989 Characterizing Ground Markings from the 24 March 2023 Rolling Fork, MS EF4 Tornado

Thursday, 1 February 2024
Hall E (The Baltimore Convention Center)
Dominic Candela, Univ. of Oklahoma School of Meteorology, Norman, OK; CIWRO, Norman, OK; NSSL, Norman, OK; and M. A. Wagner, E. Rasmussen, M. Coniglio, A. Lyza, A. Sizemore, K. L. Ortega, C. N. Satrio, L. E. Pounds, and E. Tirone

On 24 March 2023, an EF4 tornado tracked through the rural delta region of Mississippi, hitting the town of Rolling Fork, MS and surrounding farmland and old growth forests. As part of Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign, multiple survey teams from NSSL, CIWRO, NWS, Jackson State and Mississippi State Universities conducted ground and uncrewed aerial systems (UAS) damage surveys in coordination with NWS Jackson. UAS crews mapped approximately six miles of the damage path, obtaining high-resolution visible (3 cm) and multispectral (8 cm) imagery, and focused on interesting ground markings in open fields and areas of intense tree damage. High-resolution satellite imagery (e.e. Planet Lab Skysat 50 cm spatial resolution) was also collected over the entire damage path to supplement and calibrate ground and UAS surveys. The CIWRO/NSSL and NWS Jackson teams found numerous instances of EF4 damage along the Rolling Fork, MS track, with a peak estimated gust speed of 87.2 m s-1 (195 mph). This work characterizes ground markings associated with the Rolling Fork tornado and estimates near surface wind flows over forested areas using treefall models. Damage patterns and analysis reveal rapid fluctuations in damage intensity, strongly convergent damage, and several instances of seemingly unusual damage instances and ground markings that changed throughout the lifecycle of the tornado. By leveraging high resolution imagery, radar data, and treefall patterns, this case provides a unique opportunity to combine multiple datasets and provide context for near-surface wind flows and interactions with land cover. Such information is needed to better rate tornado intensity in rural areas and understand tornado behavior.
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