Embry-Riddle Aeronautical University Showcase

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.

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.