Satellite-based identification of tornado damage tracks from the 27 April 2011 severe weather outbreak

Molthan, Andrew; Bell, Jordan R.; Cole, Tony; Burks, Jason

doi:10.15191/nwajom.2014.0216

Cited by 25 publications

(18 citation statements)

References 11 publications

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“…The first attempt to use satellite imagery to detect and document tornado damage in the boreal forest was made in Canada in the 1970s using images from the sun-synchronous Landsat Multispectral Scanner with a nominal spatial resolution of 60 m (Sayn-Wittgenstein and Wightman 1975). A number of studies in the United States have examined tornado damage using satellite imagery (Yuan et al 2002;Jedlovec et al 2006;Molthan et al 2014). More recent attempts in northeast Europe using Landsat data uncovered 105 previously unreported tornadoes that occurred between 2000 and 2014 (Shikhov and Chernokulsky 2018).…”

Section: Methodsmentioning

confidence: 99%

The Northern Tornadoes Project: Uncovering Canada’s True Tornado Climatology

Sills

Kopp

Elliott

et al. 2020

Bulletin of the American Meteorological Society

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Section: Methodsmentioning

confidence: 99%

The Northern Tornadoes Project: Uncovering Canada’s True Tornado Climatology

Sills

Kopp

Elliott

et al. 2020

Bulletin of the American Meteorological Society

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“…In particular, VCP 212 calls for an elevation angle of 6.48, whereas KTLX tended to scan at ;6.45-6.478 (which rounds to 6.58), and 10.08, whereas KTLX tended to scan at ;10.058-10.068 (which rounds to 10.18), during the period examined in this paper. In an attempt to learn more about the extent and nature of the damage corresponding to the in-person survey, we examined poststorm high-resolution, multispectral Landsat 8 and Sentinel-2 data (e.g., Yuan et al 2002;Jedlovec et al 2006;Molthan et al 2014;Kingfield and de Beurs 2017). Unfortunately, abundant cloudiness FIG.…”

Section: Meteorological and Event Overviewmentioning

confidence: 99%

An Analysis of an Ostensible Anticyclonic Tornado from 9 May 2016 Using High-Resolution, Rapid-Scan Radar Data

Snyder

Bluestein

Wienhoff

et al. 2020

Weather and Forecasting

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Tornadic supercells moved across parts of Oklahoma on the afternoon and evening of 9 May 2016. One such supercell, while producing a long-lived tornado, was observed by nearby WSR-88D radars to contain a strong anticyclonic velocity couplet on the lowest elevation angle. This couplet was located in a very atypical position relative to the ongoing cyclonic tornado and to the supercell’s updraft. A storm survey team identified damage near where this couplet occurred, and, in the absence of evidence refuting otherwise, the damage was thought to have been produced by an anticyclonic tornado. However, such a tornado was not seen in near-ground, high-resolution radar data from a much closer, rapid-scan, mobile radar. Rather, an elongated velocity couplet was observed only at higher elevation angles at altitudes similar to those at which the WSR-88D radars observed the strong couplet. This paper examines observations from two WSR-88D radars and a mobile radar from which it is argued that the anticyclonic couplet (and a similar one ~10 min later) were actually quasi-horizontal vortices centered ~1–1.5 km AGL. The benefits of having data from a radar much closer to the convective storm being sampled (e.g., better spatial resolution and near-ground data coverage) and providing more rapid volume updates are readily apparent. An analysis of these additional radar data provides strong, but not irrefutable, evidence that the anticyclonic tornado that may be inferred from WSR-88D data did not exist; consequently, upon discussions with the National Weather Service, it was not included in Storm Data.

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“…Remote-sensing imagery, including satellite and/ or aerial imaging and lidar capabilities (Darnell, 2012) provides an effective basis for the confirmation of tornadoes in forested and low-population areas where tornadoes may otherwise go undetected. Broad-scale geospatial data, as from earth-observing satellites (EOS), can verify the occurrence (tracks) of suspected tornadoes and can provide pertinent information about path locations, lengths, and widths (Bentley et al, 2002;Yuan et al, 2002;Myint et al, 2008;Molthan et al, 2011Molthan et al, , 2014, thereby providing a basis for more accurately developing the tornado climatology of a region.…”

Section: Establishing Improved Tornado Climatologiesmentioning

confidence: 99%

“…More recently, researchers applied PCA and NDVI methodologies in preliminary studies to help determine tornado intensity in forested areas of Alabama using NASA EOS imagery following the April 2011 tornado outbreak, with specific emphasis to provide immediate guidance for ground surveys (Molthan et al, 2011(Molthan et al, , 2014. NDVI enhancements using posttornado images alone were helpful for semi-automated detection of paths for strong tornadoes; however, before-and-after images were necessary to identify paths of weaker tornadoes (Molthan et al, 2014). For 30-m Landsat imagery of the 2011 tornado outbreak in forested areas of Alabama, Kingfield and deBeurs (2014) observed that a Disturbance Index derived from 6 Landsat spectral bands more consistently identified severe tornado paths than did an NDVI analysis derived from 2 spectral bands.…”

Section: Tornado Path Analysis With Earth-observing Satellite Datamentioning

confidence: 99%

“…Remote sensing imaging technologies have also proven effective for detecting and delineating tornado tracks in remote areas, particularly forested areas that may otherwise go undetected due to a lack of population and trained spotters (e.g., Skow and Cogil, 2017). Recent analysis techniques have also shown promise for estimating the intensity of tornadoes in forested areas (e.g., Jedlovec et al, 2006;Molthan et al, 2011Molthan et al, , 2014Karstens et al, 2013;Godfrey and Peterson, 2017).…”

Section: Introductionmentioning

confidence: 99%

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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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Satellite-based identification of tornado damage tracks from the 27 April 2011 severe weather outbreak

Cited by 25 publications

References 11 publications

The Northern Tornadoes Project: Uncovering Canada’s True Tornado Climatology

The Northern Tornadoes Project: Uncovering Canada’s True Tornado Climatology

An Analysis of an Ostensible Anticyclonic Tornado from 9 May 2016 Using High-Resolution, Rapid-Scan Radar Data

Multi-Scale Remote Sensing of Tornado Effects

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