Abstract
Tropical Cyclone (TC) wind and wave models are used to quantify meteorological and oceanographic conditions in the application of offshore engineering design criteria. In regions such as the North West Shelf of Australia, a statistically sound 'risk of failure' design assessment can require historical records far longer than are typically available, even with meteorological datasets of several decades length. To address this mismatch between design requirements and observational history, synthetic tropical cyclone wind and wave datasets can be developed to mimic long records with the objective of estimating extreme event average recurrence intervals of 10,000 years. Modelling large numbers of TC storms in practice requires a trade-off between time (computational efficiency) and accuracy (model skill). The development of the synthetic dataset in this study draws the balance by combining computationally efficient parametric models, and computationally intensive fully dynamic models, with different model grid resolutions. This paper outlines performance differences between the various model approaches and make recommendations for engineering.
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