• Title/Summary/Keyword: Enhanced Fujita Scale

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Enhanced remote-sensing scale for wind damage assessment

  • Luo, Jianjun;Liang, Daan;Kafali, Cagdas;Li, Ruilong;Brown, Tanya M.
    • Wind and Structures
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    • v.19 no.3
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    • pp.321-337
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    • 2014
  • This study has developed an Enhanced Remote-Sensing (ERS) scale to improve the accuracy and efficiency of using remote-sensing images of residential building to predict their damage conditions. The new scale, by incorporating multiple damage states observable on remote-sensing imagery, substantially reduces measurement errors and increases the amount of information retained. A ground damage survey was conducted six days after the Joplin EF 5 tornado in 2011. A total of 1,400 one- and two-family residences (FR12) were selected and their damage states were evaluated based on Degree of Damage (DOD) in the Enhanced Fujita (EF) scale. A subsequent remote-sensing survey was performed to rate damages with the ERS scale using high-resolution aerial imagery. Results from Ordinary Least Square regression indicate that ERS-derived damage states could reliably predict the ground level damage with 94% of variance in DOD explained by ERS. The superior performance is mainly because ERS extracts more information. The regression model developed can be used for future rapid assessment of tornado damages. In addition, this study provides strong empirical evidence for the effectiveness of the ERS scale and remote-sensing technology for assessment of damages from tornadoes and other wind events.

Predicting ground-based damage states from windstorms using remote-sensing imagery

  • Brown, Tanya M.;Liang, Daan;Womble, J. Arn
    • Wind and Structures
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    • v.15 no.5
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    • pp.369-383
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    • 2012
  • Researchers have recently begun using high spatial resolution remote-sensing data, which are automatically captured and georeferenced, to assess damage following natural and man-made disasters, in addition to, or instead of employing the older methods of walking house-to-house for surveys, or photographing individual buildings from an airplane. This research establishes quantitative relationships between the damage states observed at ground-level, and those observed from space using high spatial resolution remote-sensing data, for windstorms, for individual site-built one- or two-family residences (FR12). "Degrees of Damage" (DOD) from the Enhanced Fujita (EF) Scale were determined for ground-based damage states; damage states were also assigned for remote-sensing imagery, using a modified version of Womble's Remote-Sensing (RS) Damage Scale. The preliminary developed model can be used to predict the ground-level damage state using remote-sensing imagery, which could significantly lessen the time and expense required to assess the damage following a windstorm.

Reconstruction of a near-surface tornado wind field from observed building damage

  • Luo, Jianjun;Liang, Daan;Weiss, Christopher
    • Wind and Structures
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    • v.20 no.3
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    • pp.389-404
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    • 2015
  • In this study, residential building damage states observed from a post-tornado damage survey in Joplin after a 2011 EF 5 tornado were used to reconstruct the near-surface wind field. It was based on well-studied relationships between Degrees of Damage (DOD) of building and wind speeds in the Enhanced Fujita (EF) scale. A total of 4,166 one- or two-family residences (FR12) located in the study area were selected and their DODs were recorded. Then, the wind speeds were estimated with the EF scale. The peak wind speed profile estimated from damage of buildings was used to fit a translating analytical vortex model. Agreement between simulated peak wind speeds and observed damages confirms the feasibility of using post-tornado damage surveys for reconstructing the near-surface wind field. In addition to peak wind speeds, the model can create the time history of wind speed and direction at any given point, offering opportunity to better understand tornado parameters and wind field structures. Future work could extend the method to tornadoes of different characteristics and therefore improve model's generalizability.