A Bayesian modelling framework for tornado occurrences in North America

Cheng, Vincent Y. S.; Arhonditsis, George B.; Sills, David; Gough, William A.; Auld, Heather

doi:10.1038/ncomms7599

Cited by 16 publications

(7 citation statements)

References 40 publications

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“…The propagation of the polar jet stream during the winter lends itself to increased DLBS values [48,49]. DLBS values decrease during the summer months when the polar jet stream retreats northward [7]. The seasonal variability of DLBS must be taken into account when fitting a model to estimate DLBS using climate variables.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

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Regional SST and SLP conditions related to tornado 'outbreak' environments 15 days later

Schroder¹

2021

Preprint

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Global climate features are known to influence tornado frequency in the U.S., but more work needs to focus on understanding the extent to which climate variables contribute to increases in CAPE and shear on days with an outbreak of at least ten tornadoes. Here the authors quantify the conditional relationships between precursor SST and SLP variables and localized extremes of CAPE and shear associated with large outbreaks. They do this by fitting linear regressions to global climate variables averaged over the fifteen days before the outbreak to estimate the changes in CAPE and shear on days with at least ten tornadoes. Results show that for every 1° increase in the SST gradient between the Gulf of Alaska and the Caribbean, DLBS increases by 0.88 m s¯¹, SLBS increases by 0.62 m s¯¹, and CAPE decreases by 50.6 J kg¯¹, conditional on at least ten tornadoes, and holding the other variables constant. Further, results show that for every 1° E increase in longitude, DLBS increases by 0.15 m s¯¹, SLBS increases by 0.38 m s¯¹, and CAPE decreases by 39.3 J kg¯¹, conditional on at least ten tornadoes, and holding the other variables constant. Additionally, SLBS is the only environmental factor that has a significant upward annual trend.

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Section: Descriptive Statisticsmentioning

confidence: 99%

“…4). During the summer months, CAPE values increase as a result of increased air temperatures [7]. These larger CAPE values indicate more buoyant air leading to a greater potential for convection to occur.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Regional SST and SLP conditions related to tornado 'outbreak' environments 15 days later

Schroder¹

2021

Preprint

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“…Undercounting of tornadoes significantly affects the accuracy of regional tornado climatologies (Doswell and Burgess, 1988) and leads to erroneous assessment of risks to life and property (Brooks, 2013; Lloyd's of London, 2013). Low population density has long been credited as a primary factor in the failure to observe tornadoes (Snider, 1977;Schaefer and Galway, 1982;Anderson et al, 2007;Cheng et al, 2013Cheng et al, , 2015. Studies of tornado climates of sparsely populated areas of Ontario (Sills, 2012;Sills et al, 2012;Cheng et al, 2013), using integrated lightning flash density and population density data, suggest that at least 50% of tornadoes in Canada's sparsely populated areas may go undetected; in particular, they expose a suspected population bias resulting in "missing" tornadoes for an extensive region of southwest Ontario due to its rural landscape.…”

Section: "Missing" Tornadoes In Remote Forested Areasmentioning

confidence: 99%

Multi-Scale Remote Sensing of Tornado Effects

Womble

Wood

Mohammadi

2018

Front. Built Environ.

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To achieve risk-based engineered structural designs that provide safety for life and property from tornadoes, sufficient knowledge of tornado wind speeds and wind flow characteristics is needed. Currently, sufficient understanding of the magnitude, frequency, and velocity structure of tornado winds remain elusive. Direct measurements of tornado winds are rare and nearly impossible to acquire, and the pursuit of in situ wind measurements can be precarious, dangerous, and even necessitating the development of safer and more reliable means to understand tornado actions. Remote-sensing technologies including satellite, aerial, lidar, and photogrammetric platforms, have demonstrated an ever-increasing efficiency for collecting, storing, organizing, and communicating tornado hazards information at a multitude of geospatial scales. Current remote-sensing technologies enable wind-engineering researchers to examine tornado effects on the built environment at various spatial scales ranging from the overall path to the neighborhood, building, and ultimately member and/or connection level. Each spatial resolution contains a unique set of challenges for efficiency, ease, and cost of data acquisition and dissemination, as well as contributions to the body of knowledge that help engineers and atmospheric scientists better understand tornado wind speeds. This paper examines the use of remote sensing technologies at four scales in recent tornado investigations, demonstrating the challenges of data collection and processing at each level as well as the utility of the information gleaned from each level in advancing the understanding of tornado effects.

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“…Bayesian modelling has also been shown to be useful in addressing the issue of observational biases (Cheng et al, 2013(Cheng et al, , 2015(Cheng et al, , 2016. It provides an unbiased approach to incorporate the uncertainty associated with the forecasting of thunderstorms (Arhonditsis et al 2007(Arhonditsis et al , 2008a.…”

Section: Current Solutionsmentioning

confidence: 99%

“…Cheng et al (2013) used Bayesian hierarchical modelling to predict tornado occurrence across Canada and postulated that the likelihood of observing a tornado was related to population density. Cheng et al (2015) similarly used Bayesian hierarchical modelling to improve the tornado climatology of Canada using the combination of severe weather indices. Cheng et al (2016) continues this work by using a Bayesian hierarchical model framework to depict the causal linkage between the annual or seasonal tornado occurrences across North America.…”

Section: Chapter 4: Australian Convective Wind Gust Climatology Using Bayesian Hierarchical Modelling 41 Introductionmentioning

confidence: 99%

Climatology of severe convective wind gusts in Australia

Spassiani¹

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First and foremost, I would like to thank my parents, Angelina and Alessandro Spassiani, for all their love and support over the years, especially during my academic years abroad.Without them none of this would have been possible. I would also like to thank my siblings and their partners for always being there, Gian-Paolo and Elise Spassiani, Dr. Natasha Spassiani and Richard Pasquire, Lucas Spassiani and Alicia Marchini. Even though you were far away your support has meant a lot to me. I would have not gotten to the submission line without the help and support from the people I have met during my time at the University of Queensland (UQ). Especially without the help of my advisors, Dr. Matthew Mason and Dr. Richard Krupar III. Thank you both for your guidance and feedback throughout these four years. Thank you to the Wind Engineering Research group, Thomas Klöetzke, Ting Yang, Junwei Lyu, Razib Hussan and NipunMudiyanselage, for sitting and listening to me speak for countless hours about everything I did not understand. Thanks to all the friends I made during my time at UQ:

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A Bayesian modelling framework for tornado occurrences in North America

Cited by 16 publications

References 40 publications

Regional SST and SLP conditions related to tornado 'outbreak' environments 15 days later

Regional SST and SLP conditions related to tornado 'outbreak' environments 15 days later

Multi-Scale Remote Sensing of Tornado Effects

Climatology of severe convective wind gusts in Australia

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