A fast intensity simulator for tropical cyclone risk analysis

Abstract

Robust estimates of tropical cyclone risk can be made using large sets of storm events synthesized from historical data or from physics-based algorithms. While storm tracks can be synthesized very rapidly from statistical algorithms or simple dynamical models (such as the beta-and-advection model), estimation of storm intensity by using full-physics models is generally too expensive to be practical. Although purely statistical intensity algorithms are fast, they may not be general enough to encompass the effects of natural or anthropogenic climate change. Here we present a fast, physically motivated intensity algorithm consisting of two coupled ordinary differential equations predicting the evolution of a wind speed and an inner core moisture variable. The algorithm includes the effects of ocean coupling and environmental wind shear but does not explicitly simulate spatial structure, which must be handled parametrically. We evaluate this algorithm by using it to simulate several historical events and by comparing a risk analysis based on it to an existing method for assessing long-term tropical cyclone risk. For simulations based on the recent climate, the two techniques perform comparably well, though the new technique does better with interannual variability in the Atlantic. Compared to the existing method, the new method produces a smaller increase in global tropical cyclone frequency in response to global warming, but a comparable increase in power dissipation.

An Environmentally Forced Tropical Cyclone Hazard Model

“…The interannual correlation of total storm number in GTI with that in observations is shown in Figure . The correlation coefficient for ATL hurricanes in GTI is 0.48, similar to Emanuel et al () while with the new intensity model, it increases to 0.7 in Emanuel (). The correlation coefficient for WNP, ENP, SH, and IO in GTI are 0.30, 0.36, 0.46, and −0.27, respectively.…”

Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry

“…To simulate the mean field winds along each track would require estimation of the Holland parameters. The parameters V m and R m can be estimated using coupled intensity models (Emanuel et al 2004;Emanuel 2017). The parameter B can be estimated as a function of the central pressure difference and V m , as suggested by Holland et al (2010).…”

“…estimated as a function of r 0 , the outer radius at which wind velocity is zero. Finally, a lognormal distribution is recommended for drawing the radius r 0 (Emanuel 1989;Dean et al 2009).…”

“…Well-known synthetic-track-generation models combine both physical and statistical modeling approaches (Casson and Coles 2000;Vickery et al 2000b;Emanuel et al 2006) and use historical hurricane ''best track'' data as input. Maximum wind intensity estimates along the tracks are provided by models such as the Coupled Hurricane Intensity Prediction System (CHIPS) (Emanuel et al 2004) or FAST intensity simulator (Emanuel 2017).…”

“…Wind field models include those proposed by Vickery et al (2000a), Emanuel (2004), Willoughby et al (2006), Holland et al (2010), and Chavas et al (2015). For risk assessment of large-scale infrastructure systems, the impact of spatially heterogeneous wind velocities must be suitably captured.…”