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James W. Yarbrough Jr.
and
Vernon Meentemeyer

Abstract

The goal of this study was to determine the degree of correlation between monthly values of thunderstorm days and tornado occurrences in the eastern United States and to determine the geographic and seasonal patterns of significantly correlated areas. In an initial 8-year analysis (January 1968–December 1975) for each of 124 2° squares of longitude and latitude the number of days with thunderstorms for one city within each square was correlated by month with the number of tornado occurrences. This analysis revealed that 110 coefficients of 1488 (124 squares × 12 months) were significant at the 95% confidence level. Only five coefficients were negative. The pattern of significant squares suggests that strong correlation is most likely at a place during the month(s) of average passage of the polar front. In a second analysis the 124 squares used in the initial analysis were grouped into 33 regions and into four seasons. Maps of the r values in these 33 regions and four seasons were prepared which show the distribution of the thunderstorm day-tornado relationship. Throughout the entire eastern United States the spring season shows the strongest correlations and summer the least. By using grouped data, areas of inverse relationships are apparent in the Southeast in summer and apparently also fall. In a third analysis squares significant at the 99% confidence level in the initial analysis were researched further for a 26-year period (January 1950–December 1975) to refine tendencies and patterns. Sixteen such squares were examined, but only eight exhibited significant correlations at the 99% level with the highest coefficient equal to +0.712. Thus, the statements in the literature which suggest widespread inverse relationships need to be questioned. Strong positive relationships are somewhat rare; however, for squares for which strong correlations exist, the thunderstorm day per tornado ratio for a particular month and square tends to be numerically smaller when fewer thunderstorm days occur.

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Alan Basist
,
Gerald D. Bell
, and
Vernon Meentemeyer

Abstract

Statistical relationships between topography and the spatial distribution of mean annual precipitation are developed for ten distinct mountainous regions. These relationships are derived through linear bivariate and multivariate analyses, using six topographic variables as predictors of precipitation. These predictors are elevation, slope, orientation, exposure, the product (or interaction) of slope and orientation, and the product of elevation and exposure.

The two interactive terms are the best overall bivariate predictors of mean annual precipitation, whereas orientation and exposure are the strongest noninteractive bivariate predictors. The regression equations in many of the climatically similar regions tend to have similar slope coefficients and similar y-intercept values, indicating that local climatic conditions strongly influence the relationship between topography and the spatial distribution of precipitation. In contrast, the regression equations for the tropical and extratropical regions exhibit distinctly different slope coefficients and y-intercept values, indicating that topography influences the spatial distribution of precipitation differently in convective versus nonconvective environments.

The multivariate equations contain between one and three significant topographic predictors. The best overall predictors in these models are exposure and the interaction of elevation and exposure, indicating that exposure to the prevailing wind is perhaps the single most important feature relating topography to the spatial distribution of precipitation in the mountainous regimes studied. The strongest (weakest) multivariate relationships between topography and precipitation are found in the four middle- and high-latitude west coast regions (in the tropical regions), where more than 70% (less than 50%) of the spatial variability of mean annual precipitation is explained. These results suggest that in certain regions, one can estimate the spatial distribution of mean annual precipitation from a limited network of raingauges using topographically based regression equations.

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Andrew Grundstein
,
John Dowd
, and
Vernon Meentemeyer

Thirty-seven children on average die each year in the United States from vehicle-related hyperthermia. In many cases, the parent or caregiver intentionally left the child unattended in the car, unaware of how quickly temperatures may reach deadly levels. To better quantify how quickly temperatures may increase within a car, maximum rates of temperature change were computed from data collected on 14 clear days in Athens, Georgia. Also, a human thermal exchange model was used in a case study to investigate the influence of different meteorological factors on the heat stress of a child in a hot vehicle. Results indicate that a car may heat up by approximately 4°C in 5 min, 7°C in 10 min, 16°C in 30 min, and 26°C in 60 min. Within the vehicle, the dominant energy transfers toward the child are via longwave radiation and conduction from the hot interior surfaces of the car. Modeling simulations show that sun exposure and high-humidity conditions further increase the heat stress on the child but that a negative feedback involving evaporated perspiration reduces the influence of variations in humidity on net heat storage. Last, a table of vehicle temperature changes is included that may help public officials and the media communicate the dangers of vehicle-related hyperthermia in children.

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