Social Susceptibility–Driven Longitudinal Tornado Reconnaissance Methodology: 2021 Midwest Quad-State Tornado Outbreak

Abstract

With the impact of climate change, the intensity and frequency of tornado events have been increasing. Enhancing tornado reconnaissance methods can comprehensively capture building damage and recovery data following tornado events and outbreaks, thereby strengthening community resilience against the threat of future tornado events. Advancements in tornado data reconnaissance research have embraced remote sensing techniques to assess building damage after tornado events, supplanting traditional reconnaissance methods relying on handheld cameras with GIS mapping. Community resilience research offers a groundbreaking perspective, stressing the importance of assessing buildings throughout their recovery cycle—from damage and functionality to recovery—and considering their socioeconomic stability in the face of natural hazards. This paradigm shift in approach lays the groundwork for advancing tornado reconnaissance through longitudinal studies. This paper presents a holistic methodology for the longitudinal tornado reconnaissance study, beginning with socially driven community selection and extending through rapid perishable data collection and processing. The 2021 Midwest quad-state tornado outbreak serves as an illustrative example of these methods and tools, with longitudinal tornado reconnaissance findings presented herein. The methodology proposed marks the inception of a new era in longitudinal tornado reconnaissance, which facilitates community resilience research through model calibration and new recovery model development to provide decision-making support to stakeholders, city planners, practitioners, and beyond.