Effects of weather variables on suicide are well-documented, but there is still little consistency among the results of most studies. Nevertheless, most studies show a peak in suicides during the spring season, and this is often attributed to increased temperatures. The purpose of this study is to test the relationship between monthly temperature and monthly suicide, independent of months or seasons, for five counties located across the United States. Harmonic analysis shows that four of the five counties display some seasonal components in the suicide data. However, simple linear regression shows no correlation between suicide and temperature, and discriminant analysis shows that monthly departure from mean annual suicide rates is not a useful tool for identifying months with temperatures that are colder or warmer than the annual average. Therefore, it appears that the seasonality of suicides is due to factors other than temperature.
Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.
Florida has been visited by some of the most destructive and devastating hurricanes on record in the United States causing well over $450 billion in damage since the early 20 th century. The value of insured property in Florida against windstorm damage is the highest in the nation and on the rise. The frequency and severity of hurricanes affecting Florida are examined from the best set of available data and the damages are related to characteristics of the storms at landfall. Results show that normalized losses are increasing over time consistent with small increases in hurricane intensity and hurricane size. The best predictor of potential losses is minimum central pressure. Hurricane size alone or in combination with hurricane intensity does not improve on the simpler relationship. An estimate of potential losses from hurricanes can be obtained using a formula involving only a forecast of the minimum pressure at landfall. The ability to estimate potential losses in Florida will increase the ability to estimate losses in other areas of the United States, and will also allow policy makers and insurance companies to provide relevant information to the concerned public.
This study examines the relationship between diurnal temperature range (DTR) and land use/land cover (LULC) in a portion of the Southeast. Temperature data for all synoptically weak days within a 10-yr period are gathered from the National Climatic Data Center for 144 weather stations. Each station is classified as one of the following LULC types: urban, agriculture, evergreen forest, deciduous forest, or mixed forest. A threeway analysis of variance and paired-sample t tests are used to test for significant DTR differences due to LULC, month, and airmass type. The LULC types display two clear groups according to their DTR, with agricultural and urban areas consistently experiencing the smallest DTRs, and the forest types experiencing greater DTRs. The dry air masses seem to enhance the DTR differences between vegetated LULC types by emphasizing the differences in evapotranspiration. Meanwhile, the high moisture content of moist air masses prohibits extensive evapotranspirational cooling in the vegetated areas. This lessens the DTR differences between vegetated LULC types, while enhancing the differences between vegetated land and urban areas. All of the LULC types exhibit an annual bimodal DTR pattern with peaks in April and October. Since both vegetated and nonvegetated areas experience the bimodal pattern, this may conflict with previous research that names seasonal changes in evapotranspiration as the most probable cause for the annual trend. These findings suggest that airmass type has a larger and more consistent influence on the DTR of an area than LULC type and therefore may play a role in causing the bimodal DTR pattern, altering DTR with the seasonal distribution of airmass occurrence.
[1] The record of past tropical cyclones provides an important means to evaluate the hurricane hazard. Historical chronologies are a source of information about tropical cyclones prior to the modern era. Chenoweth (2006) describes an archive of 383 tropical cyclones occurring during the eighteenth and nineteenth centuries, largely before the official hurricane record. The present study demonstrates a novel way this archive can be used to articulate historical tropical cyclone activity across space. First, an event in the archive is assigned a series of latitude/longitude coordinates approximating the descriptive locations of the cyclone's affect. Second, tropical cyclones from the modern record that approach these locations (modern analogs) are mapped. Third, a probable pathway and a realistic track of the archived event is created by averaging the modern analog tracks. As an example, the procedure is used to generate a map showing the tracks of the Atlantic tropical cyclones of 1766. Sensitivity of the methodology to changes in event location and event timing are considered. The study shows that historical hurricane chronologies when combined with a history of cyclone tracks can provide useful information about the older events that is not directly related to where the original information was gathered. When this information is available for all cyclones it should help climatologists better understand long-term variations in tropical cyclone activity.
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