Tornado Occurrence Research Articles

Geographic scale and probabilistic forecasts: a trade-off for protective decisions?

This pilot study aimed to examine the impact of varying geographic scales, probabilities of tornado occurrence, and presentation formats within severe weather forecasts on individuals’ protective decisions. This pilot was unique in its specific examination of the trade-off between highly localized geography and higher valued probabilistic threat information in weather-related decision making. This pilot utilized a 4 (geographic scale) × 12 (probability) × 3 (forecast presentation format) mixed, nested experimental design. Participants were 440 US adults who completed electronic questionnaires containing experimentally manipulated severe weather forecasts. A linear mixed model analysis revealed several findings. First, participants who saw only categorical forecasts made similar preparatory decisions across geographic scales. Additionally, they were more willing to take preparatory action as categorical risk increased. Second, when probabilities were presented, the propensity to take protective action was greater at higher probabilities and at larger geographic scales, affirming the regional geographic reference class selected by the Storm Prediction Center in today’s outlook system. Third, individuals’ propensity for action generally increased as scale and probability increased, but the pattern varied across presentation formats. Lastly, participants reported having a map to look at was moderately important to their decisions and having probabilistic and categorical risk information was highly important to their decisions. Taken together, the findings suggest a complex relationship between geographic scale and probability, which is further complicated by forecast presentation format.

Tornadoes in Romania—from Forecasting and Warning to Understanding Public’s Response and Expectations

Significant progress in tornado research and management can be claimed over the last few decades worldwide. However, tornado forecasting and warning continue to be permanent challenges for most European national meteorological services because they require particular skills and experience. Moreover, tornado warnings may generate panic. Therefore, one can remark that the main difficulties are related to (1) forecasting the tornado genesis, and (2) finding the most efficient way to communicate to the general public the possibility of tornado occurrence. This article presents the main characteristics of two convective events that occurred in Romania in order to emphasize the similarities and disparities between the tornado event (30 April 2019) and the non-tornado event (6 May 2019), from the warning perspective. Further, we investigate, for the first time in Romania, the general public’s comprehension, risk perception and reactions regarding the tornado events. The survey performed in 2020 emphasized that the Romanian public is able to recognize tornadoes (60%), understand the risks (over 80%), can manage the panic (over 70%), and is rather desirous to receive clear (over 90%) and real-time (95%) tornado warnings. The lessons learned may support the further development of tornado forecasting and warning procedures, and foster the public’s awareness related to tornado events.

Tornado Risk Climatology in Europe

Violent tornadoes are rare in Europe but they can have devastating effects. Damage associated with individual tornadoes can reach several billion euros and they have caused hundreds of fatalities. The tornado risk varies considerably over Europe, but so far only a few national maps of tornado risk and one Europe-wide map exist. We show several different ways to create quantitative maps of tornado occurrence rates as follows: Kernel smoothing of observations, climatologies of convective parameters from reanalysis, output of a logistic regression model to link convective parameters with observed tornadoes, orography-dependent climatologies and finally the population-bias corrected tornado occurrence rates from the Risk Management Solutions (RMS) Europe Severe Convective Storm Model. We discuss advantages and disadvantages of each approach and compare the results. While the climatologies created from the individual methods show a lot of qualitative similarities, we advocate to combine the methods to achieve the most reliable quantitative climatology.

Improved Severe Weather Forecasts Using LEO and GEO Satellite Soundings

AbstractIt is shown here that improvements in numerical weather prediction (NWP) model forecasts of hazardous weather can be obtained by assimilating profile retrievals obtained in real time from combined direct broadcast system (DBS) polar satellite hyperspectral and geostationary satellite multispectral radiance data. Results of NWP model forecasts are shown for two recent tornado outbreak cases: 1) the 3 March 2019 tornado outbreak over the southeast United States and 2) the tornado outbreak that occurred across Illinois, Indiana, and Ohio during the night of 27 May and the morning of 28 May 2019, and 3) the 4 March 2019 severe precipitation event that occurred in southeast China. Improvements in both quantitative precipitation forecasts (QPFs) and predictions of the location of tornado occurrence are obtained. It is also shown that geostationary satellite hyperspectral soundings [i.e.,Fengyun-4A(FY-4A) Geosynchronous Interferometric Infrared Sounder (GIIRS)] further improve hazardous precipitation forecasts when used, in addition to the combined polar hyperspectral and geostationary multispectral satellite profile data, to initialize the numerical forecast model. The lowest false alarm rate (FAR) and the highest probability of detection (POD) and critical success index (CSI) scores are achieved when assimilating atmospheric profile retrievals obtained by combining all the available satellite high-vertical-resolution hyperspectral radiance measurements with geostationary satellite high-spatial-resolution and high-temporal-resolution multispectral radiance measurements.

Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction

Abstract This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites. We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Both models achieve an area under the receiver-operating-characteristic curve (AUC) well above 0.9, which is considered to be excellent performance. The GridRad model achieves a critical success index (CSI) of 0.31, and the MYRORSS model achieves a CSI of 0.17. The difference is due primarily to event frequency (percentage of storms that are tornadic in the next hour), which is 3.52% for GridRad but only 0.24% for MYRORSS. The best CNN predictions (true positives and negatives) occur for strongly rotating tornadic supercells and weak nontornadic cells in mesoscale convective systems, respectively. The worst predictions (false positives and negatives) occur for strongly rotating nontornadic supercells and tornadic cells in quasi-linear convective systems, respectively. The performance of our CNNs is comparable to an operational machine-learning system for severe weather prediction, which suggests that they would be useful for real-time forecasting.

Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach

In this paper, we analyze the tornado occurrences in the Unites States. To perform inference procedures for the spatio-temporal point process we adopt a dynamic representation of Log-Gaussian Cox Process. This representation is based on the decomposition of intensity function in components of trend, cycles, and spatial effects. In this model, spatial effects are also represented by a dynamic functional structure, which allows analyzing the possible changes in the spatio-temporal distribution of the occurrence of tornadoes due to possible changes in climate patterns. The model was estimated using Bayesian inference through the Integrated Nested Laplace Approximations. We use data from the Storm Prediction Center’s Severe Weather Database between 1954 and 2018, and the results provided evidence, from new perspectives, that trends in annual tornado occurrences in the United States have remained relatively constant, supporting previously reported findings.

The Extended-Range Tornado Activity Forecast (ERTAF) Project

AbstractLarge-scale weather patterns favorable for tornado occurrence have been understood for many decades. Yet prediction of tornadoes, especially at extended lead periods of more than a few days, remains an arduous task, partly due to the space and time scales involved. Recent research has shown that tropical convection, sea surface temperatures, and the Earth-relative atmospheric angular momentum can induce jet stream configurations that may increase or decrease the probability of tornado frequency across the United States. Applying this recent theoretical work in practice, on 1 March 2015, the authors began the Extended-Range Tornado Activity Forecast (ERTAF) project, with the following goals: 1) to have a map room–style discussion of the anticipated atmospheric state in the 2–3-week lead window; 2) to predict categorical level of tornado activity in that lead window; and 3) to learn from the forecasts through experience by identifying strengths and weaknesses in the methods, as well as identifying any potential scientific knowledge gaps. Over the last five years, the authors have shown skill in predicting U.S. tornado activity two to three weeks in advance during boreal spring. Unsurprisingly, skill is shown to be greater for forecasts spanning week 2 versus week 3. This manuscript documents these forecasting efforts, provides verification statistics, and shares the challenges and lessons learned from predicting tornado activity on the subseasonal time scale.

Hybrid Prediction of Weekly Tornado Activity Out to Week 3: Utilizing Weather Regimes

AbstractTornadoes are one of the high‐impact weather phenomena that can induce life loss and property damage. Here, we investigate the relationship between large‐scale weather regimes and tornado occurrence in boreal spring. Results show that weather regimes strongly modulate the probability of tornado occurrence in the United States due to changes in shear and convective available potential energy and that persisting weather regimes (lasting ≥3 days) contribute to greater than 70% of outbreak days (days with ≥10 tornadoes). A hybrid model based on the weather regime frequency predicted by a numerical model is developed to predict above/below normal weekly tornado activity and has skill better than climatology out to Week 3. The hybrid model can be applied to real‐time forecasting and aide in mitigation of severe weather events.

A Statistical Investigation of Mesoscale Precursors of Significant Tornadoes: The Italian Case Study

In this study, mesoscale environments associated with 57 significant tornadoes occurring over Italy in the period 2000–2018 are analyzed. The role of the vertical Wind Shear in the lower and middle troposphere, in terms of low-level shear (LLS) and deep-level shear (DLS), and of the convective available potential energy (CAPE) as possible precursors of significant tornadoes is statistically investigated. Wind shear and CAPE data are extracted from the ERA-5 and ERA-Interim reanalyses. Overall, the study indicates that: (a) values of these variables in the two uppermost quartiles of their statistical distribution significantly increases the probability of tornado occurrences; (b) the probability increases for increasing values of LLS and DLS, and (c) is maximum when either wind shear or CAPE are large. These conclusions hold for both the reanalysis datasets and do not depend upon the season and/or the considered area. With the possible exception of weak tornadoes, which are not included in our study, our results show that large wind shear, in the presence of medium-to-high values of CAPE, are reliable precursors of tornadoes.

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.

People's thresholds of decision-making against a tornado threat using dynamic probabilistic hazard information

With pervasive use of smartphones for acquiring weather information and efforts of National Oceanic and Atmospheric Administration (NOAA) in developing a tool under the banner of the Forecasting a Continuum of Environmental Threats (FACETs) to disseminate probabilistic information about a tornado event, it is important to investigate people's probabilistic thresholds for taking protective action when presented with dynamic visual information about a tornado threat on a smartphone. We presented dynamic displays of probabilistic information of five hypothetical tornado scenarios to 109 college students on their smartphones and asked them to report at what moment they would take protective action (if any). After conducting Cox proportional hazard regression and Poisson regression, we found that proximity to the tornado, likelihood of the threat occurrence, and being inside vs. outside of the risk area played a significant role in people's decision-making. This study illustrated that regardless of tornado trajectory, as a moving probabilistic swath showing probabilistic forecast of the tornado becomes closer to the information recipients, around 12% of the participants would take protective action prior to being impacted by the probabilistic zone with more than 0% chance of the tornado occurrence. Almost half of them would take protective action before being impacted by the probabilistic zone with more than 20% chance, and around 88% of the participants would take protective action before being impacted by the probabilistic zone with more than 40% chance of the tornado occurrence. Our study corroborates previous relevant research that providing probabilistic hazard information to the public could enhance the warnings' effectiveness.

North American April tornado occurrences linked to global sea surface temperature anomalies.

Annual tornado occurrences over North America display large interannual variability and a statistical linkage to sea surface temperature (SST) anomalies. However, the underlying physical mechanisms for this connection and its modulation in a rapidly varying seasonal environment still remain elusive. Using tornado data over the United States from 1954 to 2016 in combination with SST-forced atmospheric general circulation models, we show a robust dynamical linkage between global SST conditions in April, the emergence of the Pacific-North American teleconnection pattern (PNA), and the year-to-year tornado activity in the Southern Great Plains (SGP) region of the United States. Contrasting previous studies, we find that only in April SST-driven atmospheric circulation anomalies can effectively control the northward moisture-laden flow from the Gulf of Mexico, boosting low-level moisture flux convergence over the SGP. These strong large-scale connections are absent in other months because of the strong seasonality of the PNA and background moisture conditions.

Characteristics of Tornado Events and Warnings in the Southeastern United States

Abstract The southeastern United States has become a prime area of focus in tornado-related literature due, in part, to the abundance of tornadoes occurring in high-shear low-CAPE (HSLC) environments. Through this analysis of 4133 tornado events and 16 429 tornado warnings in the southeastern United States, we find that tornadoes in the Southeast do indeed have, on average, higher shear and lower CAPE than tornadoes elsewhere in the contiguous United States (CONUS). We also examine tornado warning skill in the form of probability of detection (POD; percent of tornadoes receiving warning prior to tornado occurrence) and false alarm ratio (FAR; percent of tornado warnings for which no corresponding tornado is detected), and find that, on average, POD is better and FAR is worse for tornadoes in the Southeast than for the CONUS as a whole. These measures of warning skill remain consistent even when we consider only HSLC tornadoes. The Southeast also has nearly double the CONUS percentage of deadly tornadoes, with the highest percentage of these deadly tornadoes occurring during the spring, the winter, and around local sunset. On average, however, the tornadoes with the lowest POD also tend to be those that are weakest and least likely to be deadly; for the most part, the most dangerous storms are indeed being successfully warned.

Environmental Covariate Representation of Seasonal U.S. Tornado Frequency

AbstractThe significant tornado parameter is a widely used meteorological composite index that combines several variables known to favor tornadic supercell thunderstorms. This research examines the spatial relationship between U.S. tornado frequency and the significant tornado parameter (the predictor covariate) across four seasons in order to establish a spatial–statistical model that explains significant amounts of variance in tornado occurrence (the predictand). U.S. tornadoes are highly dependent on the significant tornado parameter in a climatological sense. The strength of this dependence is seasonal, with greatest dependence found during December–February and least dependence during June–August. Additionally, the strength of this dependence has not changed significantly through the 39-yr study period (1979–2017). Results herein represent an important step forward for the creation of a predictive spatial–statistical model to aid in tornado prediction at seasonal time scales.

Modeling The Number of Flood Occurrence in Indonesia in 2015 Using Poisson Regression Model

The Poisson regression model is one of the regression models that can show the relationship between the Poisson distributed response variable with predictor variables through the regression parameter. In this paper, the number of flood occurrences in Indonesia in 2015 which is the count data using a Poisson regression model. To obtain the Poisson regression model of the number of flood occurrence are conducted using multicollinearity test on predictor variables, estimation of regression parameters, parameter test, and goodness of fit model test. The method used to estimate parameter values is the Maximum Likelihood Estimation method that is obtained from the iteration results using the Newton-Raphson iterative method. Based on the results of data processing, the Poisson regression model that is obtained for the number of flood occurrences shows that the average of the number of flood occurrences in Indonesia is affected by the number of tornado occurrences, the number of rainy days, and the sun exposure.

Stochastic Track Model for Tornado Risk Assessment in the U.S.

This paper presents the development of a stochastic tornado simulation model for the United States (US). The continental of the US is subjected to more than 1,000 tornadoes each year, causing significant financial losses and social disruption. Compared to hurricanes, the damage region of a tornado is relatively small and the probability of occurrence at a given location is extremely low. Therefore, it is not feasible to use solely the observed data or tracks to quantify the tornado risk for a given structure or a city that has not been affected by historical tornadoes. In this paper, a methodology for performing stochastic simulation of tornado tracks for the US is presented. The stochastic simulation framework consists of a genesis model, which utilizes the kernel density estimation to simulate the spawn locations of tornadoes. Statistical models for tornado parameters such as track length, path width and intensity, were calibrated using the tornado database maintained by the US National Oceanic and Atmospheric Administration (NOAA) Storm Prediction Center (SPC). The developed statistical models were used to simulate 1,000,000 years of tornado tracks. The simulated tornado parameters include the tornado occurrence rate, intensity (EF-scale), location, touchdown time, path length and path width. All these parameters are geographic dependent, meaning the parameters vary depending on the tornado spawn locations. The simulated spawn rates and other key parameters for the continental of the US are compared to the observations. Good agreements are observed between simulations and observations. To illustrate a potential use of the simulated tornado track database, a probabilistic tornado hazard analysis was performed for Moore, Oklahoma. The 50-year tornado hazard curves for three domain sizes are developed to assess the influence of the domain size on tornado risk.

Effect of Providing the Uncertainty Information About a Tornado Occurrence on the Weather Recipients' Cognition and Protective Action: Probabilistic Hazard Information Versus Deterministic Warnings.

Currently, a binary alarm system is used in the United States to issue deterministic warning polygons in case of tornado events. To enhance the effectiveness of the weather information, a likelihood alarm system, which uses a tool called probabilistic hazard information (PHI), is being developed at National Severe Storms Laboratory to issue probabilistic information about the threat. This study aims to investigate the effects of providing the uncertainty information about a tornado occurrence through the PHI's graphical swath on laypeople's concern, fear, and protective action, as compared with providing the warning information with the deterministic polygon. The displays of color-coded swaths and deterministic polygons were shown to subjects. Some displays had a blue background denoting the probability of any tornado formation in the general area. Participants were asked to report their levels of concern, fear, and protective action at randomly chosen locations within each of seven designated levels on each display. Analysis of a three-stage nested design showed that providing the uncertainty information via the PHI would appropriately increase recipients' levels of concern, fear, and protective action in highly dangerous scenarios, with a more than 60% chance of being affected by the threat, as compared with deterministic polygons. The blue background and the color-coding type did not have a significant effect on the people's cognition of the threat and reaction to it. This study shows that using a likelihood alarm system leads to more conscious decision making by the weather information recipients and enhances the system safety.

Occurrence of Anticyclonic Tornadoes in a Topographically Complex Region of Mexico

Tornadoes are violent and destructive natural phenomena that occur on a local scale in most regions around the world. Severe storms occasionally lead to the formation of mesocyclones, whose direction or sense of rotation is often determined by the Coriolis force, among other factors. In the Northern Hemisphere, more than 99% of all tornadoes rotate anticlockwise. The present research shows that, in topographically complex regions, tornadoes have a different probability of rotating clockwise or anticlockwise. Our ongoing research programme on tornadoes in Mexico has shown that the number of tornadoes is significantly higher than previously thought. About 40% of all tornadoes occur in the complex topographic region of the Trans-Mexican Volcanic Belt. Data collected (from Internet videos) on the rotation of tornadoes formed in this region showed that about 50% of them rotated in a clockwise direction, contradicting tornado statistics for most of North America. Time series of the helicity parameter showed that tornadoes formed in topographically complex areas exhibited different behaviours compared to those formed in plains that are related with supercell systems.

Land surface heterogeneity and tornado occurrence: an analysis of Tornado Alley and Dixie Alley

Tornadoes rank among the most destructive hazards to human life and property. In the United States, two regions known as Tornado Alley and Dixie Alley are particularly prone to tornadoes, but determining exactly where tornadoes will occur remains difficult. Examining the relationship between land surface heterogeneity (i.e. variations in topography, vegetation, and land cover) and tornado occurrence can provide insight into the spatial distribution of these storms. The objective of this study is to uncover the relationships between severe storm occurrence and land surface heterogeneity variables to determine (1) whether certain land heterogeneity variables exhibit a significant relationship with tornado occurrence, and (2) whether those relationships vary by region and storm strength. We delineate the spatial extent of Tornado Alley and Dixie Alley based on clusters of tornadic activity. We then use logistic regression to investigate how land heterogeneity factors are related to tornado formation of weakly and significantly tornadic storms. Using these relationships uncovered, we map tornado probability across the two regions. Regression results show that several land surface heterogeneity variables have a statistically significant relationship with tornado occurrence, but these relationships vary with region and storm severity, supporting the need for more local studies.

Conditions of occurrence and short-term forecast of strong squalls and tornadoes in European Russia

Conditions of occurrence and short-term forecast of strong squalls and tornadoes in European Russia