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In this study, a stochastic tropical cyclone (TC) intensity model based on a deterministic fast physics-based TC intensity model is developed for potential applications in the TC hazard assessment. To account for the effect of randomness, an error term is introduced to the ordinary differential equation for the TC intensity. The error term is assumed to be the summation of a mean part and a white noise. The geographically weighted summary statistics method is adopted to estimate the geographically varying mean and white noise intensity of the error term using the 1,010 historical tropical cyclone records from the western North Pacific. The effects of the mean and random parts of the error term on the model's performance are investigated in terms of the individual samples and the probabilistic characteristics of TC intensity evolution. The results show that, by introducing the error term, the difference of the 25-, 50-, and 100-year recurrence interval values between the model and observation can be largely reduced from the level of 10% to less than 4%, validating the adequacy of the proposed model in the TC hazard assessment. [ABSTRACT FROM AUTHOR]

• A framework of TC wind hazard assessment based on the probability density evolution method integrated with the change-of-measure technique is proposed. • The high efficiency of the proposed framework in assessing the TC wind hazard at multiple locations is validated. • The sensitivity of the TC wind hazard to controlling random variables is analyzed. • The TC wind hazard on the southeast coast of China is assessed. The tropical cyclone (TC) wind hazard assessment formulates a change-of-random-variable problem, which maps the probability space of controlling parameters to that of the TC-induced wind. This study presents a framework for analyzing the TC wind hazard based on the probability density evolution method (PDEM) integrated with the change of probability measure (COM). It is advantageous in the problem of assessing the TC wind hazards at multiple locations over the traditional approach because a common set of representative deterministic solutions of physical equations can be re-used for all locations rather than solving a separate set of solutions at each location. The proposed method is applied to estimate the wind hazard on the Chinese southeast coastline. The common representative deterministic points are selected by the generalized-F-discrepancy-based method with the probability distributions of controlling parameters at Shantou. Based on the COM, the geographical dependence of probabilistic characteristics is represented by changing the assigned probability. The PDEM is incorporated to evaluate the propagation of randomness from the input-controlling parameters to the TC-induced surface wind. The comparison to the Monte Carlo simulation demonstrates the high efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This study proposes a model for predicting tropical cyclone (TC) intensity evolution by utilizing the conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for TC-related risk assessment. The TC intensity evolution is treated as a random variable, which is the output of a nonlinear system conditioned on a series of input random variables that characterize both the TC state (e.g., translation speed and potential intensity) and environment (e.g., vertical wind shear and ocean mixed layer depth). To represent the high-dimensional non-Gaussian probabilistic characteristics, the system is modeled by the CWGAN-GP and calibrated concerning the dataset of 1010 historical TCs from the western North Pacific basin. The over-ocean and overland branches of the model are calibrated separately and then merged. The suitability of the established model is validated by comparing the predicted TC intensity to the observations and the results of existing models such as those based on linear regression. Numerical results indicate that the proposed model successfully replicates the probabilistic properties such as the non-Gaussian marginal distribution of the intensity change and the input-output joint distribution and moments between the TC intensity and predictors, which the linear regression model cannot provide. Further application examples are also carried out by using the model to simulate the evolution of historical TC events and the extreme TC intensities in Southern China, demonstrating its potential role in the TC hazard assessment. • A deep-learning model for predicting tropical cyclone intensity evolution is proposed. • The CWGAN-GP is used to model TC intensity change every 6 h conditioned on environmental variables. • Numerical results demonstrate that the proposed model can replicate the probabilistic properties of TC intensity. [ABSTRACT FROM AUTHOR]

• The proposed tropical cyclone model is completely driven by a set of environmental variables. • This model reproduces the spatial and temporal characteristics of observed tropical cyclones. • The model achieves remarkable balance between computational complexity and efficiency. • This model will provide decision-makers with valuable information regarding potential wind-induced hazards. • When future climate data are fed into this model, the impact of climate change on tropical cyclones can be evaluated. This paper presents an environment-driven tropical cyclone (TC) model for the Western North Pacific basin, which comprises a revised Poisson regression genesis model, a tailored beta-advection track model, and a fast intensity model. The TC model reproduces the temporal and spatial distributions of genesis events, the motion pattern of tracks, as well as the intensity evolutions along tracks. Risk analyses for Hong Kong and along the southeast coastline of mainland China demonstrate that this model can simulate extreme TC events with high fidelity. And the Gaussian mixture model outperforms the Frank Copula in approximating the joint distributions of the annual maximum wind speeds and the corresponding wind directions. This model is driven by a set of environmental variables including relative vorticity, relative humidity, sea surface temperature, vertical wind shear, potential intensity, sub mixed layer depth stratification, mixture layer depth and so on. This enables the model to not only reproduce historical records, but also make predictions for future TC behaviors under climate change with combination of global climate models. Besides, the computational efficiency of the TC model is comparable to traditional purely statistical models. The proposed model can also be coupled with other natural hazard models to conduct multi-hazard analysis. [ABSTRACT FROM AUTHOR]