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- Author or Editor: Kelsey N. Scheitlin x
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Abstract
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 three-way 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.
Abstract
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 three-way 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.
Abstract
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.
Abstract
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.
