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Tropical Storm David produced numerous tornadoes during its transit up the East Coast in September 1979. One of these occurred in Fairfax County, Virginia, during early evening daylight and in a residential community. A study of this tornado was conducted through ground inspection, personal interviews, examination of low-level aerial photography, and meteorological records. Several eyewitness accounts of this storm are of special interest, considering the general paucity of data on hurricane and/or tropical storm tornadoes. Among such observations were: an isolated break in the overcast immediately south of the tornado, with an echo-free “hole” seen by radar 10 min prior to its formation; an outflow gust front wrapping around the tornadic circulation; and widely varying sound reports. The isolated visual and radar break in the clouds may be a significant forecast indicator of these small, short-lived, but destructive storms.
Tropical Storm David produced numerous tornadoes during its transit up the East Coast in September 1979. One of these occurred in Fairfax County, Virginia, during early evening daylight and in a residential community. A study of this tornado was conducted through ground inspection, personal interviews, examination of low-level aerial photography, and meteorological records. Several eyewitness accounts of this storm are of special interest, considering the general paucity of data on hurricane and/or tropical storm tornadoes. Among such observations were: an isolated break in the overcast immediately south of the tornado, with an echo-free “hole” seen by radar 10 min prior to its formation; an outflow gust front wrapping around the tornadic circulation; and widely varying sound reports. The isolated visual and radar break in the clouds may be a significant forecast indicator of these small, short-lived, but destructive storms.
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.
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.
