The Enhanced Fujita Scale: Development and Implementation

McDonald, James R.; Mehta, Kishor C.; Smith, Douglas A.; Womble, J. Arn

doi:10.1061/41082(362)73

Cited by 19 publications

(13 citation statements)

References 1 publication

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“…However, it tends to follow EF-scale which adds additional damage indicators consisting of types of buildings (e.g., mall), structures (e.g., tower) and trees (e.g., hardwood). The additional damage indicators will lead to more consistent ratings of intensity since an expected, upper, and lower bound wind speed will be well defined for each degree of damages (McDonald et al, 2009). However, two amendments were made, the first amendment is no upper bound for the potential damages and its associated intensity and the second amendment is to add the cumulative rainfall parameter as an additional parameter for damages to tree (uprooted tree) and property inside the building.…”

Section: Resultsmentioning

confidence: 99%

Damages Scale as a Supporting Tool for Windstorm Early Warning System in Malaysia

Latib¹,

Bachok²,

Zakariah³

et al. 2021

ASMScJ

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It is a fact that windstorm is a natural disaster that can create enormous damages. Thus, the article aims to serve as a guideline in establishing the damages scale as the supporting tool in the windstorm disaster management. The purpose of the scale is to mitigate impacts of windstorm via early warning system, so that local authorities and public at the affected area are able to know the worst-case scenario if windstorm occurred and could make early preparation in minimising the impacts. The damage scale is established based on the current damage criteria as an early warning of windstorm occurrences was adopted from the damages scale which was established outside Malaysia. On the other hand, there is no damages scale originally established in the country. In order to establish this supporting tool, 51 windstorm occurrences in year 2012 to 2016 from 6 districts in Peninsular Malaysia were selected as a study case and 246 damages were assessed. Besides windstorm damages, gust speed, wind direction, rainfall and radar images as raw data, the study also acquires local conditions wind multiplier map coordinated with meteorological station and overlaid with wind direction map at the study area as well. All the damage locations for each windstorm occurrence will be coordinated in the local conditions wind multiplier map. However, the damages which located underneath the same convective cluster with meteorological station, in position with wind direction and gust speed greater than hazard threshold only will be determined its associated intensity. The scale listed each potential damage according to categories either building, property, or tree. The gust speeds at 1-minute in the scale are between 9.0 m/s to 19.3 m/s. From the study, damages scale has similar hierarchy of level damages with existing damages scale but much lower intensity.

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Section: Resultsmentioning

confidence: 99%

Damages Scale as a Supporting Tool for Windstorm Early Warning System in Malaysia

Latib¹,

Bachok²,

Zakariah³

et al. 2021

ASMScJ

View full text Add to dashboard Cite

show abstract

“…Typically, they have wind speeds of 180 km h −1 (50 m s −1 ) and a horizontal extent of 80 m but they can contain winds of up to 480 km h −1 (133 m s −1 ) and extend laterally for 3 km. They occur in the same locations as the most severe thunderstorms and derechos and are rated using the Enhanced Fujita scale that provides six categories depending on wind speed and levels of damage (McDonald et al 2021). The damage they cause can be extreme (Peterson et al 1997;Peterson 2000) but is generally along a narrow but often long path (Brooks 2004).…”

Section: Tornadoesmentioning

confidence: 99%

Wind damage to forests and trees: a review with an emphasis on planted and managed forests

Gardiner

2021

Journal of Forest Research

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This paper presents a review of our current understanding of the process of wind damage to trees and forests, with a particular, but not exclusive focus, on planted and managed forests. It makes a direct comparison with the state of knowledge just over 50 years ago when systematic research on wind damage to forests was beginning and discusses how our knowledge has changed over that period. The paper starts with a discussion of the types of severe winds that cause damage and then explores the effect of a number of factors on the risk of wind damage. These include species differences, the influence of different tree characteristics, and the effect of tree competition, tree spacing, gaps and edges in the forest, and soil and site preparation. There is then a section dealing with wind damage at a variety of spatial and temporal scales and the processes occurring at these different scales. The penultimate part of the paper describes the actual physical mechanisms of stem damage and uprooting, how this understanding can be used to develop wind damage risk models, and then how the different parts of the overall wind damage problem can be brought together to form a holistic view. Finally, there is a brief review of the advance in our understanding over the last few decades, the continued areas of uncertainty or that require further work, and recommendations of subjects and topics that could be a focus for future research.

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“…Though reports are commonly used as ground-truth data for SCS climatological studies, it is important to explicitly mention their caveats herein. For tornadoes, reports do not consistently capture hazard magnitude, as they rely on a damage-based scale (Fujita 1971;Doswell III et al 2009;McDonald et al 2010;Edwards et al 2013;Wurman et al 2021). Tornadoes of the same pathlength, width, and intensity can produce vastly different societal impacts depending on their geographic location and underlying affected assets (Ashley et al 2014;Ashley and Strader 2016;Strader and Ashley 2018;Strader et al 2018).…”

Section: A Scs Reportsmentioning

confidence: 99%

Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

Gensini

Converse

Ashley

et al. 2021

Weather and Forecasting

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Previous studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

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The Enhanced Fujita Scale: Development and Implementation

Cited by 19 publications

References 1 publication

Damages Scale as a Supporting Tool for Windstorm Early Warning System in Malaysia

Damages Scale as a Supporting Tool for Windstorm Early Warning System in Malaysia

Wind damage to forests and trees: a review with an emphasis on planted and managed forests

Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

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