Tornado Intensity Research Articles

Development and Application of a Tornado Database for the Chinese Mainland

This study develops a preliminary tornado database for the Chinese mainland using information provided by the Yearbook of Meteorological Disasters in China (YMDC) from 2003 to 2019 and other public media report data. This database includes tornado occurrence time, geographical location, intensity, and damage descriptions. A modified Enhanced Fujita (EF) scale criterion adapted to the description of the damage indicator (DI) and degree of damage (DOD) in the YMDC and media reports is presented to better estimate the tornado intensity. The spatial and temporal distribution characteristics of tornadoes in Chinese mainland are examined. A stochastic simulation algorithm is proposed to perform a risk assessment of tornado hazards. The occurrence of tornadoes is randomly sampled using negative binomial distribution. The kernel density estimation (KDE) method based on the Gaussian kernel is applied to estimate the probability density of geographically dependent tornado occurrence before randomly generating tornado occurrence locations. The probability and conditional probability of tornado occurrence with different intensity levels are calculated for different counties in Jiangsu and Guangdong Provinces using Bayes’ theorem. The database provides a forward step toward rational assessment of tornado-induced disasters in Chinese mainland. The database can be accessed at https://doi.org/10.4121/19801633.v1 (Zhang et al., 2022).

Radar Signatures Associated with Quasi-Linear Convective System Mesovortices

Abstract Recent operationally driven research has generated a framework, known as the three ingredients method and mesovortex warning system, that can help forecasters anticipate mesovortex development and issue warnings within quasi-linear convective systems (QLCSs). However, dual-polarization radar data has not yet been incorporated into this framework. Therefore, several dual- and single-polarization radar signatures associated with QLCS mesovortices were analyzed to determine if they could provide additional information about mesovortex development and intensity. An analysis of 167 mesovortices showed that 1) KDP drops precede ∼95% of mesovortices and provide an initial indication of where a mesovortex may develop; 2) midlevel KDP cores are a potentially useful precursor signature because they precede a majority of mesovortices and have higher magnitudes for mesovortices that produce wind damage or tornadoes; 3) low-level KDP cores and areas of enhanced spectrum width have higher magnitudes for mesovortices that produce wind damage or tornadoes but tend to develop at about the same time as the mesovortex, which makes them more useful as diagnostic than as predictive signatures; and 4) as range from the radar increases, the radar signatures become less useful in anticipating mesovortex intensity but can still be used to anticipate mesovortex development or build confidence in mesovortex existence. Significance Statement The purpose of this study is to look at weather radar features that might help forecasters predict the development and intensity of tornadoes and strong winds within linear thunderstorm systems. Our results show that the intensity and trends of some radar features are helpful in showing when these hazards might develop and how strong they might be, while other radar features are less helpful. This information can help forecasters focus on the most useful radar features and ultimately provide the best possible warnings.

Comparison of Tornado Damage Characteristics to Low-Altitude WSR-88D Radar Observations and Implications for Tornado Intensity Estimation

Abstract Given the obvious difficulties in directly sampling tornadic wind fields, ongoing work continues to improve estimates of near-ground wind speeds in tornadoes. This study builds upon a recently proposed empirical relationship between radar-observed velocities in the lowest 150 m above ground level (AGL) and the theoretical peak 15-m AGL wind speed. We create and analyze a dataset of 194 velocity observations within tornadoes in the lowest 150 m AGL. These observations are drawn from 105 individual tornadoes that occurred across a diverse range of EF-scale ratings (EF0–4), convective modes (discrete supercell and quasi-linear convective system), geographical regions, and housing unit densities (HUDs). Comparing the radar-estimated and damage-estimated tornado wind speeds, and corresponding EF- and F-scale ratings, is the primary focus of the ensuing analysis. Consistent with recent work, damage-estimated tornado wind speeds tend to be lower than radar-estimated near-surface wind speeds, especially for stronger tornadoes. Damage- and radar-estimated wind speed differences are not strongly related to the availability of damage indicators (as measured by HUD). While some relationship exists—particularly underestimates of peak wind speeds for strong–violent tornadoes in low HUD areas—the tendency of radar-based strong/violent tornado intensity estimates to be meaningfully higher than EF-scale-based damage estimates exists across the HUD spectrum. The legacy F-scale wind speed ranges may ultimately provide a better estimate of peak tornado wind speeds at 10–15 m AGL for strong–violent tornadoes and a better damage-based intensity rating for all tornadoes. These results are contextualized with regard to ongoing community efforts to improve tornado intensity estimation. Significance Statement Due to the numerous difficulties in collecting direct observations of tornado wind speeds, we compare methods that are used to estimate near-ground wind speeds using both radar measurements of wind speeds aloft and damage. Our results show that 1) the damage-estimated intensities of stronger tornadoes are more likely to be underestimates of true wind speeds than weaker tornadoes based on radar observations, 2) this bias is present for tornadoes that occur in more built-up areas as well as more sparsely populated ones, and 3) the legacy Fujita scale may provide better wind speed estimates in stronger tornadoes. These findings contribute to community-wide efforts to improve damage-based estimates of peak tornado wind speeds.

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

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.

The Jersey tornado and hailstorm of 1–2 November 2023

AbstractA powerful tornado affected eastern parts of Jersey, Channel Islands, on the night of Wednesday 1 and Thursday 2 November 2023, associated with a supercell thunderstorm. As well as spawning the tornado, the parent thunderstorm also produced a severe hailstorm, with hailstones upwards of 5cm in diameter documented. A TORRO site investigation, assisted by staff from Jersey Met, concluded that the tornado produced a continuous damage path across the east of the island of around 8km in length, with a width of up to 500m. The tornado was assigned a provisional rating of T6 on the International Tornado Intensity Scale.

The Role of the School Nurse in Addressing Climate-Associated Illnesses: Mental Well-being.

Human health is being impacted by anthropogenic (human-made) climate change. This article describes four ways that climate change may affect mental well-being in school-age children. First, natural disasters-such as more frequent and intense tornadoes and flash floods-may have a direct influence on mental well-being by contributing to acute anxiety and distress. Second, indirect effects of severe weather-including changes in social support systems-may affect mental well-being by increasing isolation. Third, children may suffer feelings of anxiety or depression if they perceive a sense of powerlessness to solve the challenges of a changing climate. Finally, school nurses need to be aware of the emergence of correlations-such as data that suggest increases in temperature may influence the use of inpatient mental health services and suicidal ideations-that require further scientific exploration. This article aims to increase school nurses' understanding of how climate changes may impact the mental well-being of school-age children and to provide strategies for creating a safe, healthy learning environment. This article is the fourth in a series aimed at raising awareness among school nurses about climate-associated illnesses and equipping them with the resources they need to protect school-age children's health.

ENERGY OF TORNADOES AND LANDSPOUTS

According to the data on the sizes and intensity of tornadoes and landspouts, their characteristic times and forcings are found. Depending on the intensity, the average times vary by a factor of 3 and the forcings by a factor of 6. The energy of the vortices has been estimated and is expressed, among other things, in TNT equivalent mass units.

Информационная инфраструктура, обеспечивающая своевременное обнаружение мезомасштабных конвективных комплексов

The results of the development of an information infrastructure that provides timely detection of mesoscale convective complexes (MCC) are described, automatic procedures that implement the basic functionality of this infrastructure are described. Along with the detection of MCC, the described procedures allow us to identify zones of deep convection, i.e. such MCC that develop according to the scenario of "daytime storms", i.e. intense thunderstorms, squalls and tornadoes. Information infrastructure, provides integration of satellite microwave radiometric sensing data of geostationary observations in the visible and IR range. This integration is designed to ensure the restoration of atmospheric temperature and humidity profiles in the conditions of emergency atmospheric situations caused by the MCC. The methodological feature of the developed approach is to minimize the probability of stopping the data analysis system when access to individual sources of a priori information is terminated. This technique is based on the use of an automatic system of adaptation to changes in access conditions and the application of the results of model forecasts of the development of atmospheric processes.

Subvortices within a Numerically Simulated Tornado: The Role of Unstable Vortex Rossby Waves

Abstract Multiple subvortices corresponding to suction vortices in observations are obtained within a simulated tornado for the EF4 tornado case of Funing, China, on 23 June 2016. Within the simulation, the tornado evolves from a one-cell structure with vorticity maximum at its center to a two-cell structure with a ring of vorticity maximum. Five well-defined subvortices develop along the ring. The radial profile of tangential wind across the vorticity ring satisfies the necessary condition of barotropic instability associated with phase-locked, counterpropagating vortex Rossby waves (VRWs) along the ring edges. The phased-locked waves revolve around the parent vortex at a speed less than the maximum azimuthal-mean tangential velocity, agreeing with theoretically predicted VRW phase speed. The radii within which the wave activities are confined are also correctly predicted by the VRW theory where radial group velocity approaches zero. Several other characteristics related to the simulated subvortices agree with VRW theories also. The most unstable azimuthal wavenumber depends on the width and the relative magnitude of vorticity of the vortex ring. Their values estimated from the simulation prior to subvortex formation correctly predict wavenumber 5 as the most unstable. The largest contribution to wave kinetic energy is diagnosed to be from the radial shear of azimuthal wind term, consistent with barotropic instability. Vorticity diagnostics show that vertical vorticity stretching is the primary vorticity source for the intensification and maintenance of the simulated subvortices. Significance Statement Multiple subvortices or suction vortices in tornadoes can produce extreme damage but their cause is not well understood. An intense tornado from China that developed five strong subvortices, along a vorticity ring a distance from the tornado vortex center, was successfully simulated. By examining the propagation and other characteristics of these subvortices and comparing them with theoretical models of vortex Rossby waves (VRWs) that have been studied mostly in the context of typhoons/hurricanes, it is believed that nonlinear growth of unstable VRWs associated with barotropic instability is the primary reason for the development of subvortices within the tornado. The conclusion is further supported by analyses of the primary source of wave growth energy. Vertical vorticity stretching is the main vorticity source for intensifying and maintaining the subvortices at their development and mature stages. The unstable growth of VRWs as the cause of tornado suction vortices has not been analyzed in detail for realistic tornadoes until now.

Energy of Tornadoes and Landspouts

According to the data on the sizes and intensity of tornadoes and landspouts, their characteristic times and forcings are found. Depending on the intensity, the average times vary by a factor of three and the forcings by a factor of six. The energy of the vortices is estimated and is expressed, among other things, in TNT equivalent mass units.

The Impact of Human‐Induced Climate Change on Future Tornado Intensity as Revealed Through Multi‐Scale Modeling

AbstractA novel, multi‐scale climate modeling approach is used to show the potential for increases in future tornado intensity due to anthropogenic climate change. Historical warm‐ and cool‐season (WARM and COOL) tornado events are virtually placed in a globally warmed future via the “pseudo‐global warming” method. As hypothesized based on meteorological arguments, the tornadic‐storm and associated vortex of the COOL event experiences consistent and robust increases in intensity in an ensemble of imposed climate‐change experiments. The tornadic‐storm and associated vortex of the WARM event experiences increases in intensity in some of the experiments, but the response is neither consistent nor robust, and is overall weaker than in the COOL event. An examination of environmental parameters provides further support of the disproportionately stronger response in the cool‐season event. These results have implications on future tornadoes forming outside of climatologically favored seasons.

Environmental and Radar-Derived Predictors of Tornado Intensity within Ongoing Convective Storms

Analyses of Doppler radar data and environmental parameters for 300 tornado cases are used to propose an alternative framework for tornado intensity prediction during pretornadic stages of ongoing storms, conditional on tornadogenesis. This framework is founded on the robust relationship (R² = 0.69) between pretornadic mesocyclone width and the EF rating of the subsequent tornado. In contrast, the linear relationship between pretornadic mesocyclone intensity and EF scale is much weaker (R² = 0.29). Environmental information for each case was additionally used to explore relationships between environmental parameters and tornado intensity. Such relationships depend in part on how the tornado-intensity categories are distributed [i.e., nonsignificant (EF0–1) versus significant (EF2–5), or weak (EF0–1) versus strong (EF2–3) versus violent (EF4–5)]. Low-level shear parameters discriminate the environments of significant tornadoes from nonsignificant tornadoes, but not the environments of violent tornadoes from strong tornadoes. The converse is true for thermodynamic parameters. Operational implementation of this framework for thepurposes of impact-based warnings will require real-time, automated quantification of mesocyclone width in addition to intensity and other attributes. The information gained from the pretornadic analysis demonstrated in this study would allow an operational forecaster to be aware of—and communicate—information about potential tornado intensity in warning text to the public before a tornado develops to better protect life and property. Currently, these relationships are being utilized in machine learning models for binary prediction of non-significant versus significant tornado intensity where skill is being demonstrated.

Assessment of wind speeds along the damage path of the Alonsa, Manitoba EF4 tornado on 3 August 2018

Given the impracticality of attempting to directly measure wind speeds in tornadoes, wind speed estimation typically relies on the assessment of damage to structures and vegetation using classifications described in the Enhanced Fujita (EF) Scale. The advent of technology enabling the collection of large amounts of data, including detailed ground, drone, and aerial imagery, has led to a growth in research on non-conventional approaches for estimating tornado wind speeds. Research methods focused on defining the tornadic wind field based on forensic analysis of damage observations have shown promise for improving tornado assessments in a quantitative manner. In this study, novel methods for collecting forensic data following tornadoes are presented. Data from the Alonsa, MB tornado are applied to estimating the wind field along the damage path using treefall pattern analysis and threshold debris flight speed calculations. Comparison of the resulting wind speed estimates show reasonable agreement, with maximum speeds from both methods in the EF5 range. These research methods yield higher wind speeds than the maximum value obtained from the conventional EF-Scale assessment, which is in the low-end of the EF4 range based on a wood-frame house with sub-standard construction that was swept entirely from its foundation. Further work is still needed to make these methods operational for routine tornado intensity estimates.

Factors Associated with Housing Damage Caused by an EF4 Tornado in Rural Areas of Funing, China.

Rural areas are vulnerable to natural disasters and tend to suffer severe losses. An EF4 tornado occurred in Funing on 23 June 2016, killing 99 people, injuring at least 846 people, and destroying more than 2000 houses. Using a multinomial logistic regression model, this study explored the influencing factors between housing damage and variables of building conditions, tornado intensity, and village environmental factors. The results show that 2-story houses and masonry houses were more likely to be slightly damaged or be in a dangerous state. Furthermore, the building area was positively related to houses in two categories: slight damage (SD) and dangerous and requiring immediate repair (DR), indicating that the larger or taller the house, the more severe the damage. In terms of tornado intensity, houses classified as SD were more likely to be hit by EF4 tornados than by EF3 tornados, and houses were damaged more by EF1 or EF2 tornados. This finding demonstrates that the level of housing damage was not strongly correlated with the tornado intensity. Slightly damaged houses exhibited the highest correlation with environmental factors. The proportion of slightly damaged houses was positively correlated with the water area in the village, unlike the proportion of houses in the DR and unable to be repaired (UR) categories. Moreover, the larger the water area of a village, the less housing damage it suffered. These findings provide new insights into minimizing housing damage in wind disasters to improve disaster prevention planning in rural areas.

Distinguishing between Hodographs of Severe Hail and Tornadoes

Abstract Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.

Reliability of a structure stricken by a tornado

A tornado may become a very dangerous climatic action for any structure, depending on the tornado intensity and extent of the structure. The origin of a tornado, its range and further activity appear to be unpredictable and completely random. The strike of a tornado to a structure depends on the tornado movement and the structure extent. The tornado intensity is specified by the wind speed, following observed damages and consequences. The probability of a tornado strike on a structure depends on the extent of the structure itself, on the tornado range, and the extent of the considered reference area, for which data concerning tornado appearance are available. The probability of failure of a structure is given by the product of the probability of contact of a tornado with the structure and the probability of exceeding the design speed of the tornado wind. The probability of exceeding the design wind speed depends on the assumed distribution of wind speed and the frequency of tornadoes during the required lifetime of the structure. The target failure probability of common structures presented in available documents is 10−6 per year, and 10−7 per year of structures in power plants. However, it is not clear how these criteria have been derived. Further development of reliability theory of tornado-stricken structures is recommended to be focused on the risk analysis of appropriate systems, of which the considered structures are elements, on the target failure probability, on detailed analysis of the probability distribution of tornado wind speed and their frequency during the required lifetime of the structures.

Analysis of the 12 April 2020 Northern Louisiana Tornadic QLCS

On 12 April 2020, a tornadic quasi-linear convective system (QLCS) produced two EF-3 tornadoes in Ouachita Parish, Louisiana in close proximity to instrumentation operated by the University of Louisiana Monroe’s (ULM) Atmospheric Science program. In addition to the in situ environmental information, a high-resolution aerial damage survey was conducted by the ULM Unmanned Aerial Systems program. In this paper, these datasets are used to provide a comprehensive environmental and storm-scale analysis of the tornadic QLCS through northern Louisiana. In addition, we discuss the importance of aerial damage surveys, and how Doppler radar-derived tornado intensity estimates compared to the damage survey.

Damage Analysis and Close-Range Radar Observations of the 13 April 2019 Greenwood Springs, Mississippi, Tornado during VORTEX-SE Meso18-19

Abstract A tornado outbreak occurred across the Southeast United States on 13–14 April 2019, during the Verification of the Origins of Rotation in Tornadoes Experiment–Southeast (VORTEX-SE) Meso18-19 experiment. Among the most noteworthy events was a pair of large tornadoes in Monroe County, Mississippi, near the Columbus Air Force Base (GWX) Weather Surveillance Radar–1988 Doppler (WSR-88D). The second tornado, near the Greenwood Springs community, formed within the “no data” region near the radar and passed about 900 m to its east, rapidly strengthening into an intense tornado. This tornado produced forest devastation and electrical infrastructure damage up to at least EF4 intensity. The maximum radial velocity from GWX was 81.5 m s−1 (182 mph) in a resolution volume centered at 56 m (183 ft) above radar level. This paper presents a damage survey of the Greenwood Springs tornado and compares this assessment to the GWX data. A displacement of the maximum forest damage from the maximum radial velocity, despite the radar beam sampling <100 m ARL, is documented, as well as other likely effects of debris loading by the tornado on the observed radar signatures. The radar observations are placed into context with past mobile radar studies to illustrate the unique nature of this dataset. The relationship between radar data and damage observations, the implications for tornado structure in rough terrain and land cover, and the use of forest damage and radar data in tornado intensity estimation are discussed. Significance Statement This study showcases radar and damage observations of an intense tornado in a forested region of Mississippi. The formation of the tornado within 1 km of a WSR-88D allowed for near-surface radar observations to be collected as significant tree destruction was occurring. Doppler velocities below 60 m above radar level (ARL), near tree canopy top, exceeded 80 m s−1. Tree damage patterns were complicated while the tornado was near maximum intensity. The most severe tree damage was notably displaced from the highest radar-observed velocities, despite the radar sampling as low as 45 m ARL. These findings highlight challenges in utilizing radar data to estimate tornado intensity and structure, particularly in a region of relatively high surface and terrain roughness.

Improving Estimates of U.S. Tornado Frequency by Accounting for Unreported and Underrated Tornadoes

Abstract Many tornadoes are unreported because of lack of observers or are underrated in intensity, width, or track length because of lack of damage indicators. These reporting biases substantially degrade estimates of tornado frequency and thereby undermine important endeavors such as studies of climate impacts on tornadoes and cost–benefit analyses of tornado damage mitigation. Building on previous studies, we use a Bayesian hierarchical modeling framework to estimate and correct for tornado reporting biases over the central United States during 1975–2018. The reporting biases are treated as a univariate function of population density. We assess how these biases vary with tornado intensity, width, and track length and over the analysis period. We find that the frequencies of tornadoes of all kinds, but especially stronger or wider tornadoes, have been substantially underestimated. Most strikingly, the Bayesian model estimates that there have been approximately 3 times as many tornadoes capable of (E)F2+ damage as have been recorded as (E)F2+ [(E)F indicates a rating on the (enhanced) Fujita scale]. The model estimates that total tornado frequency changed little over the analysis period. Statistically significant trends in frequency are found for tornadoes within certain ranges of intensity, pathlength, and width, but it is unclear what proportion of these trends arise from changes in damage survey practices. Simple analyses of the tornado database corroborate many of the inferences from the Bayesian model. Significance Statement Prior studies have shown that the probabilities of a tornado being reported and of its intensity, track length, and width being accurately estimated are strongly correlated with the local population density. We have developed a sophisticated statistical model that accounts for these population-dependent tornado reporting biases to improve estimates of tornado frequency in the central United States. The bias-corrected tornado frequency estimates differ markedly from the official tornado climatology and have important implications for tornado risk assessment, damage mitigation, and studies of climate change impacts on tornado activity.

Terrain Effects on the 13 April 2018 Mountainburg, Arkansas EF2 Tornado

Storm-scale interactions with rough terrain are complex. Terrain has been theorized to impact the strength of low-level mesocyclones. Surface roughness and modifications of the surrounding environment also may impact tornadogenesis or tornado intensity. The Mountainburg, Arkansas EF2 tornado on 13 April 2018 traveled along a path with minor variations in intensity and elevation throughout most of the nearly 19-km (11.8 mi) damage path as the storm moved along a river valley. A detailed damage survey showed that the tornado then made an abrupt ascent of more than 200 m (656 ft) in the last 2 km (1.2 mi) before dissipating. By examining model soundings and conducting a detailed terrain analysis, this study examines what role terrain may have had in channeling the momentum surge and enhancing the low-level vorticity to influence tornadogenesis. Other storm-scale factors are investigated to determine their potential impact on the demise of the tornado. The differential reflectivity column is studied to determine if the updraft was weakening. The relative position of the tornado and mesocyclone also are examined as the tornado ascended the terrain and dissipated to determine whether the change in elevation impacted the overall strength of the storm and to evaluate whether the storm was undergoing a traditional occlusion cycle. Finally, a large-eddy simulation model is used to explore physical changes in a tornado encountering terrain similar to the Mountainburg, Arkansas, tornado near its demise.