On the Limitations of Discriminating Outbreaks of Severe Convection

Although much has been learned in recent decades regarding the synoptic and subsynoptic environments associated with major tornado outbreaks, very little research has been conducted using a large sample of outbreak cases to distinguish major tornado outbreaks from less significant events. Preliminary investigations suggested that there were systematic differences between the synoptic-scale environments of tornado outbreaks and primarily nontornadic outbreaks, and that synoptic-scale processes occurring up to three days in advance of the outbreaks were a useful means of discriminating the events. Subsequent investigations showed that the time of year in which the outbreaks occurred played a non-negligible role in the discrimination of these events, but that precursor synoptic-scale environments could be used as initial data in mesoscale models to simulate outbreak environments that were systematically different between the two types of outbreaks.Because of the promising results of these initial studies, research encompassing all types of outbreaks should be investigated, as most outbreaks of severe convection cannot be classified readily as major tornado outbreaks or primarily nontornadic outbreaks. This is the focus of the present study. For this work to be implemented, a new scheme to rank and classify severe weather outbreaks of any type must be developed. Initial work implemented a multivariate, linear-weighted index to rank the events based on the characteristics of the severe reports during individual 24-h periods. Although the technique was relatively effective in the identification of the most significant events, days with multiple regionally distinct clusters of severe reports were considered as single events. A modification to this ranking scheme will be introduced in this study to account for these days, using a kernel density estimation technique. Two objective techniques (the areal coverage and principal component analysis methods) will be introduced and their ability to discriminate major severe weather outbreaks from less significant events will be compared. Finally, comparison of these objective techniques to current operational forecasting will be examined to determine the potential utility of these methods in future forecasts.The results of this work suggest that there is reasonable accuracy and skill in discriminating major tornado outbreaks from less significant events. However, a substantial false alarm problem exists with any of the objective techniques developed, which is also observed in current operational forecasts. Implications of these findings and potential topics for future research will be discussed

An assessment of areal coverage of severe weather parameters for severe weather outbreak diagnosis

The areal extent of severeweather parameters favorable for significant severeweather is evaluated as a means of identifying major severe weather outbreaks. The first areal coverage method uses kernel density estimation (KDE) to identify severeweather outbreak locations. Aselected severeweather parameter value is computed at each grid point within the region identified by KDE. The average, median, or sum value is used to diagnose the event's severity. The second areal coverage method finds the largest contiguous region where a severe weather parameter exceeds a specified threshold that intersects theKDEregion. The severeweather parameter values at grid points within the parameter exceedance region are computed, with the average, median, or sumvalue used to diagnose the event's severity. A total of 4057 severe weather outbreaks from 1979 to 2008 are analyzed. An event is considered a major outbreak if it exceeds a selected ranking index score (developed in previous work), and is a minor event otherwise. The areal coverage method is also compared to Storm Prediction Center (SPC) day-1 convective outlooks from 2003 to 2008. Comparisons of the SPC forecasts and areal coverage diagnoses indicate the areal coverage methods have similar skill to SPC convective outlooks in discriminating major and minor severe weather outbreaks. Despite a seemingly large sample size, the rare-events nature of the dataset leads to sample size sensitivities. Nevertheless, the findings of this study suggest that areal coverage should be tested in a forecasting environment as a means of providing guidance in future outbreak scenarios. © 2012 American Meteorological Society

Identification of mid-tropospheric patterns associated with tornado outbreaks in the United States.

The identification of large-scale atmospheric patterns associated with tornado outbreaks poses a great challenge. It involves analysis of physical processes occurring at different time and space scales that, in the right configuration, result in environmental conditions favoring tornado outbreak formation. Over the years, there have been numerous studies that utilize the notion of ‘tornado outbreaks.’ The term has been used to define severe weather events where the occurrence of multiple tornados has been determined. The exact meaning of ‘tornado outbreak,’ however, has been repeatedly redefined and has evolved throughout the years. Depending on the availability of scientific data, technological advancements, and the purpose of these definitions, different authors offered diverse approaches to shape the perception and applications of ‘tornado outbreak.’ This work provides an extensive review of the evolving nature of the ‘tornado outbreak’ definition. Each decade contains multiple examples of manuscripts that contributed to either changes in the tornado outbreak definition or its perception. This work also offers a statistical approach that can be used to define tornado outbreak events and identify historic cases from the tornado report database of the National Weather Service’s Storm Prediction Center. The approach was informed by the review of tornado outbreak definitions and used kernel density estimation to identify 4,991 outbreaks from 1950 to 2017 –– an average of 73 outbreaks per year. Applying a data-driven threshold of seven or more tornados in a cluster, 333 major tornado outbreaks were found to occur east of the Rocky Mountains throughout the 68 years of the analysis. The highest count of tornado outbreaks by month was found in May. Finally, to support efforts directed towards research on large-scale atmospheric patterns and subseasonal forecasting of tornado outbreaks, this study applies principal component analysis (PCA), hierarchical clustering, and silhouette analysis to identify synoptic-scale patterns of 500-hPa geopotential height associated with tornado outbreaks in the United States. The PCA was performed on a similarity matrix derived from monthly 500-hPa geopotential height anomalies from the 20th Century Reanalysis during times when tornado outbreaks initiated. The analysis was performed using T-mode decomposition for observations during May from 1950 to 2014. To determine the number of PC patterns to retain, congruence coefficient analysis on loadings from Promax and Varimax transformations was performed. The PC analysis identified two major atmospheric patterns for the month of May. To validate these results, two additional statistical methods were used: hierarchical clustering and silhouette analysis. Both methods identified the same patterns as the PC analysis, and thus validated our results

Contrasting Environments Associated with Storm Prediction Center Tornado Outbreak Forecasts using Synoptic-Scale Composite Analysis

Tornado outbreaks have significant human impact, so it is imperative forecasts of these phenomena are accurate. As a synoptic setup lays the foundation for a forecast, synoptic-scale aspects of Storm Prediction Center (SPC) outbreak forecasts of varying accuracy were assessed. The percentages of the number of tornado outbreaks within SPC 10% tornado probability polygons were calculated. False alarm events were separately considered. The outbreaks were separated into quartiles using a point-in-polygon algorithm. Statistical composite fields were created to represent the synoptic conditions of these groups and facilitate comparison. Overall, temperature advection had the greatest differences between the groups. Additionally, there were significant differences in the jet streak strengths and amounts of vertical wind shear. The events forecasted with low accuracy consisted of the weakest synoptic-scale setups. These results suggest it is possible that events with weak synoptic setups should be regarded as areas of concern by tornado outbreak forecasters

Synoptic composites of tornadic and nontornadic outbreaks

Tornadic and nontornadic outbreaks occur within the United States and elsewhere around the world each year with devastating effect. However, few studies have considered the physical differences between these two outbreak types. To address this issue, synoptic-scale pattern composites of tornadic and nontornadic outbreaks are formulated over North America using a rotated principal component analysis (RPCA). A cluster analysis of the RPC loadings group similar outbreak events, and the resulting map types represent an idealized composite of the constituent cases in each cluster. These composites are used to initialize aWeather Research and Forecasting Model (WRF) simulation of each hypothetical composite outbreak type in an effort to determine the WRF's capability to distinguish the outbreak type each composite represents. Synoptic-scale pattern analyses of the composites reveal strikingly different characteristics within each outbreak type, particularly in the wind fields. The tornado outbreak composites reveal a strong low- and midlevel cyclone over the eastern Rockies, which is likely responsible for the observed surface low pressure system in the plains. Composite soundings from the hypothetical outbreak centroids reveal significantly greater bulk shear and storm-relative environmental helicity values in the tornado outbreak environment, whereas instability fields are similar between the two outbreak types. The WRF simulations of the map types confirm results observed in the composite soundings. © 2012 American Meteorological Society

Synoptic-scale differences in the characterization of high-shear low-CAPE tornado outbreaks in the United States

High-Shear Low-CAPE (Convective Available Potential Energy) (HSLC) Tornado Outbreaks (TOs) are a specific subset of TOs that occur each year, primarily East of the Rocky Mountains. This study looks to define HSLC TOs with the use of quartiles of the most supported shear and CAPE measure, create a climatology of HSLC TOs, and to give a better description of the synoptic-scale patterns associated with HSLC TOs. Statistical analysis of quartiles and inner quartile range (IQR) were conducted to see which is the best measure. Ultimately, Mixed-layer CAPE (MLCAPE) and 0-3km shear were used due to past support and were used to define HSLC TOs. Bootstrapping was conducted, and compositing was created for each of the five regions. Bootstrapping between some regions showed statistical significance, and some of the composites matched up closely to what was seen in past HSLC research

Epidemiological investigations into the 2007 outbreak of equine influenza in Australia

Equine influenza is a highly contagious and widespread viral respiratory disease of horses and other equid species, characterised by fever and a harsh dry cough. Prior to August 2007, Australia was one of only three countries to have remained free of equine influenza. An incursion of equine influenza virus H3N8 in that month resulted in a four-month outbreak during which approximately 69,000 horses were infected on an estimated 9599 premises across two States. Most of the geographic spread occurred within the first 10 days and was associated with the movement of infected horses prior to the implementation of movement controls. The outbreak was contained through a series of interventions that ultimately led to the eradication of equine influenza from Australia. During and immediately after the outbreak, intensive epidemiological investigations, laboratory and retrospective analytical studies were conducted culminating in a series of detailed reports and publications, and the collation of a highly detailed outbreak dataset. Further research into the factors that contributed to the spread of the outbreak and the effectiveness of measures implemented to control and contain it was considered important. The aim of this thesis was therefore to investigate the factors that contributed to the spread of the 2007 equine influenza outbreak in Australia and to develop statistical methods and tools useful for informing the surveillance and control of future emergency animal disease events. A case-control study was conducted to investigate premises-level risk factors, specifically whether compliance with advised biosecurity measures prevented the spread of equine influenza onto horse premises. Horse owners and managers on 200 properties across highly affected areas of New South Wales were interviewed. The proximity of premises to the nearest infected premises was the factor most strongly associated with case status. Case premises were more likely than control premises to be within 5 km and beyond 10km of an infected premises. Having a footbath in place on the premises before any horses were infected was associated with a nearly four-fold reduction in odds of infection (odds ratio = 0.27; 95% confidence interval: iv 0.09, 0.83). This protective association may have reflected overall premises biosecurity standards related to the fomite transmission of equine influenza: there was high correlation amongst several, generally protective, variables representing personal ‗barrier hygiene‘ biosecurity measures (hand-washing, changing clothes and shoes, and having a footbath in place). The movement of infected horses and local disease diffusion were known to be important mechanisms of spread early in this outbreak. A network analysis was conducted to investigate the relative contribution of each mechanism. The relationship between infected and susceptible horse premises (contact through animal movements and spatial proximity) was described by constructing a mixed transmission network. During the first 10 days of the 2007 equine influenza outbreak in Australia, horses on 197 premises were infected. A new likelihood-based approach was developed and it was estimated that 28.3% of early disease spread (prior to the implementation of horse movement restrictions) was through the movement of infected horses (95% CI: 25.6, 31.0%). Most local spread was estimated to have occurred within 5 km of infected premises. Based on a direct estimate of the shape of the spatial transmission kernel, the incidence beyond 15 km was very low. The median distance that infected horses were moved was 123 km (range 4–579 km). In an extension of the network analysis, novel methods were developed to delineate spatial clusters of infected premises and describe the sequence of cluster formation and the widespread dispersal experienced during the first 30 days of the outbreak. Premises identified as infected by the movement of infected horses were found to be critical to the seeding of infection in spatial clusters. Combined analysis of spatial and contact network data demonstrated that early in this outbreak local spread emanated outwards from the small number of infected premises in the contact network, up to a distance of around 15 km. A purely spatial method of modelling epidemic spread (kriging) was imprecise in describing the pattern of spread during this early phase of the outbreak (explaining only 13% of the variation in estimated date of onset of v infected premises), because early dissemination was dominated by network-based spread. Prior to this thesis, there was an abundance of anecdotal information regarding the role of meteorological factors and other environmental determinants in the spread of the 2007 equine influenza outbreak in Australia. A survival analysis was therefore conducted to empirically estimate the association between meteorological variables (wind, air temperature, relative humidity and rainfall) and time-to-infection in the largest cluster of the outbreak, in northwest Sydney. The equine influenza outbreak dataset was structured to enable generalised Cox regression modelling of the association between time-varying covariates representing premiseslevel meteorological conditions. The cumulative incidence in the northwest Sydney cluster was estimated to be 53.0% (95% CI: 51.4, 54.7%). Local spatial spread of equine influenza was found to be associated with relative humidity, air temperature and wind velocity. Meteorological conditions 3–5 days prior were strongly associated with hazard of influenza infection. Strong winds (>30 km hour-1) from the direction of nearby infected premises were associated with influenza infection, as was low relative humidity (<60%). A nonlinear relationship was observed with air temperature: the lowest hazard was on days when maximum daily air temperature was between 20–25 °C. Drawing on the findings of the above studies, a spatially-explicit stochastic epidemic model of equine influenza transmission was developed to investigate the underlying disease process, estimate the effectiveness of several control measures applied during the 2007 outbreak and to provide a dynamic modelling framework for rapid assessment of future equine influenza outbreaks in Australia. A reversible jump Markov chain Monte Carlo algorithm was used to estimate Bayesian posterior distributions of key epidemiological parameters based on data from two highly affected regions. A large amount of regional heterogeneity was observed in the underlying epidemic process, the estimated rate of decay of transmission by distance from infected premises, the intra-premises transmission rate and the effect of premises area. Model outputs were highly cross-correlated both temporally and spatially with data observed during vi the 2007 outbreak, and with outputs of a previous model. Pseudo-validation of the model against data, not used in its development, demonstrated of how it may be applied to develop rapid assessments of future outbreaks affecting horse populations in comparable regions to those studied. The study results documented in this thesis have elucidated the key factors underlying the spread of the 2007 equine influenza outbreak in Australia, and presented new methods of describing such rapidly spreading epidemics. The movement of infected horses, meteorological variables (air temperature, humidity and wind speed), on-farm biosecurity measures and intrinsic features of horse premises (proximity to other infected premises, numbers of horses held and premises area) were all important variables that influenced the spread of infection onto horse premises. These insights allow development of better policy and control programs in the event of a future equine influenza virus incursion

Epidemiological investigations into the 2007 outbreak of equine influenza in Australia

Equine influenza is a highly contagious and widespread viral respiratory disease of horses and other equid species, characterised by fever and a harsh dry cough. Prior to August 2007, Australia was one of only three countries to have remained free of equine influenza. An incursion of equine influenza virus H3N8 in that month resulted in a four-month outbreak during which approximately 69,000 horses were infected on an estimated 9599 premises across two States. Most of the geographic spread occurred within the first 10 days and was associated with the movement of infected horses prior to the implementation of movement controls. The outbreak was contained through a series of interventions that ultimately led to the eradication of equine influenza from Australia. During and immediately after the outbreak, intensive epidemiological investigations, laboratory and retrospective analytical studies were conducted culminating in a series of detailed reports and publications, and the collation of a highly detailed outbreak dataset. Further research into the factors that contributed to the spread of the outbreak and the effectiveness of measures implemented to control and contain it was considered important. The aim of this thesis was therefore to investigate the factors that contributed to the spread of the 2007 equine influenza outbreak in Australia and to develop statistical methods and tools useful for informing the surveillance and control of future emergency animal disease events. A case-control study was conducted to investigate premises-level risk factors, specifically whether compliance with advised biosecurity measures prevented the spread of equine influenza onto horse premises. Horse owners and managers on 200 properties across highly affected areas of New South Wales were interviewed. The proximity of premises to the nearest infected premises was the factor most strongly associated with case status. Case premises were more likely than control premises to be within 5 km and beyond 10km of an infected premises. Having a footbath in place on the premises before any horses were infected was associated with a nearly four-fold reduction in odds of infection (odds ratio = 0.27; 95% confidence interval: iv 0.09, 0.83). This protective association may have reflected overall premises biosecurity standards related to the fomite transmission of equine influenza: there was high correlation amongst several, generally protective, variables representing personal ‗barrier hygiene‘ biosecurity measures (hand-washing, changing clothes and shoes, and having a footbath in place). The movement of infected horses and local disease diffusion were known to be important mechanisms of spread early in this outbreak. A network analysis was conducted to investigate the relative contribution of each mechanism. The relationship between infected and susceptible horse premises (contact through animal movements and spatial proximity) was described by constructing a mixed transmission network. During the first 10 days of the 2007 equine influenza outbreak in Australia, horses on 197 premises were infected. A new likelihood-based approach was developed and it was estimated that 28.3% of early disease spread (prior to the implementation of horse movement restrictions) was through the movement of infected horses (95% CI: 25.6, 31.0%). Most local spread was estimated to have occurred within 5 km of infected premises. Based on a direct estimate of the shape of the spatial transmission kernel, the incidence beyond 15 km was very low. The median distance that infected horses were moved was 123 km (range 4–579 km). In an extension of the network analysis, novel methods were developed to delineate spatial clusters of infected premises and describe the sequence of cluster formation and the widespread dispersal experienced during the first 30 days of the outbreak. Premises identified as infected by the movement of infected horses were found to be critical to the seeding of infection in spatial clusters. Combined analysis of spatial and contact network data demonstrated that early in this outbreak local spread emanated outwards from the small number of infected premises in the contact network, up to a distance of around 15 km. A purely spatial method of modelling epidemic spread (kriging) was imprecise in describing the pattern of spread during this early phase of the outbreak (explaining only 13% of the variation in estimated date of onset of v infected premises), because early dissemination was dominated by network-based spread. Prior to this thesis, there was an abundance of anecdotal information regarding the role of meteorological factors and other environmental determinants in the spread of the 2007 equine influenza outbreak in Australia. A survival analysis was therefore conducted to empirically estimate the association between meteorological variables (wind, air temperature, relative humidity and rainfall) and time-to-infection in the largest cluster of the outbreak, in northwest Sydney. The equine influenza outbreak dataset was structured to enable generalised Cox regression modelling of the association between time-varying covariates representing premiseslevel meteorological conditions. The cumulative incidence in the northwest Sydney cluster was estimated to be 53.0% (95% CI: 51.4, 54.7%). Local spatial spread of equine influenza was found to be associated with relative humidity, air temperature and wind velocity. Meteorological conditions 3–5 days prior were strongly associated with hazard of influenza infection. Strong winds (>30 km hour-1) from the direction of nearby infected premises were associated with influenza infection, as was low relative humidity (<60%). A nonlinear relationship was observed with air temperature: the lowest hazard was on days when maximum daily air temperature was between 20–25 °C. Drawing on the findings of the above studies, a spatially-explicit stochastic epidemic model of equine influenza transmission was developed to investigate the underlying disease process, estimate the effectiveness of several control measures applied during the 2007 outbreak and to provide a dynamic modelling framework for rapid assessment of future equine influenza outbreaks in Australia. A reversible jump Markov chain Monte Carlo algorithm was used to estimate Bayesian posterior distributions of key epidemiological parameters based on data from two highly affected regions. A large amount of regional heterogeneity was observed in the underlying epidemic process, the estimated rate of decay of transmission by distance from infected premises, the intra-premises transmission rate and the effect of premises area. Model outputs were highly cross-correlated both temporally and spatially with data observed during vi the 2007 outbreak, and with outputs of a previous model. Pseudo-validation of the model against data, not used in its development, demonstrated of how it may be applied to develop rapid assessments of future outbreaks affecting horse populations in comparable regions to those studied. The study results documented in this thesis have elucidated the key factors underlying the spread of the 2007 equine influenza outbreak in Australia, and presented new methods of describing such rapidly spreading epidemics. The movement of infected horses, meteorological variables (air temperature, humidity and wind speed), on-farm biosecurity measures and intrinsic features of horse premises (proximity to other infected premises, numbers of horses held and premises area) were all important variables that influenced the spread of infection onto horse premises. These insights allow development of better policy and control programs in the event of a future equine influenza virus incursion

Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector

Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a huge amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, considering that the noise level varies among different bands, a noise variance estimation method is exploited to evaluate the noise level of different bands, and the bands corrupted by severe noise are removed. Second, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Then, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the isolation forest, and a set of pseudo-labeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and then, the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on airborne hyperspectral oil spill data (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches