Editorial Type:
Article
Article Type:
Research Article

Crowdsourcing the El Reno 2013 Tornado: A New Approach for Collation and Display of Storm Chaser Imagery for Scientific Applications

Anton Seimon Department of Geography and Planning, Appalachian State University, Boone, North Carolina

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John T. Allen International Research Institute for Climate and Society, Columbia University, New York, New York

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Tracie A. Seimon Wildlife Conservation Society, New York, New York

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Skip J. Talbot Springfield, Illinois

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David K. Hoadley Falls Church, Virginia

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Print Publication:
01 Nov 2016
DOI:
https://doi.org/10.1175/BAMS-D-15-00174.1
Page(s):
2069–2084
Restricted access

Abstract

The 31 May 2013 El Reno, Oklahoma, tornado is used to demonstrate how a video imagery database crowdsourced from storm chasers can be time-corrected and georeferenced to inform severe storm research. The tornado’s exceptional magnitude (∼4.3-km diameter and ∼135 m s−1 winds) and the wealth of observational data highlight this storm as a subject for scientific investigation. The storm was documented by mobile research and fixed-base radars, lightning detection networks, and poststorm damage surveys. In addition, more than 250 individuals and groups of storm chasers navigating the tornado captured imagery, constituting a largely untapped resource for scientific investigation.

The El Reno Survey was created to crowdsource imagery from storm chasers and to compile submitted materials in a quality-controlled, open-access research database. Solicitations to storm chasers via social media and e-mail yielded 93 registrants, each contributing still and/or video imagery and metadata. Lightning flash interval is used for precise time calibration of contributed video imagery; when combined with georeferencing from open-source geographical information software, this enables detailed mapping of storm phenomena. A representative set of examples is presented to illustrate how this standardized database and a web-based visualization tool can inform research on tornadoes, lightning, and hail. The project database offers the largest archive of visual material compiled for a single storm event, accessible to the scientific community through a registration process. This approach also offers a new model for poststorm data collection, with instructional materials created to facilitate replication for research into both past and future storm events.

CORRESPONDING AUTHOR: Dr. Anton Seimon, Dept. of Geography and Planning, ASU Box 32066, Appalachian State University, Boone, NC 28608, E-mail: anton.seimon@gmail.com

Abstract

The 31 May 2013 El Reno, Oklahoma, tornado is used to demonstrate how a video imagery database crowdsourced from storm chasers can be time-corrected and georeferenced to inform severe storm research. The tornado’s exceptional magnitude (∼4.3-km diameter and ∼135 m s−1 winds) and the wealth of observational data highlight this storm as a subject for scientific investigation. The storm was documented by mobile research and fixed-base radars, lightning detection networks, and poststorm damage surveys. In addition, more than 250 individuals and groups of storm chasers navigating the tornado captured imagery, constituting a largely untapped resource for scientific investigation.

The El Reno Survey was created to crowdsource imagery from storm chasers and to compile submitted materials in a quality-controlled, open-access research database. Solicitations to storm chasers via social media and e-mail yielded 93 registrants, each contributing still and/or video imagery and metadata. Lightning flash interval is used for precise time calibration of contributed video imagery; when combined with georeferencing from open-source geographical information software, this enables detailed mapping of storm phenomena. A representative set of examples is presented to illustrate how this standardized database and a web-based visualization tool can inform research on tornadoes, lightning, and hail. The project database offers the largest archive of visual material compiled for a single storm event, accessible to the scientific community through a registration process. This approach also offers a new model for poststorm data collection, with instructional materials created to facilitate replication for research into both past and future storm events.

CORRESPONDING AUTHOR: Dr. Anton Seimon, Dept. of Geography and Planning, ASU Box 32066, Appalachian State University, Boone, NC 28608, E-mail: anton.seimon@gmail.com
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