Tornado fatalities in context: 1995–2018

Tornadoes account for the third highest average annual weather-related fatality rate in the United States. Here tornado fatalities are examined as rates within the context of multiple physical and social factors using tornado level information including population and housing units within killer tornado damage paths. Fatality rates are further evaluated across annual, monthly, and diurnal categories, as well as between fatality locations and across age and sex categories. The geographic distribution of fatalities are then given by season, time of day, and residential structures. Results can be used by emergency managers, meteorologists, and planners to better prepare for high-impact (i.e. fatality) events and used by researchers as quantitative evidence to further investigate the relationship between tornadoes, climate, and society.

Data-Driven Approaches for Tornado Damage Estimation with Unpiloted Aerial Systems

Tornado damage estimation is important for providing insights into tornado studies and assisting rapid disaster response. However, it is challenging to precisely estimate tornado damage because of the large volumes of perishable data. This study presents data-driven approaches to tornado damage estimation using imagery collected from Unpiloted Aerial Systems (UASs) following the 26 June 2018 Eureka Kansas tornado. High-resolution orthomosaics were generated from Structure from Motion (SfM). We applied deep neural networks (DNNs) on the orthomosaics to estimate tornado damage and assessed their performance in four scenarios: (1) object detection with binary categories, (2) object detection with multiple categories, (3) image classification with binary categories, and (4) image classification with multiple categories. Additionally, two types of tornado damage heatmaps were generated. By directly stitching the resulting image tiles from the DNN inference, we produced the first type of tornado damage heatmaps where damage estimates are accurately georeferenced. We also presented a Gaussian process (GP) regression model to build the second type of tornado damage heatmap (a spatially continuous tornado damage heatmap) by merging the first type of object detection and image classification heatmaps. The GP regression results were assessed with ground-truth annotations and National Weather Service (NWS) ground surveys. This detailed information can help NWS Weather Forecast Offices and emergency managers with their damage assessments and better inform disaster response and recovery.

Tornado fatalities in context: 1995--2018

Tornadoes account for the third highest average annual weather-related fatality rate in the United States. Here tornado fatalities are examined within the context of multiple physical and social factors using tornado level information related to population and housing units within killer tornado damage paths. The 24-year United States per-capita fatality rate is .32%. The per-housing unit fatality rate is .75%. Fatality rates are further evaluated across annual, monthly, and diurnal categorizations. They are also evaluated between fatality locations and across age and sex categorizations. The geographic distribution of fatalities are then given by season, time of day, and residential structures. Results can be used by emergency managers, meteorologists, and planners to better prepare for high-impact events and used by researchers as quantitative evidence to further investigate the relationship between tornadoes, climate, and society.

WSR-88D Tornado Intensity Estimates. Part I: Real-Time Probabilities of Peak Tornado Wind Speeds

The Storm Prediction Center (SPC) has developed a database of damage-surveyed tornadoes in the contiguous United States (2009–17) that relates environmental and radar-derived storm attributes to damage ratings that change during a tornado life cycle. Damage indicators (DIs), and the associated wind speed estimates from tornado damage surveys compiled in the Damage Assessment Toolkit (DAT) dataset, were linked to the nearest manual calculations of 0.5° tilt angle maximum rotational velocity Vrot from single-site WSR-88D data. For each radar scan, the maximum wind speed from the highest-rated DI, Vrot, and the significant tornado parameter (STP) from the SPC hourly objective mesoscale analysis archive were recorded and analyzed. Results from examining Vrot and STP data indicate an increasing conditional probability for higher-rated DIs (i.e., EF-scale wind speed estimate) as both STP and Vrot increase. This work suggests that tornadic wind speed exceedance probabilities can be estimated in real time, on a scan-by-scan basis, via Vrot and STP for ongoing tornadoes.

WSR-88D Tornado Intensity Estimates. Part II: Real-Time Applications to Tornado Warning Time Scales

A sample of damage-surveyed tornadoes in the contiguous United States (2009–17), containing specific wind speed estimates from damage indicators (DIs) within the Damage Assessment Toolkit dataset, were linked to radar-observed circulations using the nearest WSR-88D data in Part I of this work. The maximum wind speed associated with the highest-rated DI for each radar scan, corresponding 0.5° tilt angle rotational velocity Vrot, significant tornado parameter (STP), and National Weather Service (NWS) convective impact-based warning (IBW) type, are analyzed herein for the sample of cases in Part I and an independent case sample from parts of 2019–20. As Vrot and STP both increase, peak DI-estimated wind speeds and IBW warning type also tend to increase. Different combinations of Vrot, STP, and population density—related to ranges of peak DI wind speed—exhibited a strong ability to discriminate across the tornado damage intensity spectrum. Furthermore, longer duration of high Vrot (i.e., ≥70 kt) in significant tornado environments (i.e., STP ≥ 6) corresponds to increasing chances that DIs will reveal the occurrence of an intense tornado (i.e., EF3+). These findings were corroborated via the independent sample from parts of 2019–20, and can be applied in a real-time operational setting to assist in determining a potential range of wind speeds. This work provides evidence-based support for creating an objective and consistent, real-time framework for assessing and differentiating tornadoes across the tornado intensity spectrum.

Uso de sensoriamento remoto via satélite na identificação de rastros de destruição por tornados em um evento de tempo severo no Rio Grande do Sul

The number of tornado reports in Brazil has increased in recent years; nevertheless, it is likely that many occurrences over uninhabited areas and/or during night time hours remain unconfirmed, especially in a country devoid of official surveys of damage caused by intense winds. This work follows previous initiatives addressing the employment of remote sensing via satellite to identify damage paths associated with tornadoes. The nocturnal event analyzed in this study took place in north-northeast Rio Grande do Sul state from the night hours of 7 June 2017 into early morning hours of the following day, and represents an example of the characterization of significant damage caused by tornadoes despite the lack of visual confirmation of the phenomenon. Images produced by low-orbit environmental satellites of the Landsat and Sentinel series are analyzed, as well as imagery made available by the commercial-purpose environmental satellites that comprise the data base of Google Earth software. To aid in the identification of damage inflicted to dense vegetation, the web tools Global Florest Change and Global Florest Watch, which employ objective methods to detect abrupt modifications in the vegetation cover, are also utilized. Based on these products, it was possible to identify seven tornado damage tracks for the nocturnal event of June 2017, ratifying the value added by remote sensing products in the confirmation of tornadic episodes.