Editorial Type:
Article
Article Type:
Research Article

Simulation of Storm Occurrences Using Simulated Annealing

Ravindra S. Lokupitiya Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Leon E. Borgman L. E. Borgman, Inc., Laramie, Wyoming

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Richard Anderson-Sprecher Department of Statistics, University of Wyoming, Laramie, Wyoming

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Print Publication:
01 Nov 2005
DOI:
https://doi.org/10.1175/JCLI3546.1
Page(s):
4394–4403
Restricted access

Abstract

Modeling storm occurrences has become a vital part of hurricane prediction. In this paper, a method for simulating event occurrences using a simulated annealing algorithm is described. The method is illustrated using annual counts of hurricanes and of tropical storms in the Atlantic Ocean and Gulf of Mexico. Simulations closely match distributional properties, including possible correlations, in the historical data. For hurricanes, traditionally used Poisson and negative binomial processes also predict univariate properties well, but for tropical storms parametric methods are less successful. The authors determined that simulated annealing replicates properties of both series. Simulated annealing can be designed so that simulations mimic historical distributional properties to whatever degree is desired, including occurrence of extreme events and temporal patterning.

Corresponding author address: Dr. Ravindra S. Lokupitiya, Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: ravi@atmos.colostate.edu

Abstract

Modeling storm occurrences has become a vital part of hurricane prediction. In this paper, a method for simulating event occurrences using a simulated annealing algorithm is described. The method is illustrated using annual counts of hurricanes and of tropical storms in the Atlantic Ocean and Gulf of Mexico. Simulations closely match distributional properties, including possible correlations, in the historical data. For hurricanes, traditionally used Poisson and negative binomial processes also predict univariate properties well, but for tropical storms parametric methods are less successful. The authors determined that simulated annealing replicates properties of both series. Simulated annealing can be designed so that simulations mimic historical distributional properties to whatever degree is desired, including occurrence of extreme events and temporal patterning.

Corresponding author address: Dr. Ravindra S. Lokupitiya, Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: ravi@atmos.colostate.edu

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