Study on the applicability of a stochastic typhoon model for probabilistic forecasting of storm surge induced by a typhoon

ABSTRACT

This study investigates the applicability of a stochastic typhoon model, STM, for probabilistic forecasting of storm surge induced by a typhoon. When a typhoon forms and approaches the coast, the present STM generates a number of virtual typhoons with initial conditions specified by the recent history of the actual typhoon. Storm surge is then computed for each of the generated typhoons, and the probabilistic characteristics of the estimated storm surges are investigated. The present STM is based on the higher-order autoregressive model, which can account for the history of the characteristics of individual typhoons. The concept of minimal pressure was introduced to improve the predictive skills of the statistical characteristics of extreme typhoons. The model was applied to the cases of Typhoons Jebi and Trami, which hit Japan in 2018. The present system reasonably explained the contrasting characteristics of Jebi and Trami in terms of their peak storm surge levels in the inner part of Osaka Bay. Furthermore, the sensitivity analysis of the present probabilistic forecasting system suggested that a relatively large number of virtual typhoons are needed for forecasting of the worst storm surge conditions induced by the arbitrary actual typhoons approaching the target coast.

KEYWORDS:

• Stochastic typhoon model
• probabilistic forecast
• storm surge

Acknowledgments

A part of this study was conducted as a research activity of the “Enhancement of National Resilience against Natural Disasters,” Cross-ministerial Strategic Innovation Promotion Program (SIP), under supervision of NIED. The program was supported by the Council for Science, Technology and Innovation (CSTI).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Research Institute for Earth Science and Disaster Prevention [SIP].