A multivariate functional principal component analysis approach to the full-track simulation of tropical cyclones (TCs) is developed for risk assessment. Elemental variables of TC along the track necessary for risk assessment, such as center coordinates, maximum wind speed, minimum central pressure and ordinal dates, can be simulated simultaneously at one go, using solely the best-track data with no data supplemented from any other sources. The simulation model is optimally determined by means of the ladle estimator. A TC occurrence model using the Conway–Maxwell–Poisson distribution is proposed as well, by which different dispersion features of annual occurrence can be represented in a unified manner. With the occurrence model, TCs can be simulated on an annual basis. The modeling and simulation process is programmed and fully automated such that little manual intervention is required, which greatly improves the modeling efficiency and reduces the turnaround time, especially when newly available TC data are incorporated periodically into the model. Comprehensive evaluation shows that this approach is capable of generating high-performance synthetic TCs in terms of distributional and extreme value features, which can be used in conjunction with wind field and engineering vulnerability models to estimate economic and insurance losses for governments and insurance/reinsurance industry.

中文翻译：

---

用于一次性风险评估的热带气旋随机模拟：一种多变量功能 PCA 方法

开发了一种用于热带气旋 (TC) 全径模拟的多元函数主成分分析方法，用于风险评估。风险评估所需的沿轨道的TC元素变量，如中心坐标、最​​大风速、最小中心压力和顺序日期，可以一次性同时模拟，仅使用最佳轨道数据，没有任何其他数据补充来源。模拟模型通过钢包估算器优化确定。提出了一种利用Conway-Maxwell-Poisson分布的TC发生模型，可以统一表示年发生的不同分散特征。使用发生模型，可以每年模拟 TC。建模和仿真过程是编程的和完全自动化的，几乎不需要人工干预，这大大提高了建模效率并减少了周转时间，特别是当新可用的 TC 数据定期合并到模型中时。综合评估表明，该方法能够生成具有分布特征和极值特征的高性能合成TCs，可结合风场和工程脆弱性模型用于估计政府和保险/再保险行业的经济和保险损失.