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Abstract
A dataset of killer tornadoes is compiled and analyzed spatially in order to assess region-specific vulnerabilities in the United States from 1880 to 2005. Results reveal that most tornado fatalities occur in the lower–Arkansas, Tennessee, and lower–Mississippi River valleys of the southeastern United States—a region outside of traditional “tornado alley.” Analysis of variables including tornado frequency, land cover, mobile home density, population density, and nocturnal tornado probabilities demonstrates that the relative maximum of fatalities in the Deep South and minimum in the Great Plains may be due to the unique juxtaposition of both physical and social vulnerabilities. The spatial distribution of these killer tornadoes suggests that the above the national average mobile home density in the Southeast may be a key reason for the fatality maximum found in this area. A demographic analysis of fatalities during the latter part of the database record illustrates that the middle aged and elderly are at a much greater risk than are younger people during these events. Data issues discovered during this investigation reveal the need for a concerted effort to obtain critical information about how and where all casualties occur during future tornado and hazardous weather events. These new, enhanced data, combined with results of spatially explicit studies exploring the human sociology and psychology of these hazardous events, could be utilized to improve future warning dissemination and mitigation techniques.
Abstract
A dataset of killer tornadoes is compiled and analyzed spatially in order to assess region-specific vulnerabilities in the United States from 1880 to 2005. Results reveal that most tornado fatalities occur in the lower–Arkansas, Tennessee, and lower–Mississippi River valleys of the southeastern United States—a region outside of traditional “tornado alley.” Analysis of variables including tornado frequency, land cover, mobile home density, population density, and nocturnal tornado probabilities demonstrates that the relative maximum of fatalities in the Deep South and minimum in the Great Plains may be due to the unique juxtaposition of both physical and social vulnerabilities. The spatial distribution of these killer tornadoes suggests that the above the national average mobile home density in the Southeast may be a key reason for the fatality maximum found in this area. A demographic analysis of fatalities during the latter part of the database record illustrates that the middle aged and elderly are at a much greater risk than are younger people during these events. Data issues discovered during this investigation reveal the need for a concerted effort to obtain critical information about how and where all casualties occur during future tornado and hazardous weather events. These new, enhanced data, combined with results of spatially explicit studies exploring the human sociology and psychology of these hazardous events, could be utilized to improve future warning dissemination and mitigation techniques.
Abstract
This study compiles a nationwide database of flood fatalities for the contiguous United States from 1959 to 2005. Assembled data include the location of fatalities, age and gender of victims, activity and/or setting of fatalities, and the type of flood events responsible for each fatality report. Because of uncertainties in the number of flood deaths in Louisiana from Hurricane Katrina, these data are not included in the study. Analysis of these data reveals that a majority of fatalities are caused by flash floods. People between the ages of 10 and 29 and >60 yr of age are found to be more vulnerable to floods. Findings reveal that human behavior contributes to flood fatality occurrences. These results also suggest that future structural modifications of flood control designs (e.g., culverts and bridges) may not reduce the number of fatalities nationwide. Spatially, flood fatalities are distributed across the United States, with high-fatality regions observed along the northeast Interstate-95 corridor, the Ohio River valley, and near the Balcones Escarpment in south-central Texas. The unique distributions found are likely driven by both physical vulnerabilities for flooding as well as the social vulnerabilities.
Abstract
This study compiles a nationwide database of flood fatalities for the contiguous United States from 1959 to 2005. Assembled data include the location of fatalities, age and gender of victims, activity and/or setting of fatalities, and the type of flood events responsible for each fatality report. Because of uncertainties in the number of flood deaths in Louisiana from Hurricane Katrina, these data are not included in the study. Analysis of these data reveals that a majority of fatalities are caused by flash floods. People between the ages of 10 and 29 and >60 yr of age are found to be more vulnerable to floods. Findings reveal that human behavior contributes to flood fatality occurrences. These results also suggest that future structural modifications of flood control designs (e.g., culverts and bridges) may not reduce the number of fatalities nationwide. Spatially, flood fatalities are distributed across the United States, with high-fatality regions observed along the northeast Interstate-95 corridor, the Ohio River valley, and near the Balcones Escarpment in south-central Texas. The unique distributions found are likely driven by both physical vulnerabilities for flooding as well as the social vulnerabilities.
Abstract
A database of tornado fatalities, nontornadic convective wind fatalities, severe thunderstorm warnings, and tornado warnings was compiled for the period 1986–2007 to assess the spatial and temporal distribution of warned and unwarned fatalities. The time of fatality and location as reported in Storm Data was compared to tornado and severe thunderstorm warnings to determine if a warning was in effect when the fatality occurred. Overall, 23.7% of tornado fatalities were unwarned, while 53.2% of nontornadic convective wind fatalities were unwarned. Most unwarned tornado fatalities occurred prior to the mid-1990s—coinciding with modernization of the National Weather Service—while unwarned nontornadic convective wind fatalities remained at a relatively elevated frequency throughout the study period. Geographic locations with high numbers of unwarned tornado and nontornadic convective wind fatalities were associated with one high-magnitude event that was unwarned rather than a series of smaller unwarned events over the period. There are many factors that contribute to warning response by the public, and the issuance of a severe thunderstorm or tornado warning is an important initial step in the warning process. A better understanding of the characteristics of warned and unwarned fatalities is important to future reduction of unwarned fatalities.
Abstract
A database of tornado fatalities, nontornadic convective wind fatalities, severe thunderstorm warnings, and tornado warnings was compiled for the period 1986–2007 to assess the spatial and temporal distribution of warned and unwarned fatalities. The time of fatality and location as reported in Storm Data was compared to tornado and severe thunderstorm warnings to determine if a warning was in effect when the fatality occurred. Overall, 23.7% of tornado fatalities were unwarned, while 53.2% of nontornadic convective wind fatalities were unwarned. Most unwarned tornado fatalities occurred prior to the mid-1990s—coinciding with modernization of the National Weather Service—while unwarned nontornadic convective wind fatalities remained at a relatively elevated frequency throughout the study period. Geographic locations with high numbers of unwarned tornado and nontornadic convective wind fatalities were associated with one high-magnitude event that was unwarned rather than a series of smaller unwarned events over the period. There are many factors that contribute to warning response by the public, and the issuance of a severe thunderstorm or tornado warning is an important initial step in the warning process. A better understanding of the characteristics of warned and unwarned fatalities is important to future reduction of unwarned fatalities.
Abstract
This research evaluates the ability of image-processing and select machine-learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. The process used in this study is composed of two parts: segmentation and classification. Segmentation is performed by identifying contiguous or semicontiguous regions of deep, moist convection that are organized on a horizontal scale of at least 100 km. The second part, classification, is performed by first compiling a database of thousands of precipitation clusters and then subjectively assigning each sample one of the following labels: 1) midlatitude MCS, 2) unorganized convective cluster, 3) tropical system, 4) synoptic system, or 5) ground clutter and/or noise. The attributes of each sample, along with their assigned label, are used to train three machine-learning algorithms: random forest, gradient boosting, and “XGBoost.” Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with a high probability of detection and low probability of false detection. Further, the trained algorithm predictions are well calibrated, allowing reliable probabilistic classification. The utility of this two-step procedure is illustrated by generating spatial frequency maps of automatically identified precipitation clusters that are stratified by using various reflectivity and probabilistic prediction thresholds. These results suggest that machine learning can add value by limiting the amount of false-positive (non-MCS) samples that are not removed by segmentation alone.
Abstract
This research evaluates the ability of image-processing and select machine-learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. The process used in this study is composed of two parts: segmentation and classification. Segmentation is performed by identifying contiguous or semicontiguous regions of deep, moist convection that are organized on a horizontal scale of at least 100 km. The second part, classification, is performed by first compiling a database of thousands of precipitation clusters and then subjectively assigning each sample one of the following labels: 1) midlatitude MCS, 2) unorganized convective cluster, 3) tropical system, 4) synoptic system, or 5) ground clutter and/or noise. The attributes of each sample, along with their assigned label, are used to train three machine-learning algorithms: random forest, gradient boosting, and “XGBoost.” Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with a high probability of detection and low probability of false detection. Further, the trained algorithm predictions are well calibrated, allowing reliable probabilistic classification. The utility of this two-step procedure is illustrated by generating spatial frequency maps of automatically identified precipitation clusters that are stratified by using various reflectivity and probabilistic prediction thresholds. These results suggest that machine learning can add value by limiting the amount of false-positive (non-MCS) samples that are not removed by segmentation alone.
Abstract
This research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains.
Abstract
This research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains.
Abstract
Research has illustrated that tornado disaster potential and impact severity are controlled by hazard risk and underlying physical and social vulnerabilities. Previous vulnerability studies have suggested that an important driver of disaster consequence is the type of housing affected by tornadic winds. This study employs a Monte Carlo tornado simulation tool; mobile home location information derived from finescale, land-parcel data; and census enumerations of socioeconomic vulnerability factors to assess the tornado impact probability for one of the most wind hazard–susceptible demographics in the United States: mobile home residents. Comparative analyses between Alabama and Kansas are employed to highlight regional (i.e., Southeast vs Great Plains) differences in mobile home tornado risk, exposure, and vulnerability. Tornado impact potential on mobile homes is 4.5 times (350%) greater in Alabama than in Kansas because Alabama, in comparison to Kansas, is represented by 1) a greater number of mobile homes and 2) a more sprawling mobile home distribution. Findings reveal that the Southeast’s mobile home residents are one of the most socioeconomically and demographically marginalized populations in the United States and are more susceptible to tornado impact and death than illustrated in prior research. Policy makers, engineers, and members of integrated warning teams (i.e., National Weather Service, media, emergency managers, and first responders) should use these findings to initiate a dialogue and construct interdisciplinary actions aimed at improving societal and individual resilience before, during, and after hazardous weather events.
Abstract
Research has illustrated that tornado disaster potential and impact severity are controlled by hazard risk and underlying physical and social vulnerabilities. Previous vulnerability studies have suggested that an important driver of disaster consequence is the type of housing affected by tornadic winds. This study employs a Monte Carlo tornado simulation tool; mobile home location information derived from finescale, land-parcel data; and census enumerations of socioeconomic vulnerability factors to assess the tornado impact probability for one of the most wind hazard–susceptible demographics in the United States: mobile home residents. Comparative analyses between Alabama and Kansas are employed to highlight regional (i.e., Southeast vs Great Plains) differences in mobile home tornado risk, exposure, and vulnerability. Tornado impact potential on mobile homes is 4.5 times (350%) greater in Alabama than in Kansas because Alabama, in comparison to Kansas, is represented by 1) a greater number of mobile homes and 2) a more sprawling mobile home distribution. Findings reveal that the Southeast’s mobile home residents are one of the most socioeconomically and demographically marginalized populations in the United States and are more susceptible to tornado impact and death than illustrated in prior research. Policy makers, engineers, and members of integrated warning teams (i.e., National Weather Service, media, emergency managers, and first responders) should use these findings to initiate a dialogue and construct interdisciplinary actions aimed at improving societal and individual resilience before, during, and after hazardous weather events.
Abstract
There are still hundreds of casualties produced by thunderstorm hazards each year in the United States despite the many recent advances in prediction and mitigation of the effects of convective storms. Of the four most common thunderstorm hazards (wind, hail, flooding, and lightning), convective winds (tornadic and nontornadic) remain one of the most dangerous threats to life and property. Using thunderstorm fatality and Weather Surveillance Radar-1988 Doppler (WSR-88D) data, this research illustrates a spatial and temporal analysis of the storm morphological characteristics, or convective mode, of all fatal tornadic and nontornadic convective wind events from 1998 to 2007. The investigation employs a radar-based morphology classification system that delineates storm type based on an organizational continuum, including unorganized cellular, quasi-organized cellular (either a cluster of cells or a broken line of cells), organized cellular (supercells and supercells embedded in an organized linear system), and organized linear (either squall lines or bow echoes). Results illustrate that over 90% of the 634 recorded tornado deaths were associated with supercells, with 78% of the deaths due to isolated tornadic supercells and 12% linked to tornadic supercells embedded within an organized linear convective system. The morphologies responsible for the 191 nontornadic convective wind fatalities vary substantially, with bow echoes (24%), squall lines (19%), and clusters of cells (19%) the most prominent convective modes producing fatalities. Unorganized and quasi-organized convection accounted for nearly half (45%) of all nontornadic convective wind fatalities. Over half of all fatal tornadoes (53%) occurred between 0000 and 0600 UTC, and most (59%) fatalities from nontornadic convective winds occurred in the afternoon between 1800 and 0000 UTC. Two corridors of nontornadic convective wind fatalities were present: the lower Great Lakes region and the mid-South. Tornado fatalities were greatest in a zone extending from southeastern Missouri, through western Tennessee, northeastern Arkansas, Mississippi, Alabama, and Georgia. The methods employed and results found in this study are directly applicable in the further development of storm classification schemes and provide forecasters and emergency managers with information to assist in the creation and implementation of new convective wind mitigation strategies.
Abstract
There are still hundreds of casualties produced by thunderstorm hazards each year in the United States despite the many recent advances in prediction and mitigation of the effects of convective storms. Of the four most common thunderstorm hazards (wind, hail, flooding, and lightning), convective winds (tornadic and nontornadic) remain one of the most dangerous threats to life and property. Using thunderstorm fatality and Weather Surveillance Radar-1988 Doppler (WSR-88D) data, this research illustrates a spatial and temporal analysis of the storm morphological characteristics, or convective mode, of all fatal tornadic and nontornadic convective wind events from 1998 to 2007. The investigation employs a radar-based morphology classification system that delineates storm type based on an organizational continuum, including unorganized cellular, quasi-organized cellular (either a cluster of cells or a broken line of cells), organized cellular (supercells and supercells embedded in an organized linear system), and organized linear (either squall lines or bow echoes). Results illustrate that over 90% of the 634 recorded tornado deaths were associated with supercells, with 78% of the deaths due to isolated tornadic supercells and 12% linked to tornadic supercells embedded within an organized linear convective system. The morphologies responsible for the 191 nontornadic convective wind fatalities vary substantially, with bow echoes (24%), squall lines (19%), and clusters of cells (19%) the most prominent convective modes producing fatalities. Unorganized and quasi-organized convection accounted for nearly half (45%) of all nontornadic convective wind fatalities. Over half of all fatal tornadoes (53%) occurred between 0000 and 0600 UTC, and most (59%) fatalities from nontornadic convective winds occurred in the afternoon between 1800 and 0000 UTC. Two corridors of nontornadic convective wind fatalities were present: the lower Great Lakes region and the mid-South. Tornado fatalities were greatest in a zone extending from southeastern Missouri, through western Tennessee, northeastern Arkansas, Mississippi, Alabama, and Georgia. The methods employed and results found in this study are directly applicable in the further development of storm classification schemes and provide forecasters and emergency managers with information to assist in the creation and implementation of new convective wind mitigation strategies.
Abstract
Tornado disasters and their potential are a product of both hazard risk and underlying physical and social vulnerabilities. This investigation appraises exposure, which is an important component and driver of vulnerability, and its interrelationship with tornado risk in the United States since the mid-twentieth century. The research demonstrates how each of these dynamic variables have evolved individually and interacted collectively to produce differences in hazard impact and disaster potential at the national, regional, and local scales. Results reveal that escalating tornado impacts are driven fundamentally by growing built-environment exposure. The increasing tornado disaster probability is not uniform across the landscape, with the mid-South region containing the greatest threat based on the juxtaposition of an immense tornado footprint risk and elevated exposure/development rates, which manifests—at least for one important impact marker—in the area’s high mortality rate. Contemporary, high-impact tornado events are utilized to emphasize how national- and regional-level changes in exposure are also apparent at the scale of the tornado. The study reveals that the disaster ingredients of risk and exposure do vary markedly across scales, and where they have increasing and greater overlap, the probability of disaster surges. These findings have broad implications for all weather and climate hazards, with both short- and long-term mitigation strategies required to reduce future impacts and to build resilience in the face of continued and amplifying development in hazard-prone regions.
Abstract
Tornado disasters and their potential are a product of both hazard risk and underlying physical and social vulnerabilities. This investigation appraises exposure, which is an important component and driver of vulnerability, and its interrelationship with tornado risk in the United States since the mid-twentieth century. The research demonstrates how each of these dynamic variables have evolved individually and interacted collectively to produce differences in hazard impact and disaster potential at the national, regional, and local scales. Results reveal that escalating tornado impacts are driven fundamentally by growing built-environment exposure. The increasing tornado disaster probability is not uniform across the landscape, with the mid-South region containing the greatest threat based on the juxtaposition of an immense tornado footprint risk and elevated exposure/development rates, which manifests—at least for one important impact marker—in the area’s high mortality rate. Contemporary, high-impact tornado events are utilized to emphasize how national- and regional-level changes in exposure are also apparent at the scale of the tornado. The study reveals that the disaster ingredients of risk and exposure do vary markedly across scales, and where they have increasing and greater overlap, the probability of disaster surges. These findings have broad implications for all weather and climate hazards, with both short- and long-term mitigation strategies required to reduce future impacts and to build resilience in the face of continued and amplifying development in hazard-prone regions.
Abstract
A database was compiled for the period 1980–2005 to assess the threat to life in the conterminous United States from nonconvective high-wind events. This study reveals the number of fatalities from these wind storms, their cause, and their unique spatial distributions. While tornadoes continue to cause the most wind-related fatalities per year, nonconvective high winds (defined as phenomena such as downslope and gap winds, gradient winds, dust storms, and winds associated with midlatitude cyclones) have the potential to fatally injure more people than thunderstorm or hurricane winds. Nonconvective wind fatalities occur more frequently in vehicles or while boating. Fatalities are most common along the West Coast and Northeast in association with passing extratropical cyclones, with fewer fatalities observed in the central United States despite this region’s susceptibility for high-wind gusts. A combination of physical and social vulnerabilities is suggested as the cause for the unique fatality distribution found. More than 83% of all nonconvective wind fatalities are associated with the passage of extratropical cyclones.
Abstract
A database was compiled for the period 1980–2005 to assess the threat to life in the conterminous United States from nonconvective high-wind events. This study reveals the number of fatalities from these wind storms, their cause, and their unique spatial distributions. While tornadoes continue to cause the most wind-related fatalities per year, nonconvective high winds (defined as phenomena such as downslope and gap winds, gradient winds, dust storms, and winds associated with midlatitude cyclones) have the potential to fatally injure more people than thunderstorm or hurricane winds. Nonconvective wind fatalities occur more frequently in vehicles or while boating. Fatalities are most common along the West Coast and Northeast in association with passing extratropical cyclones, with fewer fatalities observed in the central United States despite this region’s susceptibility for high-wind gusts. A combination of physical and social vulnerabilities is suggested as the cause for the unique fatality distribution found. More than 83% of all nonconvective wind fatalities are associated with the passage of extratropical cyclones.
Abstract
This research applies an automated mesoscale convective system (MCS) segmentation, classification, and tracking approach to composite radar reflectivity mosaic images that cover the contiguous United States (CONUS) and span a relatively long study period of 22 years (1996–2017). These data afford a novel assessment of the seasonal and interannual variability of MCSs. Additionally, hourly precipitation data from 16 of those years (2002–17) are used to systematically examine rainfall associated with radar-derived MCS events. The attributes and occurrence of MCSs that pass over portions of the CONUS east of the Continental Divide (ECONUS), as well as five author-defined subregions—North Plains, High Plains, Corn Belt, Northeast, and Mid-South—are also examined. The results illustrate two preferred regions for MCS activity in the ECONUS: 1) the Mid-South and Gulf Coast and 2) the Central Plains and Midwest. MCS occurrence and MCS rainfall display a marked seasonal cycle, with most of the regions experiencing these events primarily during the warm season (May–August). Additionally, MCS rainfall was responsible for over 50% of annual and seasonal rainfall for many locations in the ECONUS. Of particular importance, the majority of warm-season rainfall for regions with high agricultural land use (Corn Belt) and important aquifer recharge properties (High Plains) is attributable to MCSs. These results reaffirm that MCSs are a significant aspect of the ECONUS hydroclimate.
Abstract
This research applies an automated mesoscale convective system (MCS) segmentation, classification, and tracking approach to composite radar reflectivity mosaic images that cover the contiguous United States (CONUS) and span a relatively long study period of 22 years (1996–2017). These data afford a novel assessment of the seasonal and interannual variability of MCSs. Additionally, hourly precipitation data from 16 of those years (2002–17) are used to systematically examine rainfall associated with radar-derived MCS events. The attributes and occurrence of MCSs that pass over portions of the CONUS east of the Continental Divide (ECONUS), as well as five author-defined subregions—North Plains, High Plains, Corn Belt, Northeast, and Mid-South—are also examined. The results illustrate two preferred regions for MCS activity in the ECONUS: 1) the Mid-South and Gulf Coast and 2) the Central Plains and Midwest. MCS occurrence and MCS rainfall display a marked seasonal cycle, with most of the regions experiencing these events primarily during the warm season (May–August). Additionally, MCS rainfall was responsible for over 50% of annual and seasonal rainfall for many locations in the ECONUS. Of particular importance, the majority of warm-season rainfall for regions with high agricultural land use (Corn Belt) and important aquifer recharge properties (High Plains) is attributable to MCSs. These results reaffirm that MCSs are a significant aspect of the ECONUS hydroclimate.
