Global Hybrid Tropical Cyclone Modeling based upon Statistical and Coupled Climate Models
Abstract
Coastal communities and the (re)insurance industry experience firsthand the adverse effects of tropical cyclones (TCs). Quantifying their financial impacts however remains an important methodological challenge. Risk modeling of TC activity therefore concentrates on representing the damage-driving aspects of the phenomenon. To maintain fidelity to historical observations, in particular for challenging features such as extreme winds and landfall rates, statistical models of risk-driving components such as cyclogenesis, trajectory, frequency, intensity, and size are typically employed instead of physical models. Statistical modeling can expand upon the historical record through the simulation of large numbers of TC events and seasons. But missing connections to environmental conditions that drive TC risk prevent analyses of interannual climate variability and climate change that are key for risk management.
We present a global TC model that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically-coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The El Nino-Southern Oscillation, being a clear driver of global TC activity and being well represented by climate models, is used to link model components to the driving environment. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track. The output from our TC model consists of two parts: 1. event sets, and 2. an annual catalogue; both are generated for six cyclogenesis basins, that is North Atlantic, Eastern North Pacific, Western North Pacific, South Pacific, and North and South Indian. The event sets are sets of trajectories, one for every member and year from the climate model large ensemble, while the annual catalogue is obtained by randomly sampling the trajectories from the event sets. This presentation will focus on model components and their validation, as well as risk maps generated by the model.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMNH52A..03B