><strong>Radar-based quantitative precipitation estimation (RADFLOOD21)</strong></a></p><p>This product provides a quantitative precipitation estimation of the event at high spatial (i.e., 1 km) and temporal (i.e., 5 min and hourly) resolutions. It is obtained after a careful processing of the weather radar measurements and a merging with rain gauge measurements. The data is provided in hdf5 format. In addition, an animation of the 5-min RADFLOOD21 data is also made available.</p><p> </p><p>These data are exposed and discussed in <a href="https://hess.copernicus.org/articles/27/3169/2023/">https://hess.copernicus.org/articles/27/3169/2023/</a>. In particular, several analyses of these data are performed to describe the spatial and temporal distribution of rainfall during the event and to illustrate its exceptional character.</p><p> </p>
Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP
<p>Contains</p> <ul> <li>raw_data <ul> <li>nature run initial conditions for the cases "random" and "warm-bubble"</li> <li>ensemble initial condition sounding profiles</li> </ul> </li> <li>evaluation_metrics <ul> <li>csv files of FSS, RMSE, MAE, ensemble spread, mean difference<br> to reproduce figures of FSS, RMSE and spread in the paper and more.<br> See the README.txt</li> </ul> </li> <li>figures <ul> <li>Map_VIS06 ... Simulated satellite images of visible reflectance for ensemble members and truth</li> <li>Map_VIS06_probability ... Map of ensemble probability for visible reflectance > 0.6 and the nature in red contours</li> </ul> </li> </ul> <p> </p>
Data sets for "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al.
<p>AMISR-14 data set used in the study entitled "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al. and published by Earth, Planets and Space, doi: 10.1186/s40623-023-01876-7.</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>
Flash propagation and inferred charge structure relative to radar-observed ice alignment signatures in a small Florida Mesoscale Convective System
<p>Data for paper of above title, submitted to <em>Geophysical Research Letters</em>, June 2017. Manuscript number: 2017GL072767</p>
Simulation details for: Radar signatures and surface observations of elevated convection associated with damaging surface winds
<p>Identifying radar signatures indicative of damaging surface winds produced by convection remains a challenge for operational meteorologists, especially within environments characterized by strong low-level static stability and convection for which inflow is presumably entirely above the planetary boundary layer. Numerical model simulations suggest the most prevalent method through which elevated convection generates damaging surface winds is via "up-down" trajectories, where a near-surface stable layer is dynamically lifted and then dropped with little to no connection to momentum associated with the elevated convection itself. Recently, a number of unique convective episodes during which damaging surface winds were produced by apparently elevated convection coincident with mesoscale gravity waves were identified and cataloged for study. A novel radar signature indicative of damaging surface winds produced by elevated convection is introduced through six representative cases. One case is then explored further via a high-resolution model simulation and related to the conceptual model of "up-down" trajectories. Understanding the processes responsible for, and radar signature indicative of, damaging surface winds produced by gravity-wave coincident convection will help operational forecasters identify and ultimately warn for a previously underappreciated phenomenon that poses a threat to lives and property.</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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.