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Abstract Tornadoes continue to pose a significant risk to communities, making it essential to understand the near-surface winds impacting structures. This study aims to validate the use of cycloidal debris swaths (loop-like patterns of debris deposited on the surface) for estimating near-surface winds in tornadoes. Ground surveys of cycloidal debris swaths confirmed previous findings that these patterns primarily consist of organic debris deposition such as cornstalks from nearby fields. However, the possibility of scouring cannot be ruled out, and the exact formation mechanisms of cycloidal debris swaths remain unknown. Cycloidal debris swaths occur in both violent and weak tornadoes, typically outside of the peak growing season and in terrain with limited flow impedance, where debris is available to be lofted and deposited on the surface. Aerial imagery of cycloidal debris swaths was analyzed to measure the height and width of each individual loop. The measurements were used in an equation derived by Fujita to estimate wind speeds for the 10 December 2021 western Kentucky tornado near Princeton, Kentucky, and the 9 December 2023 Clarksville, Tennessee, tornado. These estimates were compared to other wind speed estimates from nearby anemometry, damaged structures, an overturned shipping container, and treefall. The findings of this study show that wind speed estimates from cycloidal debris swaths are consistent with wind speed estimates from other estimation methods. This study’s validation efforts and technological advancements (availability of aerial imagery and GIS software) support the use of the cycloidal debris swath method for estimating near-surface tornado wind speeds. Significance StatementWith the recent improvements in remote sensing technology and imagery availability, this study revisits a Fujita method that uses cycloidal debris swaths to estimate tornado wind speeds. Findings show that wind speed estimates derived from cycloidal debris swaths align with other estimation methods, validating their use for future tornado assessments. These findings allow for a better understanding of near-surface winds impacting structures and can help improve building codes and create more resilient communities.
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Estimating atmospheric motion winds from satellite image data using space‐time drift models
Abstract Geostationary weather satellites collect high‐resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half‐integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates us to statistically model wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. We conduct extensive simulation studies to determine the situations where our method performs well. The proposed method is applied to the GOES‐15 brightness temperature data over Colorado and reduces prediction error of brightness temperature compared to the DMW Algorithm.
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- PAR ID:
- 10485003
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Environmetrics
- Volume:
- 34
- Issue:
- 8
- ISSN:
- 1180-4009
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/env.2818
