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Embry-Riddle Aeronautical University

Embry-Riddle Aeronautical University Showcase

ERAU Data Commons is an institutional data repository and data management tool, designed to give ERAU researchers a place to collect, manage, curate, share, preserve, and publish their research-supporting data and the research output of the university. This repository is a companion to Scholarly Commons, where legacy data sets and a broad range of ERAU-created materials can be found.

Data Commons is managed by the Scholarly Communication Department of Hunt and Hazy Libraries. To learn more, contact commons@erau.edu.

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1970
2024
1970 2024
4 results
  • Supplemental Materials to the Manuscript "Atmospheric and Ionospheric Responses to Orographic Gravity Waves prior to the December 2022 Cold Air Outbreak"
    This collection hosts the animations associated with the journal article, Atmospheric and Ionospheric Responses to Orographic Gravity Waves prior to the December 2022 Cold Air Outbreak. The full-text manuscript is published in Journal of Geophysical Research: Space Physics. Authors: P.A. Inchin Computational Physics, Inc., Springfield, Virginia, USA Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA A. Bhatt SRI International, Menlo Park, CA, USA M. Bramberger NorthWest Research Associates, Boulder Office, Boulder, CO, USA S. Chakraborty Center for Space Science and Engineering Research, Virginia Tech, Blacksburg, VA, USA S. Debchoudhury Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA C. Heale Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA The collection includes next datasets: Dataset 1 Title: Green and red line airglow difference images Description: The animation of difference images of green line (557.7 nm) and red line (630.0 nm). Dataset 2 Title: Maps of filtered GNSS vTEC Description: The animations of reconstructed maps of filtered vertical total electron content (vTEC) observations. Dataset 3 Title: Climatological context Description: The animations of absolute, mean and anomaly (absolute-mean) values of geopotential heights and vertical velocities based on ERA5 data products.
    • Dataset
  • Supplemental Materials to the Manuscript "Earthquake Source Impacts on the Generation and Propagation of Seismic Infrasound to the Upper Atmosphere"
    This collection hosts the data and animations associated with the journal article, Earthquake Source Impacts on the Generation and Propagation of Seismic Infrasound to the Upper Atmosphere. The full-text manuscript is published in Geophysical Journal International. Authors: Y. Nozuka, Department of Geophysics, Graduate School of Science, Kyoto University P.A. Inchin, Computational Physics, Inc., Springfield, Virginia 22151, USA Department of Physical Sciences and Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA Y. Kaneko, Department of Geophysics, Graduate School of Science, Kyoto University R. Sabatini, Department of Physical Sciences and Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA Ecole Centrale de Lyon, CNRS, Universite Claude Bernard Lyon 1, INSA Lyon, LMFA, UMR5509, 69130, Ecully, France J.B. Snively Department of Physical Sciences and Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA The collection includes next datasets: Dataset 1 Title: MAGIC3D Simulation Results. Vertical Fluid Velocities Description: The 3D fields of vertical fluid velocities from MAGIC3D simulations and Matlab script for reading and plotting. Keywords: Dataset 2 Title: SPECFEM3D Simulation Results. Fault Plane Fields Description: The 2D fields of fault plane fields from SPECFEM3D simulations and Matlab scripts for reading and plotting. Dataset 3 Title: SPECFEM3D Simulation Results. Surface Vertical Velocities Description: The 2D fields of vertical velocities at the surface from 7 SPECFEM3D simulations and Matlab scripts for reading and plotting. Dataset 4 Title: MAGIC3D Simulation Results. Animations Description: The animations of simulated MAGIC3D simulation vertical fluid velocities for Simulation #1-7. The movies supplement Figures 2-4 of the manuscript.
    • Dataset
  • System for Rapid Analysis of Ionospheric Dynamics based on GNSS TEC Signals
    System for Rapid Analysis of Ionospheric Dynamics based on GNSS TEC Signals (S-RAID) performs downloading, parsing, and processing of Global Navigation Satellite System (GNSS) signal measurements and their geometry of observations, calculation of slant and vertical total electron contents (sTEC and vTEC) and subsequent visualization for selected band-passes of fluctuations with periods shorter than two hours. In a routine operation, the System processes 15/30 sec GNSS signal measurements over Continental United States (CONUS) from ~2700 stations. Raw GNSS signal measurements are collected from public archives, including The Crustal Dynamics Data Information System (CDDIS), EarthScope/UNAVCO, National Oceanic and Atmospheric Administration (NOAA), and Scripps Institution of Oceanography's Orbit and Permanent Array Center (SOPAC). The System is oriented towards rapid access to GNSS sTEC/vTEC data for the investigation of traveling ionospheric disturbances (TIDs) of various nature, from large scale TIDs to irregularities of ~10s of km and minutes of periods, in particular those driven by atmospheric acoustic and gravity wave dynamics. The details of data processing methodology are provided the section Steps to Reproduce below, and in (2) and Supporting Information to it. Currently, this archive provides an access to high-resolution visualization of processed vTEC mapped over CONUS for years 2017-2023. The structure of the archive: /YYYY/MM/DD/animation.mp4, where YYYY - year, MM - month, DD - day of month, animation.mp4 - temporally evolving visualization of processed vTEC (see Steps to Reproduce for the details of data processing methodology). To reference the archive or the methodology for data processing, we suggest to: (1) Cite this archive by its DOI: 10.17632/jbx98yscmd.1, and/or (2) Cite Inchin, P. A., et al. (2023). Multi-layer evolution of acoustic-gravity waves and ionospheric disturbances over the United States after the 2022 Hunga Tonga volcano eruption. AGU Advances, 4. https://doi.org/10.1029/2023AV000870. Being processed in an automated regime, the visualizations may contain errors and bugs and thus should be used with caution as is. If you encounter any issues or wish to obtain data for animation replication, contact Inchin P.A. inchinp@erau.edu, inchinpa@gmail.com, J.B. Snively snivelyj@erau.edu or M.D. Zettergren zettergm@erau.edu. Research is supported by DARPA Cooperative Agreement HR00112120003. This work is approved for public release; distribution is unlimited. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.
    • Dataset
  • Airport Data for Artificial Intelligence Forecasting of Air Passenger Throughput
    The dataset comprises 974 daily observations for each of the five selected major airports, namely Hartsfield-Jackson Atlanta International Airport (ATL), Denver International Airport (DEN), O'Hare International Airport (ORD), Los Angeles International Airport (LAX), and Dallas/Fort Worth International Airport (DFW), encompassing the period from February 15, 2020, to October 15, 2022. Data observations include daily airport passenger flow from aggregated airport TSA security checkpoint throughput (pax). Additionally, anonymized Google measured visitor numbers to retail & recreation, grocery & pharmacy stores, parks, transit stations, workplaces, and duration of stay in residential locations (retail, groc, park, transit, work, resident) for the immediate surrounding county where each of the sample airports are located (Fulton, GA for ATL; Denver, CO for DEN; Tarrant, TX for DFW; Los Angeles, CA for LAX; and Cook, IL for ORD) are included, which has been normalized by comparing relative change to baseline days before the pandemic outbreak. Google Trends data denoting the airport's metropolitan statistical area's search intensity for the keywords "COVID", "flight" and "airport" (srch_cov, srch_flght, srch_airprt) are included. Lastly, county data for each respective airport for daily COVID-19 related deaths and COVID-19 confirmed cases obtained from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (cov_death, cov_case) are included.
    • Dataset