981 A Comparison of Three Wind Speed Estimation Techniques Based on Tornado-Induced Treefall Patterns

Tuesday, 14 January 2020
Hall B (Boston Convention and Exhibition Center)
Christopher M. Godfrey, Univ. of North Carolina Asheville, Asheville, NC; and C. D. Karstens, D. Rhee, C. J. Peterson, and F. T. Lombardo
Manuscript (2.5 MB)

Handout (25.2 MB)

The 22 May 2011 Joplin, Missouri EF5 tornado provides a unique opportunity to compare three published techniques that estimate wind speeds from tornadoes based on either discernible patterns of treefall or the severity of damage within forested areas: the Lombardo, Karstens, and Godfrey–Peterson methods. Application of both the Lombardo and Karstens methods requires the creation of a detailed database of digitized, georeferenced treefall vectors, collected through field surveys or high-resolution aerial imagery. The best match between the treefall patterns identified through a mapping of these vectors and treefall patterns produced by a simulated wind field provides an estimate of the wind speed from the tornado. In contrast, the Godfrey–Peterson method uses the results of a coupled wind and tree resistance model to estimate the most probable wind speed associated with distinct levels of forest damage and is particularly useful in regions with complex topography. Despite the very different methodological approaches, all three techniques capture the spatial characteristics of the Joplin tornado wind field and yield similar estimates of wind speed as a function of the percentage of trees blown down. This presentation shows the results of this methodological comparison and highlights that while these approaches differ in procedural aspects, data collection, and processing requirements, each method produces comparable results with some notable differences. This general agreement across the methods supports the application of any one method with confidence, given individual circumstances and data availability.

Supplementary URL: http://www.atms.unca.edu/cgodfrey/research/ams2020

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