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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

Correlated random effects from the state tornado models.
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pone.0131876.g012: Correlated random effects from the state tornado models.

Mentions: Maps showing the correlated random effects from the state models are shown in Fig 12. Illinois features a band of significantly below average frequency across the northern quarter of the state with much of the rest of the state above average. Some significant hot spots of above normal activity are noted across the midsection and over the extreme south. Mississippi shows a similar pattern with below normal frequency in the north and higher than average frequency across central and southern parts of the state. These north-south gradients are partially hidden in the map of raw counts but become conspicuous when controlling for county size and population density. The gradients are consistent with what would be expected over the long-term as the tornado season is longer in the south. South Dakota shows a well-defined mainly east-west gradient with significantly more tornadoes across the southeast and significantly fewer tornadoes in the west. Ohio features significantly fewer tornadoes across the southeast and a band of significantly more tornadoes running from near the city of Canton westward to the state line. The model with a correlated random-effects term is a type of smooth algorithm that accounts for population changes, differences in exposure, and trends.


A Statistical Model for Regional Tornado Climate Studies.

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

Correlated random effects from the state tornado models.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131876.g012: Correlated random effects from the state tornado models.
Mentions: Maps showing the correlated random effects from the state models are shown in Fig 12. Illinois features a band of significantly below average frequency across the northern quarter of the state with much of the rest of the state above average. Some significant hot spots of above normal activity are noted across the midsection and over the extreme south. Mississippi shows a similar pattern with below normal frequency in the north and higher than average frequency across central and southern parts of the state. These north-south gradients are partially hidden in the map of raw counts but become conspicuous when controlling for county size and population density. The gradients are consistent with what would be expected over the long-term as the tornado season is longer in the south. South Dakota shows a well-defined mainly east-west gradient with significantly more tornadoes across the southeast and significantly fewer tornadoes in the west. Ohio features significantly fewer tornadoes across the southeast and a band of significantly more tornadoes running from near the city of Canton westward to the state line. The model with a correlated random-effects term is a type of smooth algorithm that accounts for population changes, differences in exposure, and trends.

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