Tropical cyclones (TCs) frequently affect coastal areas of Australia and islands in the tropical Indian and Pacific oceans. Multi-hazards associated with TCs (destructive winds, storm surges and torrential rain) have dramatic impact on population and infrastructure. Accurate forecasting of TC seasonal activity is an important part of a Climate Risk Early Warning System (CREWS) for improving resilience of the society to potentially destructive impacts of TCs. Currently, a statistical model-based prediction of TC activity in the coming season is used for operational seasonal forecasting in the Australian region and the South Pacific Ocean. In this chapter, a possibility of improving the accuracy of seasonal TC prediction using advanced statistical model-based approaches is demonstrated. It is also demonstrated that an alternative approach-dynamical (physics-based) climate modelling-is promising for skilful seasonal TC forecasting. Using improved statistical and dynamical model-based methodologies for TC seasonal prediction as an integral part of the CREWS will provide valuable information about TC seasonal variability and will assist with decision making, responses and adaptation in island countries.
CITATION STYLE
Kuleshov, Y. (2016). Climate Risk Early Warning System for Island Nations: Tropical Cyclones. In Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection. InTech. https://doi.org/10.5772/64029
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