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A Statistical Model for Regional Tornado Climate Studies.

Jagger TH, Elsner JB, Widen HM - PLoS ONE (2015)

Bottom Line: The model is significantly improved by adding terrain roughness.The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation.Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, Florida State University, Tallahassee, Florida, United States of America.

ABSTRACT
Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA). A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

No MeSH data available.


Related in: MedlinePlus

Population changes between 1970 and 2012.The change is expressed as a percentage difference with 2012 as the base year.
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pone.0131876.g002: Population changes between 1970 and 2012.The change is expressed as a percentage difference with 2012 as the base year.

Mentions: Data preparation continues by adding annual population estimates over the period 1970–2013 from http://www.nber.org/data/census-intercensal-county-population.html to each county. The percentage change over this period using 2012 as the baseline is displayed on a Lambert conformal conic map in Fig 2. Counties in blue indicate more people in 2012 compared to 1970. Counties to the south and west of Kansas City show the largest increases. Butler and Sedgwick counties (Wichita area) and Ford, Gray, and Finney (Dodge City area) also show large percentage increases although the latter area has fewer people (Fig 3). Population densities exceeding 190 people per square kilometer are found in Wyandotte (Kansas City), Johnson, and Sedgwick counties. Population densities for 2013 are estimated using the 2012 county values.


A Statistical Model for Regional Tornado Climate Studies.

Jagger TH, Elsner JB, Widen HM - PLoS ONE (2015)

Population changes between 1970 and 2012.The change is expressed as a percentage difference with 2012 as the base year.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526675&req=5

pone.0131876.g002: Population changes between 1970 and 2012.The change is expressed as a percentage difference with 2012 as the base year.
Mentions: Data preparation continues by adding annual population estimates over the period 1970–2013 from http://www.nber.org/data/census-intercensal-county-population.html to each county. The percentage change over this period using 2012 as the baseline is displayed on a Lambert conformal conic map in Fig 2. Counties in blue indicate more people in 2012 compared to 1970. Counties to the south and west of Kansas City show the largest increases. Butler and Sedgwick counties (Wichita area) and Ford, Gray, and Finney (Dodge City area) also show large percentage increases although the latter area has fewer people (Fig 3). Population densities exceeding 190 people per square kilometer are found in Wyandotte (Kansas City), Johnson, and Sedgwick counties. Population densities for 2013 are estimated using the 2012 county values.

Bottom Line: The model is significantly improved by adding terrain roughness.The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation.Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, Florida State University, Tallahassee, Florida, United States of America.

ABSTRACT
Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA). A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

No MeSH data available.


Related in: MedlinePlus