Global Calculations of Tropical Cyclone Return Periods and an ACE-like Risk Metric
Abstract
Calculating the risk of tropical cyclone (TC) impacts in a given location is very complex, and private sector risk models typically focus only on specific parts of the world with large amounts of insurable assets, leaving developing countries without access to good information about these destructive and sometimes catastrophic storms. The models are also highly specialized to specific industries such as insurance and reinsurance, leaving little information on TC risk to the many other sectors that can be severely disrupted. To address these issues, Gro Intelligence has created a global TC hazard model known as the Gro Climate Indicator for Tropical Cyclone Risk. The model provides return periods of tropical cyclones of each Saffir-Simpson category passing within 50 km of any US county-level administrative region in the world, as well as an index similar to Accumulated Cyclone Energy that facilitates risk averages weighted by exposed assets. The model is based on historical storms from IBTrACS since the late 1970s globally, with the option to use a longer period of record where available, and uses synthetic storm tracks based on this history to address the "luck" issue of estimating risk from historical tracks alone. Comparison of our return periods to those from the National Hurricane Center in various U.S. coastal locations shows strong correlations, and overall good agreement, especially in places where tropical cyclones are common like South Florida. Agreement for stronger storms, and in places where hurricanes are less common like the northeast US, was improved by fitting extreme value distributions to the wind speeds of cyclones hitting a particular location. With this in place, differences between our return periods and NHC's in the northeast are well within the range of various published estimates. Development of this model has led to interesting insights on many aspects of the science of tropical cyclone hazard modeling, which we explore in the presentation.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.A22G1762R