C. Yang1,2 and J. Xu31CAMS & China Re CRM · Joint Open Lab on Meteorological Risk and Insurance, Beijing, China.2Faculty of Geographical Science, Beijing Normal University, Beijing, China.3Qingdao Joint Institute of Marine Meteorology, Chinese Academy of Meteorological Sciences, Beijing, China.Corresponding author: C. Yang (chi@bnu.edu.cn), J. Xu (xujing@cma.gov.cn)Key Points:A modified Rankine vortex model with four free parameters is adopted to reconstruct wind fields along the tropical cyclone tracksA Bayesian hierarchical model with a latent neural network is developed to estimate the parameters based on the best-track data setBayesian model averaging is used to predict wind field parameters for synthetic tropical cyclones over the western North Pacific basinAbstractTropical cyclones (TCs) are one of the biggest threats to life and property around the world. Accurate estimation of TC wind hazard requires estimation of catastrophic TCs having a very long return period spanning up to thousands of years. Since reliable TC data are available only for recently decades, stochastic modeling and simulation turned out to be an effective approach to achieve more stable hazard estimates. In common practice, hundreds of thousands of synthetic TCs are generated first, then wind fields are reconstructed along synthetic TC tracks for hazard estimation. A Bayesian hierarchical modeling approach to the reconstruction of TC wind field is proposed. A modified Rankine vortex is adopted as the wind field model, of which the four free parameters are modeled simultaneously through a multi-output neural network as a latent process of the wind field. The four parameters are finally represented, spatially and temporally, by a set of neural network weights, The Bayesian model averaging technique is used for parameter estimation and wind field reconstruction, based on a ensemble of maximum a posteriori estimates of the set of weights. Together with previously proposed algorithm for synthetic TC simulation, a two-stage scheme for TC wind hazard estimation has been formed, which is based on best-track data only and thus is highly consistent. Application of this scheme to the offshore waters in the western North Pacific basin shows inspiring performance and great flexibility for various purposes of TC wind hazard estimation.

A multivariate functional principal component analysis (PCA) 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.