Sparks Nathan, Toumi Ralf
Imperial College London, Department of Physics, London, SW7 2AZ, UK.
Sci Data. 2024 Apr 24;11(1):424. doi: 10.1038/s41597-024-03250-y.
Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones (TC) and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset of wind speed which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed are realistic globally.
鉴于登陆热带气旋（TC）的频率较低且可靠观测的时间较短，在全球范围内评估热带气旋风险仍然是一项挑战。合成热带气旋数据集有助于克服这些问题。在此，我们展示了一个由帝国理工学院风暴模型（IRIS）创建的全新全球数据集。IRIS具有创新性，因为与其他合成TC模型不同，它仅模拟从生命周期最大强度点开始的衰减过程。这将数据集中的偏差降至最低。它以42年观测到的热带气旋为输入，创建了一个包含10000年风速的合成数据集，然后根据观测数据进行验证。IRIS捕捉到了观测数据的重要统计特征。全球范围内登陆时最大风速的重现期是符合实际情况的。
