Reconstruction of Hurricane Sally (2020) geometric parameters during landfall, using tree lean azimuths

Keywords: Hurricane, climate change, tree lean, pine, Gulf Coast, reconstruction, model, methodology, smartphone, fieldwork
Abstract Type: Virtual Poster Abstract

Abstract

Hurricane Sally was part of the North Atlantic basin’s hyperactive 2020 hurricane season, whose tropical cyclones produced elevated rainfall attributable to human-induced climate change. Top-tier tropical storm observation technologies including doppler radar, sail drones, land/sea weather stations, and Air Force reconnaissance missions, are neither universally available/affordable, nor are they invulnerable to service-disruption. This research tested the ability of tree-lean analysis to provide an accurate measure of Hurricane Sally’s parameters at second landfall along the US Gulf Coast. We sampled 556 trees in five sites in coastal Alabama and Florida using a smartphone magnetometer to determine the direction of lean. Using the tree-lean data set, we modeled Sally’s windfield across the entire study area by extracting important physical values from a logistic 4-parameter fit to the full set. We found Sally’s inner eye diameter to be ~49km and the alongshore position of Sally’s geometric center at -87.68 ± 0.02°longitude. We also calculated the location of Sally’s offshore geometric-center during sample-disturbance under center-front eyewall. To verify the tree-lean findings, we analyzed concurrent microwave scatterometer images from an orbital platform and determined Sally’s inner eye diameter to be 52—65km at 95% confidence interval and alongshore position of Sally’s geometric center, -87.66 ± 0.02°longitude. Our tree lean reconstruction fit well statistically with independent validation data from imagery. Tree lean analysis provided a surprisingly accurate measure of hurricane parameters. Our low cost, low tech, labor-efficient methodology could support teaching/outreach opportunities; plus aid reports, hindcasts, and stand-damage models of future similar events.