Tornadoes cause numerous deaths and significant property damage each year, yet how tornado activity varies across climate states, particularly under global warming, remains poorly understood. Importantly, severe weather events arise during transient periods of extreme thermodynamic environments whose variability may differ from that of the environmental mean state. This study analyzes the climatological relationships between commonly-used severe weather environmental proxies (the product of convective available potential energy and bulk vertical wind shear, energy-helicity index, and the significant tornado parameter) and tornado density on three dominant timescales of climate forcing: diurnal, seasonal, and interannual. We utilize reanalysis data to calculate the spatial distributions of the mean, median, and a range of extreme percentiles of these proxies across each timescale as well as for the full climatology. We then test the extent to which each measure captures the spatiotemporal variability of tornado density over the continental United States. Results indicate that the mean is a suitable statistic when used with the full climatology of the energy-helicity index and the significant tornado parameter without using convective inhibition in calculations, the diurnal cycle for convective available potential energy and the product of convective available potential energy and bulk vertical wind shear, and the interannual variations for all proxies except convective available potential energy. The mean is outperformed by extreme percentiles otherwise. This understanding of climatological relationships between tornadoes and the large scale environments can improve prediction of tornado frequency and provides a foundation for understanding how changes in the statistics of large-scale environments may affect tornado activity in a future warmer climate state.

The southeast United States is prone to severe weather throughout the year. Despite technological advances, some severe weather events occasionally remain unwarned in this part of the country. Past studies examined the relationship between cloud-to-ground (CG) lightning and warm season severe weather episodes. The present study examined the relationship between cool season tornadoes and CG lightning, with a focus over the southeastern United States, where most cool season tornadoes occur. Data from the Storm Prediction Center and National Lightning Detection Network (NLDN) were used to investigate CG lightning properties within 50 km and one hour before tornado touchdown. This was completed over a period of 13 cool seasons from October 1989 through March 2002. Of 3325 tornado events, 2358 contained at least one NLDN-detectable flash. CG lightning attributes of peak current, multiplicity, and flash density compared well with those of prior warm season lightning research. Overall event frequency appeared to be lower than in the warm season. Almost all Central Plains events were accompanied by at least one NLDN-detectable flash. Up to 70% of tornado events near the Gulf of Mexico and Atlantic coasts contained no NLDN-detectable lightning. Although it is not known why this trend was observed, it is speculated that NLDN detection efficiency and/or storm structure differences may play a role in these observations. Warm season studies have correlated tornadoes with predominantly positive (>50% positive CG lightning), or PPCG storms. Gridded maps showed the greatest percentage and highest frequency of cool season PPCG storms across Kansas and Nebraska, with up to 70% of events associated with PPCG lightning. A secondary, albeit lower, frequency maximum extended 1° to 2° inland across Louisiana into North Carolina. This study also subjectively defined a storm with “enhanced” positive cloud-to-ground (EPCG) lightning as one containing >25% positive cloud-to-ground lightning, which corresponds to approximately the 75th percentile of all cool season tornadoes. This has lead to speculation that EPCG criterion may be a better indicator of the possibility of severe weather than the traditional PPCG criterion.

No description available.

Severe weather outbreaks are fairly common events that occur multiple times a year. Many studies have attempted to define and quantify these outbreaks, however, no work has been done to directly relate synoptic-scale processes to outbreak intensity using the N15 ranking index. It is believed that a statistically significantly strong relationship between outbreak severity and quantified synoptic-scale parameters exists and can be utilized to predict the severity of an upcoming outbreak using the N15 ranking index. Utilizing the NCEP-NCAR Reanalysis dataset, synoptic-scale variables were chosen and standardized into domains created from areal coverages. A series of tests were completed, including stepwise regression, principal component analysis, and a bootstrap cross-validation method to find the most significant variables and best domain size. The findings from this study suggest that synoptic-scale processes do not have a strong relationship to severe weather outbreak intensity and that future work would be necessary.

The development of posttraumatic stress disorder (PTSD) and severe weather phobia (SWP) symptoms in relation to weather-related trauma was examined. Participants were college students (N = 815) enrolled at Mississippi State University. Findings suggest that distinct factors (specifically, disorder-specific cognitions and anxiety sensitivity) contribute to the development of PTSD and in the onset of SWP symptoms following exposure to weather-related trauma. A weather trauma model taking into account disorder-specific cognitive vulnerabilities, previous exposure to weather-related trauma, and levels of anxiety sensitivity is suggested to provide an explanation for these differences. The results suggest that prevention and treatment efforts should address learned fear response, aim at reducing anxiety sensitivity, and target disorder-specific cognitions.

The current study examined the prevalence of severe weather phobia in high school students who had experienced a traumatic weather event and considered possible predictor variables to distinguish between students who did and did not develop severe weather phobia after experiencing the traumatic weather event. Participants (N = 17) completed a diagnostic interview and various questionnaires. Severe weather phobia symptoms (e.g., excessive fear, avoidance, anticipatory anxiety, realization that fear is excessive, distress or dysfunction) were common in the sample. Higher levels of PTSD symptoms and certain coping styles distinguished between those with phobia or subclinical phobia and those without, indicating that traumatic responses to severe weather and coping with severe weather by using social support or restraint predicts the development of severe weather phobia.

No description available.

Recent severe weather research has examined the potential role of total lightning patterns in the severe thunderstorm warning-decision process although none to-date have examined these patterns in explicitly weak-shear environments. Total lightning flashes detected by the Earth Networks Total Lightning Network (ENTLN) during the 2012-13 convective seasons (1 May – 31 August) over a region of the Central Appalachian Mountains were clustered into likely discrete thunderstorms and subsequently classified as either single-cell or multicell/supercell storm modes. The classification of storms was determined using a storm index (SI) which was informed by current National Weather Service (NWS) identification techniques. The 36 days meeting the minimum threshold of lightning activity were divided into 24 lightning-defined (LD) single-cell thunderstorm days and 12 LD multicell/supercell days. LD single-cell days possessed statistically significant lower 0000 UTC 0-6 km wind shear (13.8 knots) than LD multicell/supercell days (26.5 knots) consistent with traditional expectations of single-cell and multicell/supercell environments respectively. The popular 2σ total lightning jump algorithm was applied to all flashes associated with 470 individual LD thunderstorms. The frequencies of the storms’ total lightning jumps were then compared against any associated severe weather reports as an accuracy assessment. The overall performance of the algorithm among both categories was much poorer than in previous studies. While probability of detections (POD) of the 2σ algorithm were comparable to previous research, false alarm rates (FAR) were much greater than previously documented. Given these results, the 2σ algorithm does not appear fit for operational use in a weak shear environment. / Master of Science

Severe weather events frequently affect important marine fish stocks and fisheries along the United States Atlantic and Gulf of Mexico coasts. However, the effects of these events on fish and fisheries are not well understood. The availability of self-reported data from two fisheries in a region frequently affected by tropical cyclones provided a unique opportunity to investigate short-term responses to past events. This study involved selecting severe weather events, calculating changes in effort and catch-per-unit- effort (CPUE), and analyzing those changes across various temporal, spatial, and species-specific scenarios. Responses in each variable were analyzed within and across scenario factors and explored for correlations and linear multivariate relationships with hypothesized explanatory variables. A negative overall directional change was identified for logbook fishing effort. Based on both correlations and linear models, changes in logbook fishing effort were inversely related to changes in average maximum wind speed. Severe weather events are more likely to affect fishing effort than catch rates of reef-fish species. However, lack of responses in CPUE may also relate to the ability of this study to detect changes. The temporal and spatial scales analyzed in this study may not have been adequate for identifying changes in effort for the headboat fishery, or in CPUE for either fishery. Although there was no region-wide response in CPUE associated with severe weather events, further research on this topic is necessary to determine if storm-induced changes in fishery data are likely strong, long-lasting, or widespread enough to influence the outcome of stock-wide assessments. / Master of Science

In recent years, rare catastrophic disasters have triggered pharmaceutical supply chain disruptions, resulting in product shortages and other problems that have negatively impacted healthcare delivery. Since the health sector is part of many nations’ critical infrastructure, ensuring minimal disruption in the supporting supply chains is an essential aspect of national security. While traditional risk management approaches have proven adequate for handling independent routine operational risks, these methods are inadequate for infrequent disruptive risks that propagate further risks, producing a domino effect in complex systems. Since today’s pharmaceutical supply chains have many characteristics of complex systems, elucidating the disruptive risks and required resilience capabilities, through knowledge gained from actual disasters, can be valuable in developing mitigation strategies. This research aims to investigate two catastrophic disasters that disrupted pharmaceutical supply chains in recent years. The first case explores the impact of a localized Category IV hurricane that hit Puerto Rico and disrupted the supply chain for saline, an essential product used in the treatment of many illnesses. The risk events that propagated from the disaster are identified and modeled using a Bayesian network to determine where resilience should be strengthened. In accordance with ISO 31000 risk management guidelines, Bayesian networks have been identified as appropriate tools for modeling complex system risks. The second case employs a qualitative research study on the vaccine supply chain supporting mass immunization during the COVID-19 pandemic. A content analysis is conducted to extract the insights of over 100 professionals on disruptors and resilience capability requirements of the COVID-19 vaccine supply chain. The results indicate that a large and diverse set of risk factors and resilience capabilities are pertinent to pharmaceutical supply chains during localized severe weather and global pandemic disasters. Furthermore, to mitigate the impacts of severe weather risk in the case of Puerto Rico, the predominant type of resilience required is absorptive capacity, while in the case of the mass immunization vaccine supply chain, adaptive capacity is key. These findings should be useful to national leaders, pharmaceutical supply chain and healthcare sourcing managers, disaster preparedness officers, and others charged with pharmaceutical supply chain resilience.