1.4 Crowd-Sourcing the Storm: A New Approach for Obtaining and Collating Scientific Tornado Observations

Monday, 11 January 2016: 11:45 AM
Room 350/351 ( New Orleans Ernest N. Morial Convention Center)
John T. Allen, Columbia University/IRI, Palisades, NY; and A. Seimon, T. Seimon, and S. Talbot

Visual documentation of severe storms by storm chasers has remained largely unutilized by the scientific community. Here, we present a case study from the 31 May 2013 El Reno tornadic supercell demonstrating the high research value of this imagery, and the methods that can be applied to make it scientifically useful. The El Reno Storm produced the widest tornado and potentially the strongest near-surface wind speeds yet recorded. The event was documented in real-time by research and operational radars, lightning detection networks, with still and video imagery also being recorded by a multitude of storm chasers. Additionally, post-storm surveys mapped the tornado track and associated damage. To collate available data, we created the El Reno Survey (http://el-reno-survey.net/) as a first-ever effort to crowd-source imagery from storm chasers and compile submitted materials in a quality-controlled, open-access research database. Solicitations to the storm chaser community extended via social media outlets yielded 91 registered participants. Each video record was then time-corrected to within 0.03 sec. of real-time using lightning flashes, and spatially located through pattern matching to within 5m using Google Maps™. This presentation will detail the methods developed to precisely fix the time and location characteristics of uncontrolled storm chaser imagery, and will demonstrate a novel online visualization tool that displays multi-perspective video and Phased Array Radar with user-selected geographic referencing. The database is now being applied in a wide range of research applications, including: multi-perspective visual reconstruction of tornadogenesis, multiple vortex evolution and long-lived sub-vortex behavior, spatiotemporal measurements of giant hail occurrence for integration into radar studies, verification of lightning network detection efficiency and confirmation of systemic biases, and hazards presented to storm chasers and the public at large. The El Reno Survey approach employed is presented as a new paradigm for post-storm data collection and event reconstruction, with templates and instructional materials created to facilitate replication for both past and future tornadic storms of research interest.
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