Tornado Track Research Articles

Tornado damage in forest ecosystems of the United States

Changing climate dynamics have been forecasted worldwide, including in some regions of the United States (U.S.), leading to potentially significant damage to forest ecosystems. To better understand how natural disturbances may affect forests, we analyzed trends in frequency and area of tornado damage for 1995–2021, damage area within forested ecosystems, and assessed data limitations for the contiguous U.S. for 1950–2021. We found that recent, more uniform data collection periods from 1995-present showed stable to declining numbers of tornadoes nationally. However, our results indicate that the southeastern U.S. has experienced significant increases in total and forest area damaged by tornadoes as compared to other regions over the last three decades. The northeastern U.S. also showed a slight increase in damage, while all other regions showed no significant trends. This indicates a potential shift in the geographic location of tornadic events from the Midwest towards the Southeast, which has a larger area of commercial timberland. Additionally, forest area damage has increased 36 %, as estimated by total tornado track area, over the most recent 20 years. Increased tornado damage in forests suggests increased financial risks for the wood products sector and landowners in recent decades, and we recommend that large, institutional landowners monitor and assess natural disturbances (occurrence, extent, severity, and financial consequences). These findings highlight the need to improve risk assessment methodologies and support research that informs both pre- and post-disturbance forest management practices.

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.

A study of acoustic array signal processing applied to tornado tracking

In low signal-to-noise ratio (SNR) applications it is often necessary to integrate array measurement data over an extended temporal period to achieve satisfactory estimates of source bearings-of-arrival (BOA), especially in the case of multiple sources. For example, in popular frequency-domain methods such as MUSIC and its variants, the number of so-called snapshots (temporal windows) can be increased to improve performance. However, this improvement is achieved under the assumption that the bearing-of-arrival is not changing during the integration period. In this presentation, a study of the estimation performance of two algorithms (a MUSIC variant and a maximum-likelihood based method) is performed as a function of the number of snapshots, and the result of application of these algorithms to infrasound data recorded during the presence of tornadic storms (moving sources) is presented. Additionally, a method for integration over time using a moving source model is introduced.

Natural Canopy Disturbance Patterns and Ecological Silviculture in the Alabama Fall Line Hills

Abstract As a natural disturbance-based approach to silviculture is increasingly embraced by forest managers, quantitative reference conditions of natural disturbance patterns are paramount. We used LiDAR data to quantify resultant structural patterns from an EF3 tornado on the Oakmulgee Ranger District of Talladega National Forest in the Alabama Fall Line Hills, USA. We found the zone of catastrophic disturbance totaled 123 ha, had a mean width of 360 m, and residual trees were randomly distributed. This zone was buffered by an edge zone of intermediate-severity disturbance where trees were removed individually and in groups. The edge zone averaged 220 m in width. In total, the study area included seventy-nine stands and the percentage of stand area damaged ranged from < 1% to 94%. We suggest that clearcut, seed tree, and shelterwood with reserves regeneration methods may be appropriate analogs to natural canopy disturbance in the Fall Line Hills region. These catastrophic disturbance entries should be buffered from undisturbed neighborhoods by a system that retains mature forest structures and would constitute a zone of intermediate-severity disturbance. Our approach may be used as a template to expand our understanding of natural canopy disturbance patterns in other regions and forest types. Study Implications: In regions where tornadoes are a natural component of the disturbance regime, including much of the southeastern United States, we suggest that clearcut, seed tree, or shelterwood with reserves regeneration methods may be used to approximate the zone of catastrophic disturbance that corresponds to the tornado track. We observed that reserve tree spatial patterns were random, but residual trees could be selected based on desired species or protection of important biophysical features such as seeps. Surrounding the catastrophic disturbance zone was an edge zone of intermediate-severity disturbance where trees were removed individually and in variably sized groups. We suggest that the structural patterns documented in this edge zone may be created through variable retention harvesting.

Soil Moisture Observations From Shortwave Infrared Channels Reveal Tornado Tracks: A Case in 10–11 December 2021 Tornado Outbreak

AbstractSatellite‐based post‐tornado assessments have been widely used for the detection of tornado tracks, which heavily relies on the identification of vegetation changes through observations at visible and near‐infrared channels. During the deadly 10–11 December 2021 tornado outbreak, a series of violent tornadoes first touched down over northeastern Arkansas, an area dominated by cropland with rare vegetation coverage in winter. Through the examination of Moderate Resolution Imaging Spectroradiometer multi‐spectral observations, this study reveals significant scars on shortwave infrared channels over this region, but none are captured by visible and near‐infrared channels. The dominant soil type is aquert (one of vertisols), whose high clay content well preserves the severe changes in soil structure during the tornado passage, when the topmost soil layer was removed and underlying soil with higher moisture content was exposed to the air. This study suggests a quick post‐tornado assessment method over less vegetated area by using shortwave infrared channels.

A Vortex Relocation Method for Improving Initial Conditions and Subsequent Predictions of Tornadoes

Abstract A time–space shift method is developed for relocating model-predicted tornado vortices to radar-observed locations to improve the model initial conditions and subsequent predictions of tornadoes. The method consists of the following three steps. (i) Use the vortex center location estimated from radar observations to sample the best ensemble member from tornado-resolving ensemble predictions. Here, the best member is defined in terms of the predicted vortex center track that has a closest point, say at the time of t = t*, to the estimated vortex center at the initial time t0 (when the tornado vortex signature is first detected in radar observations). (ii) Create a time-shifted field from the best ensemble member in which the field within a circular area of about 10-km radius around the vortex center is taken from t = t*, while the field outside this circular area is transformed smoothly via temporal interpolation to the best ensemble member at t0. (iii) Create a time–space-shifted field in which the above time-shifted circular area is further shifted horizontally to co-center with the estimated vortex center at t0, while the field outside this circular area is transformed smoothly via spatial interpolation to the non-shifted field at t0 from the best ensemble member. The method is applied to the 20 May 2013 Oklahoma Newcastle–Moore tornado case, and is shown to be very effective in improving the tornado track and intensity predictions. Significance Statement The time–space shift method developed in this paper can smoothly relocate tornado vortices in model-predicted fields to match radar-observed locations. The method is found to be very effective in improving not only model initial condition but also the subsequent tornado track and intensity predictions. The method is also not sensitive to small errors in radar-estimated vortex center location at the initial time. The method should be useful for future real-time or even operational applications although further tests and improvements are needed (and are planned).

Monte Carlo modeling of tornado hazard to wind turbines in Germany.

Using a Monte Carlo hazard modeling approach, this study assesses the damage and losses to wind turbines caused by tornadoes over Germany. The model combines observed climatology of tornadoes, exposure map of wind turbines and their capacities, vulnerability curve (first developed in this study), and costs of wind turbines to estimate the likelihood of financial losses of different magnitudes. The losses are presented in terms of aggregated and highest annual losses as a function of the return period. Deterministic modeling of the vulnerability function produced larger variability of the most severe losses (rare, but high-intensity events) compared to the pseudorandom sampling of the damage from the vulnerability curve. Doubling the number of current wind turbines results in smaller expected losses than installing fewer but more powerful units (repowerment). Tornadoes are most frequent in the summer months when the capacity factors of wind farms are the lowest and the electricity consumption is also lower than in the winter months. By repeatedly placing a tornado track of a fixed size over the same wind farm, we further investigated the sensitivity of our model to different parameters. The probability of tornadoes of different intensities hitting a wind turbine over Germany is also calculated.

The Hunt for Missing Tornadoes: Using Satellite Imagery to Detect and Document Historical Tornado Damage in Canadian Forests

Abstract Historical tornado events from 1982 to 2020 were documented within Canada’s forested regions using high-resolution satellite imagery. Tornado forest disturbances were identified using a three-step process: 1) detecting, 2) assessing, and 3) dating each event. A grid of 120 km × 120 km boxes was created covering Canada (excluding the extreme north). Of the 484 boxes, 367 were manually searched. Once a long, narrow region of tree damage was detected, it was first cross-referenced with known tornado databases to ensure it was a unique event. Once events were classified as either tornadic or downburst, the coordinates of the start, worst damage, and end locations were documented, as well as the direction of motion, damage indicators, degree of damage, estimated maximum wind speed, and F/EF-scale rating. In total, 231 previously unknown tornadoes were identified. In Ontario, 103 events were discovered, followed by 98 in Quebec, 9 in Manitoba, 6 in Saskatchewan, 9 in Alberta, 5 in British Columbia, and 1 in New Brunswick. The largest number of discovered tornadoes occurred in 2015, and the largest number of strong F2 tornadoes occurred in 2005. Most of the discovered tornadoes occurred in July for both F/EF1 and F/EF2 ratings. Most tornado tracks had widths between 200 and 400 m, and more than 50% of the tornadoes had a pathlength of less than 10 km. Of all the events that were discovered, 125 events could be fully dated, 19 were dated only by month, 41 were dated only by year, and 46 remained undated.

Tornado Radar Images and Path Directions: An Assessment of Public Knowledge in the Southeastern United States

Abstract Due to the current use and reliance on tornado warning polygons, several published articles have concentrated on themes related to risk perception and interpretation of risk within and outside of polygons. Despite the general success of warning polygons, not everybody is able to spatially estimate their risk by looking at maps with tornado warning polygons. Using polygons in conjunction with radar images can improve comprehension and better inform protective action decision-making for tornado warnings. Additionally, a potential latent area of research is how past tornado tracks and climatological knowledge about tornado path directions may influence tornado risk perception and protective action decision-making. In this study, we surveyed 1,023 individuals across the southeastern United States. Participants were asked to rate their level of concern for a tornadic supercell moving toward two locations. They were also asked to name the direction tornadoes usually come from and travel toward in their counties. Results indicated significantly more concern about the radar reflectivity within the supercell than concern about the location of the hook echo. Additionally, the perceived directions of tornado paths across the region were inaccurate with 75% of the sample either not answering, indicating that they did not know the most common direction for tornado paths, or answering that tornadoes travel in uncommon or unrealistic path directions. The Atlanta metropolitan area was used as a case study to illustrate inaccurate perceptions of path directions.

Tornado Radar Images and Path Directions: An Assessment of Public Knowledge in the Southeastern United States

Abstract Due to the current use and reliance on tornado warning polygons, several published articles have concentrated on themes related to risk perception and interpretation of risk within and outside of polygons. Despite the general success of warning polygons, not everybody is able to spatially estimate their risk by looking at maps with tornado warning polygons. Using polygons in conjunction with radar images can improve comprehension and better inform protective action decision-making for tornado warnings. Additionally, a potential latent area of research is how past tornado tracks and climatological knowledge about tornado path directions may influence tornado risk perception and protective action decision-making. In this study, we surveyed 1023 individuals across the southeastern United States. Participants were asked to rate their level of concern for a tornadic supercell moving toward two locations. They were also asked to name the direction tornadoes usually come from and travel toward in their counties. Results indicated significantly more concern about the radar reflectivity within the supercell than concern about the location of the hook echo. Additionally, the perceived directions of tornado paths across the region were inaccurate with 75 percent of the sample either not answering, indicating that they did not know the most common direction for tornado paths, or answering that tornadoes travel in uncommon or unrealistic path directions. The Atlanta metropolitan area was used as a case study to illustrate inaccurate perceptions of path directions.

High Resolution Real-data WRF Modeling and Verification of Tropical Cyclone Tornadoes Associated with Hurricane Ivan 2004

This study used high-resolution real-data WRF simulations of Hurricane Ivan (2004) to document the structure of potentially tornadic supercells embedded in tropical cyclone (TC) rainbands. The simulated TC track and intensity matched well with observations post landfall, and the simulated TC structure closely replicated the observed shape and rainbands, as evident by an assessment of observed composite and simulated radar reflectivity. TC tornado surrogates (TCT-Ss) were identified and calibrated using thresholds based on percentile values of maximum updraft helicity (UHmax) and simulated radar reflectivity. Although the magnitude of UHmax generally decreased as the simulated TC moved inland, sensitivity testing revealed that a threshold based on the 99.95% percentile value of UHmax achieved optimal TCT-S coverage and agreement with observed TC tornado (TCT) tracks on domains with 3-km and 1-km grid spacings. Three cells were identified at three different stages of the TC inland evolution and were found to have hook appendages resembling supercells found in midlatitudes. The cells produced storm tops of 13 km, with rotating cores with depths of 4 km above ground level. Although results in this study are unique to Hurricane Ivan, the authors believe the use of convection-allowing models and UH-based surrogate methodologies can be applied to other TC cases.

Investigating the Impacts of Assimilating Surface Observations on High-Resolution Forecasts of the 15 May 2013 Tornado Event

In this study, the Advanced Regional Prediction System (ARPS), and its associated three-dimensional variational analysis (3DVAR) package are used to simulate a tornadic supercell at 400-m grid spacing. This storm produced an EF3 tornado in Johnson County, TX during the evening of 15 March 2013. Data from Doppler radar, satellite, aircraft, radiosondes, profilers, and surface observations are assimilated in this work. We show that the assimilation of non-conventional surface observations from three networks: the Citizen Weather Observer Program (CWOP), Global Science and Technology (GST) and Automated Weather Stations (AWS) operated by EarthNetworks, in and around the storm inflow, were fundamentally important to the development of an intense low-level mesocyclone. Simulations that did not incorporate this non-conventional data either developed a weak mesocyclone that was displaced to the east of the actual tornado track, or were unable to develop a defined mesocyclone in the first place. In particular, the assimilation of thermodynamic variables (temperature, pressure and moisture) from a subset of AWS observations near the storm’s inflow environment leads to the most accurate simulation of the low-level mesocyclone.

High-resolution observations of microscale influences on a tornado track using Unpiloted Aerial Systems (UAS)

AbstractTopography can have a significant influence on tornado intensity and direction by altering the near-surface inflow. However, past research involving topographic influence on tornadoes has shown significant variety in investigative approaches and conclusions. This study uses Unpiloted Aerial Systems (UAS)-based high-resolution imagery, UAS-based 3D-modeling products, and correlation analyses to examine topographical influences on a portion of the 01 May 2018 Tescott, Kansas EF3 tornado. Two new metrics, Visible Difference Vegetative Index (VDVI) gap and (VDVI) aspect ratio, are introduced to quantify damage severity using UAS-based imagery and elevation information retrieved from a UAS-based digital surface model (DSM). Areas of enhanced scour are seen along the track in areas of local elevation maxima. Correlation analysis shows that damage severity, as measured by VDVI gap and VDVI aspect ratio, are both well correlated with increasing elevation. VDVI gap is only weakly correlated with slope, while VDVI aspect ratio is not correlated with slope. These findings are statistically significant at p < 0.05. As the tornado weakened in intensity, the path became non-linear, traversing between two local elevation maxima. It is hypothesized that fast-moving intense flow formed and weakened as elevation increased over the short spatial distance. This research shows topography and surface conditions are two of many important variables that should be considered when performing tornado-damage site investigations. It also illustrates the importance of UASs in detailed tornado analysis. VDVI gap and VDVI aspect ratio can provide insight into damage severity as a function of topography.

How Does the Relationship between Ambient Deep-Tropospheric Vertical Wind Shear and Tropical Cyclone Tornadoes Change between Coastal and Inland Environments?

AbstractThis work investigates how the relationship between tropical cyclone (TC) tornadoes and ambient (i.e., synoptic-scale) deep-tropospheric (i.e., 850–200-hPa) vertical wind shear (VWS) varies between coastal and inland environments. Observed U.S. TC tornado track data are used to study tornado frequency and location, while dropsonde and radiosonde data are used to analyze convective-scale environments. To study the variability in the TC tornado–VWS relationship, these data are categorized by both 1) their distance from the coast and 2) reanalysis-derived VWS magnitude. The analysis shows that TCs produce coastal tornadoes regardless of VWS magnitude primarily in their downshear sector, with tornadoes most frequently occurring in strongly sheared cases. Inland tornadoes, including the most damaging cases, primarily occur in strongly sheared TCs within the outer radii of the downshear-right quadrant. Consistent with these patterns, dropsondes and coastal radiosondes show that the downshear-right quadrant of strongly sheared TCs has the most favorable combination of enhanced lower-tropospheric near-surface speed shear and veering, and reduced lower-tropospheric thermodynamic stability for tornadic supercells. Despite the weaker intensity farther inland, these kinematic conditions are even more favorable in inland environments within the downshear-right quadrant of strongly sheared TCs, due to the strengthened veering of the ambient winds and the lack of changes in the TC outer tangential wind field strength. The constructive superposition of the ambient and TC winds may be particularly important to inland tornado occurrence. Together, these results will allow forecasters to anticipate how the frequency and location of tornadoes and, more broadly, convection may change as TCs move inland.

Estimating Wind Damage in Forested Areas Due to Tornadoes

Research Highlights: Simulations of treefall patterns during tornado events have been conducted, enabling the coupled effects of tornado characteristics, tree properties and soil conditions to be assessed for the first time. Background and Objectives: Treefall patterns and forest damage assessed in post-storm surveys are dependent on the interaction between topography, biology and meteorology, which makes identification of characteristic behavior challenging. Much of our knowledge of tree damage during extreme winds is based on synoptic storms. Better characterization of tree damage will provide more knowledge of tornado impacts on forests, as well as their ecological significance. Materials and Methods: a numerical method based on a Rankine vortex model coupled with two mechanistic tree models for critical wind velocity for stem break and windthrow was used to simulate tornadic tree damage. To calibrate the models, a treefall analysis of the Alonsa tornado was used. Parametric study was conducted to assess induced tornadic tree failure patterns for uprooting on saturated and unsaturated soils and stem break with different knot factors. Results: A power law relationship between failure bending moments and diameter at breast height (DBH) for the hardwood species provided the best correlation. Observed failure distributions of stem break and windthrow along the tornado track were fitted to lognormal distributions and the mean of the critical wind speeds for windthrow were found to be higher than that for stem break. Relationships between critical wind speed and tree size were negatively correlated for windthrow and positively correlated for stem break. Higher soil moisture contents and lower knot factors reduced the critical wind speeds. The simulations show varying tree fall patterns displaying forward and backward convergence, different tornado damage widths and asymmetry of the tracks. These variations were controlled by the relative magnitudes of radial and tangential tornado velocities, the ratio between translational speed and maximum rotational wind speed and the mode of failure of the trees. Conclusions: The results show the complexity of predicting tornadic damage in forests, and it is anticipated that this type of simulation will aid risk assessments for insurance companies, emergency managers and forest authorities.

Prediction of Tornado-Like Vortex (TLV) Embedded in the 8 May 2003 Oklahoma City Tornadic Supercell Initialized from the Subkilometer Grid Spacing Analysis Produced by the Dual-Resolution GSI-Based EnVar Data Assimilation System

Abstract Explicit forecasts of a tornado-like vortex (TLV) require subkilometer grid spacing because of their small size. Most previous TLV prediction studies started from interpolated kilometer grid spacing initial conditions (ICs) rather than subkilometer grid spacing ICs. The tornadoes embedded in the 8 May 2003 Oklahoma City tornadic supercell are used to understand the impact of IC resolution on TLV predictions. Two ICs at 500-m and 2-km grid spacings are, respectively, produced through an efficient dual-resolution (DR) and a single-coarse-resolution (SCR) EnVar ingesting a 2-km ensemble. Both experiments launch 1-h forecasts at 500-m grid spacing. Diagnostics of data assimilation (DA) cycling reveal DR produces stronger and broader rear-flank cold pools, more intense downdrafts and updrafts with finer scales, and more hydrometeors at high altitudes through accumulated differences between two DA algorithms. Relative differences in DR, compared to SCR, include the integration from higher-resolution analyses, the update for higher-resolution backgrounds, and the propagation of ensemble perturbations along higher-resolution model trajectory. Predictions for storm morphology and cold pools are more realistic in DR than in SCR. The DR-TLV tracks match better with the observed tornado tracks than SCR-TLV in timing of intensity variation, and in duration. Additional experiments suggest 1) the analyzed kinematic variables strongly influence timing of intensity variation through affecting both low-level rear-flank outflow and midlevel updraft; 2) potential temperature analysis by DR extends the second track’s duration consistent with enhanced low-level stretching, delayed broadening large-scale downdraft, and (or) increased near-surface baroclinic vorticity supply; and 3) hydrometeor analyses have little impact on TLV predictions.

Severe Weather Forecasts and Public Perceptions: An Analysis of the 2011 Super Outbreak in Tuscaloosa, Alabama

AbstractThis paper uses the “Super Outbreak” of 2011 as a case study to examine the potential gaps between the dissemination of severe weather warnings and the public’s behavioral response to this information. This study focuses on a single tornado track that passed through Tuscaloosa, Alabama. The tornado caused massive damage and destruction and led to a total of 62 fatalities. The threat of severe storms was known days in advance, and forecasts were disseminated to the public. Questions were raised about the forecasts, warning lead times, and the perception of the warnings among residents. This paper examines the potential gaps that exist between the dissemination of tornadic warning information and citizen response. The analysis of data collected through a mixed-method approach suggests that, regardless of weather forecast accuracy, a significant chasm exists between the dissemination of warnings and the personalizing of risks, which results in limited use of protective measures in the face of severe weather threats.

The lodging of crops by tornadoes

It is well known that tornadoes passing over fields can cause significant damage to crops, and tornado tracks of fallen, or lodged, crops can extend for many hundreds of metres. An examination of photographic evidence of such events suggests that, at least for low speed EF0/EF1 events, lodging occurs beneath tornadoes primarily due to a strong radial flow (rather than circumferential flow) at the canopy surface. In order to investigate this effect further, a simple model of a tornado has been developed which, whilst fully satisfying the three dimensional Euler equations, models a circumferential flow at the edge of the tornado boundary layer near the ground, which becomes a radial flow as the ground is approached. This model is then used in a generalised model of lodging to predict lodging track widths and crop fall directions. It is shown that, when expressed in a suitably normalised form, both lodging width and crop fall direction are functions of a normalised translational velocity and a normalised crop lodging velocity. The lodging patterns are of two forms – a forward convergence (FC) where the cropfall converges on the tornado track in a forward direction, and a backward convergence (BC) where the convergence is in the opposite direction to tornado translations. Regions of FC and BC in the normalised parameter plane are calculated. These patterns are very similar to those observed in the field, which gives some confidence in the nature of the model. The model is then used to investigate the sensitivity of lodging width to crop and tornado parameters, and also to carry out a risk analysis to determine the probability distributions of lodging width for specified distributions of crop and tornado parameters.

Hindcasting the First Tornado Forecast in Europe: 25 June 1967

Abstract The tornado outbreak of 24–25 June 1967 was the most damaging in the history of western Europe, producing 7 F2–F5 tornadoes, 232 injuries, and 15 fatalities across France, Belgium, and the Netherlands. Following tornadoes in France on 24 June, the Royal Netherlands Meteorological Institute (KNMI) issued a tornado forecast for 25 June, which became the first ever—and first verified—tornado forecast in Europe. Fifty-two years later, tornadoes are still not usually forecast by most European national meteorological services, and a pan-European counterpart to the NOAA/NWS/Storm Prediction Center (SPC) does not exist to provide convective outlook guidance; yet, tornadoes remain an extant threat. This article asks, “What would a modern-day forecast of the 24–25 June 1967 outbreak look like?” To answer this question, a model simulation of the event is used in three ways: 20-km grid-spacing output to produce a SPC-style convective outlook provided by the European Storm Forecast Experiment (ESTOFEX), 800-m grid-spacing output to analyze simulated reflectivity and surface winds in a nowcasting analog, and 800-m grid-spacing output to produce storm-total footprints of updraft helicity maxima to compare to observed tornado tracks. The model simulates a large supercell on 24 June and weaker embedded mesocyclones on 25 June forming along a stationary front, allowing the ESTOFEX outlooks to correctly identify the threat. Updraft helicity footprints indicate multiple mesocyclones on both days within 40–50 km and 3–4 h of observed tornado tracks, demonstrating the ability to hindcast a large European tornado outbreak.

Tornado loss model of Oklahoma and Kansas, United States, based on the historical tornado data and Monte Carlo simulation

This paper introduces a new tornado loss assessment model for one-family and two-family wood frame residential buildings in the states of Kansas (KS) and Oklahoma (OK), United States. The model belongs to the group of Monte Carlo simulations and consists of five main modules: (1) tornado climatology; (2) tornado intensity and tornado track geometry; (3) exposure map; (4) fragility and vulnerability functions; and (5) financial module that is used to calculate the resulting damages. In addition, two analytical formulations of radial distribution of tangential velocity in tornado vortex were implemented in the tornado intensity module—Rankine Vortex (RV) and Modified Rankine Vortex (MRV) models. Using the MRV tornado model, the resulting mean aggregated annual expected losses for the return period of 1000 years are US$ 392,080,526.8 and US$ 641,281,336.0 for KS and OK, respectively. Although house prices in KS are higher than in OK, the higher occurrence of tornadoes in OK combined with the larger population of OK (i.e., larger density of houses) result in the increased tornado losses for OK. The losses derived using the MRV module are higher than the losses from the RV module due to the RV module underestimating the tornado tangential velocity. Furthermore, the losses predicted by the current model using the MRV formulation are less than the estimated losses from the previous version of the model (Romanic et al., 2016). The model is also tested for the case of a single tornado track positioned over suburban area of Oklahoma City, OK.