Extreme wind speeds in tropical cyclones using parametric models

Tropical cyclones are among the most destructive natural disasters. Accurately estimating wind speeds during these extreme weather events remains a challenge but is essential for optimising the design of offshore structures, such as offshore wind turbines, which could be exposed to such phenomena. In this paper, a state-of-the-art parametric model fed with the best-track dataset is implemented to predict wind generated by tropical cyclones at hub height. The surface wind model accounts for a parametric axisymmetric surface wind model and an asymmetric part, both being adjusted with satellite-borne synthetic aperture radar observations. The surface wind is then extrapolated vertically with a logarithmic law using the wave-age-dependent stress parameterisation drag coefficient. The performance of this extrapolation is first assessed with wind measurements of five tropical cyclones ranging from a Category 1 to a Category 4. Then, modelled wind time series and surface wind fields are compared with measurements, a global reanalysis dataset, and a mesoscale model. The consistent results confirm the ability of the model to predict extreme tropical cyclone winds. A key limitation of parametric models lies in their omission of large-scale orographic effects, as illustrated by the complex terrain of Taiwan.