A Stochastic Tropical Cyclone Model for the Northwestern Pacific Ocean with Improved Track and Intensity Representations

Abstract

Tropical cyclones (TCs) present a considerable threat to people and engineering facilities in coastal and offshore regions. Accurate estimation of long-return-period ocean environmental parameters such as wind and waves are limited by sparse historical observations. Thus, stochastic tropical cyclone models (STCMs) are widely used to generate large amounts of artificial synthetic TCs, which can reduce the uncertainty in TC hazard risk assessments. To this end, we developed a new STCM with improved modeling for the TC track and intensity. A linear superposition moving model (LSMM) is proposed for track modeling, in which the translation speeds and directions are expressed as the linear superposition of two parts, representing the temporal development of TCs and the environmental influences, respectively, using a weighting factor. In addition, a three-point intensity model (TPIM) is proposed, in which a third point in addition to the starting and ending points, i.e., the “strongest point” with the lowest central pressure in a TC track, is determined first. Then TC strengthening and decaying is simulated by random sampling. The sensitivity and robustness of this model are analyzed in this study. By comparing with historical data and common models without applying the LSMM and TPIM, it was found that the proposed STCM significantly improves the modeling of TC tracks and the temporal development of TC intensities.