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Parameterization and Application of Storm Surge/Tide Modeling Using a Genetic Algorithm for Typhoon Periods


doi: 10.1007/s00376-011-0113-9

  • A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model (STORM) to improve sea level predictions. The genetic algorithm was applied to nine typhoons that affected the Korean Peninsula during 2005--2007. The following model parameters were used: the bottom drag coefficient, the background horizontal diffusivity, Smagorinski's horizontal viscosity, and the sea level pressure scaling. Generally, the simulation results using the optimized, mean, and median parameter values improved sea level predictions. The four estimated parameters improved the sea level prediction by 76% and 54% in the bias and root mean square error for Typhoon Kalmaegi (0807) in 2008, respectively. One-month simulations of February and August 2008 were also improved using the estimated parameters. This study demonstrates that parameter optimization on STORM can improve sea level prediction.
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Manuscript History

Manuscript received: 10 September 2011
Manuscript revised: 10 September 2011
通讯作者: 陈斌, bchen63@163.com
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Parameterization and Application of Storm Surge/Tide Modeling Using a Genetic Algorithm for Typhoon Periods

  • 1. Marine Meteorology Division, Observation Infrastructure Bureau/KMA, Korea,Forecast Research Laboratory, National Institute of Meteorological Research/KMA, Korea,Global Environment System Research Laboratory, National Institute of Meteorological Research/KMA, Korea

Abstract: A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model (STORM) to improve sea level predictions. The genetic algorithm was applied to nine typhoons that affected the Korean Peninsula during 2005--2007. The following model parameters were used: the bottom drag coefficient, the background horizontal diffusivity, Smagorinski's horizontal viscosity, and the sea level pressure scaling. Generally, the simulation results using the optimized, mean, and median parameter values improved sea level predictions. The four estimated parameters improved the sea level prediction by 76% and 54% in the bias and root mean square error for Typhoon Kalmaegi (0807) in 2008, respectively. One-month simulations of February and August 2008 were also improved using the estimated parameters. This study demonstrates that parameter optimization on STORM can improve sea level prediction.

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