Global Stochastic Tropical Cyclone Model Based on Principal Component Analysis and Cluster Analysis

Abstract

A global stochastic tropical cyclone model was developed as a means for preparing a large number of artificial tropical cyclone (TC) samples with different values for parameters such as track, minimum sea level pressure, and translation speed. In this paper, the model and the results of its verification are presented in detail. The proposed stochastic model is sensitive to approximations of the joint probability distribution functions (PDFs) of TC parameters and temporal correlations. A newly introduced accurate method for approximating joint PDFs by using principal component analysis and cluster analysis resulted in improved reproducibility of TC parameters. The simulation results were compared with historical observational data from the northwestern Pacific, southwestern Pacific, and North Atlantic Oceans. The gridaveraged mean values and distribution patterns of PDFs of TC parameters were in agreement with observational data.

Effects of global warming on the impacts of Typhoon Mireille (1991) in the Kyushu and Tohoku regions

“…Regional climate models nested in GCMs can improve quantitative representations of TCs (Nakano et al, , 2012. The second approach is to estimate statistical characteristics by generating a large number of simulated typhoons with the assumption of a stochastic typhoon model, like the ones used in Fujii and Mitsuta (1986), Fujii (1998), and Nakajo et al (2014). Another approach is to use a pseudo-global warming (PGW) experiment in which climate change components are added to past analysis fields in regional climate modeling (Sato et al, 2007) to assess the impacts of climate change on the intensity of past extreme TCs.…”

Risk Assessment of Aggregate Loss by Storm Surge Inundation in Ise and Mikawa Bay

“…Typhoon data which passed only Ise Bay and both Ise and Mikawa Bay are extracted from the stochastic typhoon model (Nakajo, et al, 2014). Number of typhoons are 192 and 805 in 1000 years, respectively.…”

Coastal flooding risk associated to tropical cyclones in a changing climate. Application to Port of Spain (Trinidad and Tobago)

“…Therefore, a methodology based on synthetic TC tracks and statistical and dynamical modelling of the physical mechanisms controlling the flood levels is proposed to characterize present and future inundation. Synthetic TC tracks generation aims to increase the historical population of TCs with the same statistical behaviour than those existing in the record (Nakajo et al, 2014) considering all possible storms consistent with basin climatology. The synthetic TC population allows to improve the historical characterization, but implies a high computational cost when 25 dynamically modelling waves and sea levels associated with the passage of each TC.…”

“…To stochastically generate synthetic TC tracks two main approaches are found in the literature. Statistical approaches are based on Monte Carlo methods that simulate synthetic TCs using historical TC statistics , Nakajo et al 2014) whereas 5 the statistical-deterministic approach simulates TC environments statistically and generates TCs in the simulated environments deterministically (Emanuel et al 2006, Aerts et al 2013, Neumann et al 2015. Considering climate change effects on TC in the near-term (up to 2035) there is low confidence of trends in TC frequency, due to several sources of uncertainty, including the large influence of internal variability (Villarini et al 2011, Villarini andVecchi 2012).…”