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1. Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

   https://doi.org/10.3390/rs16071130  

   Nasimi, Mitra; Wood, Richard L (April 2024, Remote Sensing)

   Liu, Weifeng (Ed.)

   Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation. 

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2. Exploring Tornadic Debris Signature Hypotheses Using Radar Simulations and Large-Eddy Simulations

   https://doi.org/10.1175/JTECH-D-22-0141.1  

   Cross, Rachael N.; Bodine, David J.; Palmer, Robert D.; Griffin, Casey; Cheong, Boonleng; Torres, Sebastian; Fulton, Caleb; Lujan, Javier; Maruyama, Takashi (October 2023, Journal of Atmospheric and Oceanic Technology)

   Abstract When a tornado lofts debris to the height of the radar beam, a signature known as the tornadic debris signature (TDS) can sometimes be observed on radar. The TDS is a useful signature for operational forecasters because it can confirm the presence of a tornado and provide information about the amount of damage occurring. Since real-time estimates of tornadic intensity do not have a high degree of accuracy, past studies have hypothesized that the TDS could also be an indicator of the strength of a tornado. However, few studies have related the tornadic wind field to TDS characteristics because of the difficulty of obtaining accurate, three-dimensional wind data in tornadoes from radar data. With this in mind, the goals of this study are twofold: 1) to investigate the relationships between polarimetric characteristics of TDSs and the three-dimensional tornadic winds, and 2) to define relationships between polarimetric radar variables and debris characteristics. Simulations are performed using a dual-polarization radar simulator called SimRadar; large-eddy simulations (LESs) of tornadoes; and a single-volume,-matrix-based emulator. Results show that for all simulated debris types increases in horizontal and vertical wind speeds are related to decreases in correlation coefficient and increases in TDS area and height and that, conversely, decreases in horizontal and vertical wind speeds are related to increases in correlation coefficient and decreases in TDS area and height. However, the range of correlation coefficient values varies with debris type, indicating that TDSs that are composed of similar debris types can appear remarkably different on radar in comparison with a TDS with diverse scatterers. Such findings confirm past observational hypotheses and can aid operational forecasters in tornado detection and potentially the categorization of damage severity using radar data. 

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3. Field Assessment Structural Team (FAST-1) Dataset:StEER - 3 March 2020 Nashville Tornadoes

   https://doi.org/10.17603/ds2-a6mf-mz82  

   Kijewski-Correa, Tracy; Prevatt, David; Roueche, David; Pilkington, Stephanie; Liao, Yijun; Lombardo, Frank; Rawajfih, Hadiah; Gutierrez_Soto, Mariantonieta; Cullum, Keith; Davis, Brett; et al (January 2024, Designsafe-CI)

   Observing damage and documenting successful performance of buildings and other structures. Classes include residential, commercial, and power infrastructure. Methodologies include detailed damage assessments in Fulcrum, deployment of UAS for high-resolution aerial imagery, and deployment of surface-level panoramic imaging devices. Hazard indicators were also captured.In the early morning hours of March 3, 2020, a strong tornado struck the City of Nashville and the surrounding metropolitan region with estimated maximum wind speeds of 165 mph. The tornado passed through Nashville and continued east for 53 miles, impacting the communities of Donelson, Mt. Juliet and Lebanon before lifting. The same storm system then produced a second tornado that struck Cookeville, TN with estimated wind speeds of 175 mph. The Nashville tornado was the third tornado that passed through the Five Points area of Nashville. Damage was reported across a diverse cross-section of buildings spanning a number of communities: Camden, Germantown/North Nashville, East Nashville/Five Points, Donelson, Mt. Juliet, Lebanon and Cookeville. Exposure of an urban metro area to this series of tornadoes resulted in significant impacts to power infrastructure and building performance ranging from loss of roof cover and broken windows to complete destruction. Affected typologies and building classes include single and multi-family wood framed homes, commercial construction (ranging from big box stores down to smaller restaurants/retail shops), airport and industrial buildings, and a number of schools. More gravely, these nocturnal tornadoes claimed two dozen lives and injured hundreds more. Given the loss of life and property in this event and the fact that the Nashville tornado sequence impacted an urban area with diverse building classes and typologies, this event offers an opportunity to advance our knowledge of structural resistance to strong winds, particularly given that new construction was among the inventory significantly damaged. This project encompasses the products of StEER's response to this event: Preliminary Virtual Reconnaissance Report (PVRR), Early Access Reconnaissance Report (EARR) and Curated Dataset. 

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4. Polarimetric Radar Observations of Simultaneous Tornadoes on 10 May 2010 near Norman, Oklahoma

   https://doi.org/doi.org/10.1175/MWR-D-19-0156.1  

   Griffin, Casey B; Bodine, David J; Palmer, Robert D (January 2020, Monthly weather review)

   This study utilizes data collected by the University of Oklahoma Advanced Radar Research Center’s Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) C-band radar as well as the federal KTLX and KOUN WSR-88D S-band radars to study a supercell that simultaneously produced a long-track EF-4 tornado and an EF-2 landspout tornado (EF indicates the enhanced Fujita scale) near Norman, Oklahoma, on 10 May 2010. Contrasting polarimetric characteristics of two tornadoes over similar land cover but with different intensities are documented. Also, the storm-scale sedimentation of debris within the supercell is investigated, which includes observations of rotation and elongation of a tornadic debris signature with height. A dual-wavelength comparison of debris at S and C bands is performed. These analyses indicate that lofted debris within the tornado was larger than debris located outside the damage path of the tornado and that debris size outside the tornado increased with height, likely as the result of centrifuging. Profiles of polarimetric variables were observed to become more vertically homogeneous with time. 

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5. A Novel Technique to Correct Debris Centrifuging Bias in Doppler Velocity Measurements from Tornadoes

   https://doi.org/10.1175/JTECH-D-24-0065.1  

   Schneider, Morgan E; Bodine, David J; Palmer, Robert D; Torres, Sebastián M; Cheong, Boonleng; Fulton, Caleb J; Griffin, Casey B; Maruyama, Takashi (June 2025, Journal of Atmospheric and Oceanic Technology)

   Abstract Due to differences between air and debris motions, debris centrifuging creates bias in wind estimates based on Doppler velocities and radar wind retrievals in tornadoes. Anomalous radial divergence, azimuthal wind underestimation, and vertical velocity bias associated with debris centrifuging can lead to erroneous interpretations of tornado intensity and structure from radar data. A novel spectral velocity correction technique is developed to reduce bias by identifying rain and debris motion in radar signals using dual-polarization spectral density estimation and fuzzy logic classification. This technique successfully improves Doppler velocity estimates in simulated S-band polarimetric time series data, although debris concentration modulates both the magnitude and correctability of velocity bias. Large bias magnitudes associated with high debris concentrations are the most difficult to fully correct using this technique, especially at low elevation angles and near the center of the tornado. However, the magnitudes of corrections applied are proportional to the original bias magnitudes, suggesting that the technique performs consistently across low and high debris concentrations. Spectral correction results in an overall 84% reduction in bias in simulations. The spectral correction technique is also applied to dual-polarization S-band radar observations of the 20 May 2013 Moore, Oklahoma tornado. Overall increases in Doppler velocity magnitudes, especially at lower elevation angles, imply that spectral correction can successfully reduce centrifuging bias in observed Doppler velocities. 

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