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U.S. Landfalling and North Atlantic Hurricanes: Statistical Modeling of Their Frequencies and Ratios

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  • 1 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, and Willis Research Network, London, United Kingdom
  • | 2 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
  • | 3 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
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Abstract

Time series of U.S. landfalling and North Atlantic hurricane counts and their ratios over the period 1878–2008 are modeled using tropical Atlantic sea surface temperature (SST), tropical mean SST, the North Atlantic Oscillation (NAO), and the Southern Oscillation index (SOI). Two SST input datasets are employed to examine the uncertainties in the reconstructed SST data on the modeling results. Because of the likely undercount of recorded hurricanes in the earliest part of the record, both the uncorrected hurricane dataset (HURDAT) and a time series with a recently proposed undercount correction are considered.

Modeling of the count data is performed using a conditional Poisson regression model, in which the rate of occurrence can depend linearly or nonlinearly on the climate indexes. Model selection is performed following a stepwise approach and using two penalty criteria. These results do not allow one to identify a single “best” model because of the different model configurations (different SST data, corrected versus uncorrected datasets, and penalty criteria). Despite the lack of an objectively identified unique final model, the authors recommend a set of models in which the parameter of the Poisson distribution depends linearly on tropical Atlantic and tropical mean SSTs.

Modeling of the fractions of North Atlantic hurricanes making U.S. landfall is performed using a binomial regression model. Similar to the count data, it is not possible to identify a single best model, but different model configurations are obtained depending on the SST data, undercount correction, and penalty criteria These results suggest that these fractions are controlled by local (related to the NAO) and remote (SOI and tropical mean SST) effects.

Corresponding author address: Gabriele Villarini, Department of Civil and Environmental Engineering, Engineering Quadrangle, Princeton University, Princeton, NJ 08540. E-mail: gvillari@princeton.edu

Abstract

Time series of U.S. landfalling and North Atlantic hurricane counts and their ratios over the period 1878–2008 are modeled using tropical Atlantic sea surface temperature (SST), tropical mean SST, the North Atlantic Oscillation (NAO), and the Southern Oscillation index (SOI). Two SST input datasets are employed to examine the uncertainties in the reconstructed SST data on the modeling results. Because of the likely undercount of recorded hurricanes in the earliest part of the record, both the uncorrected hurricane dataset (HURDAT) and a time series with a recently proposed undercount correction are considered.

Modeling of the count data is performed using a conditional Poisson regression model, in which the rate of occurrence can depend linearly or nonlinearly on the climate indexes. Model selection is performed following a stepwise approach and using two penalty criteria. These results do not allow one to identify a single “best” model because of the different model configurations (different SST data, corrected versus uncorrected datasets, and penalty criteria). Despite the lack of an objectively identified unique final model, the authors recommend a set of models in which the parameter of the Poisson distribution depends linearly on tropical Atlantic and tropical mean SSTs.

Modeling of the fractions of North Atlantic hurricanes making U.S. landfall is performed using a binomial regression model. Similar to the count data, it is not possible to identify a single best model, but different model configurations are obtained depending on the SST data, undercount correction, and penalty criteria These results suggest that these fractions are controlled by local (related to the NAO) and remote (SOI and tropical mean SST) effects.

Corresponding author address: Gabriele Villarini, Department of Civil and Environmental Engineering, Engineering Quadrangle, Princeton University, Princeton, NJ 08540. E-mail: gvillari@princeton.edu
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