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Abstract

Since 1950, tornadoes have accounted for nearly one third of disaster-related fatalities in the United States, the third high- est after floods and lightning. The largest share of fatalities occurred in the state of Texas, which also accounts for about 7 percent of the overall property damage from tornadoes. An increasing proportion of tornadoes occur during tornado out- breaks, which were responsible for almost 80 percent of tornado-related fatalities in the United States between 1972 and 2010. Although the relationships between tornado severity, economic damage, and injuries and fatalities are generally well understood, few studies examine how injuries and fatalities are affected by a single tornado versus those that are part of a tornado outbreak. This work analyzes a data set of Texas tornadoes that occurred between January 1973 and January 2007, through non-parametric statistical and spatial analyses, examining the associations between tornado severity, number, and geography of occurrence as related to direct injuries and fatalities in severe weather events. Results indicate that tornado severity exacerbates causalities, but outbreaks do have some effect as well. These findings have potential implications for improving forecasting, warning, and preparedness for tornado outbreaks, which could be critical to reducing deaths and injuries from future tornadoes. Key Words: fatalities, injuries, spatial analysis, tornado outbreaks, tornadoes.

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RESULTS OF THE 1968 THROUGH 1970 TORNADO WATCH EXPERIMENT CONDUCTED JOINTLY BY NASA AND NOAA SUGGESTED THE NECESSICTY OF CHARACTERIZING INDIVIDUAL TORNADOES IN ORDER TO IMPROVE THE IDENTITY OF TORNADO-PRODUCING NEPHSYSTEMS.AN ATTEMPT WAS MADE, THEREFORE, TO ATEGORIZE EACH TORNADO BY ITS INTENSITY AND AREA.FUJITA-SCALE WIND AND CORRESPONDING DAMAGE CATEGORIES WERE DEVISED TO CLASSIFY TORNADOES AS GALE (FO), WEAK (F1), STRONG (F2), SEVERE (F3), DEVASTATING (F4), AND INCREDIBLE (F5).ADDITIONALLY, INDIVIDUAL TORNADO AREAS WERE ALSO CATEGORIZED AS TRACE (TR), DECIMICRO (DM), MICRO (MI), MESO (ME), MACRO (MA), GIANT (GI), AND DECAGIANT (DG), THUS PERMITTING THE AUTHORS TO CHARACTERIZE A TORNADO BY A COMBINATION OF INTENSITY AND AREA, SUCH AS 'WEAK DECIMICRO TORNADO', 'SEVERE MESO TORNADO','INCREDIBLE GIANT TORNADO', ETC.A TEST CHARACTERIZATION OF 156 JAPANESE TORNADOES IN 1950-69 WAS ACCOMPLISHED FOR COMPARISON WITH 893 U.S.TORNADOES IN 1965.UNEXPECTEDLY, THE PERCENTAGE DISTRIBUTION OF INTENSITY AND INDIVIDUAL AREA OF U.S.AND JAPANESE TORNADOES IS VERY SIMILAR EXCEPT FOR LARGE AND/OR INTENSE ONES.INTENSITY DISTRIBUTION WITHIN THE DALLAS AND FARGO TORNADOES OF 1957 WAS ALSO STUDIED IN DETAIL.IT WAS ALSO FOUND THAT THE F-SCALE VARIATION ALONG THE PATHS OF FAMILY TORNADOES SHOWS AN INTENSITY OSCILLATION WITH A 45-MIN INTERVAL.FOR FURTHER APPLICATIONS, CHARACTERIZATION OF ATLANTIC HURRICANES, PACIFIC H URRICANES, AND PACIFIC TYPHOONS WAS MADE TO DETERMINE THE TREND OF THEIR CUMULATIVE FREQUENCIES.IT WAS FOUND THAT 90 PER CENT OF THESE STORMS ARE CHARACTERIZED, IN EACH REGION RESPECTIVELY, BY LESS THAN F2.8, F2.3, AND F3.3, INDICATING CLEARLY THAT AVERAGE TYPHOONS ARE MORE INTENSE THAN AVERAGE HURRICANES.FINALLY, THE AREAS OF HURRICANE CAMILLE OF AUGUST 1969 AND THE ISE-WAN TYPHOON OF SEPTEMBER 1959 WERE ANALYZED WITH F-SCALE CONTOURS IN AN ATTEMPT TO DETERMINE THE DISTRIBUTION OF DAMAGING WINDS WITHIN THESE STORMS.(A)
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A geographical information system (GIS) perspective is taken to examine conceptual and methodological complications present in tornado density and probability mapping. Tornado density is defined as the inversedistance- weighted count of tornado touchdown points or tornado-affected cells within a neighborhood area. The paper first adds a few geographic elements into the tornado definition and then characterizes tornado density as a density field in GIS that depends on predefined, modifiable areas to exist. Tornado density is therefore conceptually distinguished from both individual tornadoes and tornado probability. Three factors are identified to be vital in tornado density mapping: the neighborhood size, the distance decay function, and the choice of tornado properties. Correspondingly, 12 neighborhood sizes ranging from 20 to 360 km are tested, four distance decay functions are compared, and two tornado properties-tornado touchdown locations and pathlengths-are separately incorporated in mapping. GIS interpretations, clarifications, and demonstrations are provided for these factors to reach a thorough understanding of how the factors function and affect the resultant tornado density maps. Historical tornado data of the eastern half of the United States from 1973 to 2013 are used in these demonstrations. Uncertainty and propagation analyses are recommended for future tornado density and probability mapping, and a Monte Carlo simulation using tornado pathlength data is conducted as an example of uncertainty modeling. In all, tornado density mapping is diagnosed as a largely subjective activity, and the mapper needs to make multiple choices according to the mapping purpose, scale, and the involved tornado record data.
Article
Tropical cyclones often produce tornadoes that have the potential to compound the injury and fatality counts and the economic losses associated with tropical cyclones. These tornadoes do not occur uniformly through time or across space. Multiple statistical methods were used in this study to analyze the association between tropical cyclone intensity change and tornado frequency. Results indicate that there is an association between the two and that tropical cyclones tend to produce more tornadoes when they are weakening, but the association is weak. Tropical cyclones can also produce a substantial number of tornadoes when they are relatively stable or strengthening.
Article
Insurance data for tornado damages during 1949-2006 revealed 793 tornado events that each caused > 1 million in losses. The average annual loss of these tornado catastrophes is 982 million, an amount that greatly exceeds the existing average of 462 million based on estimates from government records. Tornado losses typically occurred in only one state but when tornadoes occurred with floods or hurricanes, the losses occurred in four or five states. Tornado catastrophes and losses were most frequent in Texas, Oklahoma, and Kansas, and relatively frequent in many Midwestern states. The temporal distribution of tornado catastrophes revealed large interannual variability with a few years of major loss and many years with none. Tornado-only catastrophes and their losses had flat trends for 1949-2006 but trends were upward for cases of tornadoes with floods and cases when tornadoes occurred with hurricanes. These result from upward trends in flooding across the nation and the tornado-hurricane temporal increase results from time-related increases in hurricane-prone storm conditions and from coastal society's growing vulnerability to storm damages.
Article
The statistics Gi(d) and Gi*(d), introduced in Getis and Ord (1992) for the study of local pattern in spatial data, are extended and their properties further explored. In particular, nonbinary weights are allowed and the statistics are related to Moran's autocorrelation statistic, I. The correlations between nearby values of the statistics are derived and verified by simulation. A Bonferroni criterion is used to approximate significance levels when testing extreme values from the set of statistics. An example of the use of the statistics is given using spatial-temporal data on the AIDS epidemic centering on San Francisco. Results indicate that in recent years the disease is intensifying in the counties surrounding the city.
Article
The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
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