DELETE_A fast intensity simulator for tropical cyclone risk analysis
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11069_2017_Article_2890.pdf
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1.9 MB
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Adobe PDF
Checksum (MD5)
c0b7e532519ebf337163ba0e4d8baeaa
Author(s)
Emanuel, Kerry
Date Issued
May 2017
Journal
Natural Hazards
Publisher
Springer Netherlands
Citation
Emanuel, Kerry. “A Fast Intensity Simulator for Tropical Cyclone Risk Analysis.” Natural Hazards 88, no. 2 (May 6, 2017): 779–796. doi:10.1007/s11069-017-2890-7.
Version
Final published version
Abstract
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.
MIT Department
Lorenz Center (Massachusetts Institute of Technology)
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s11069-017-2890-7
