Tropical cyclones, known as hurricanes in the Atlantic and Northeast Pacific or typhoons in the Northwest Pacific, are intense storm systems characterized by strong rotating winds, heavy rainfall, and low atmospheric pressure. They form over warm tropical waters and are major drivers of coastal flooding in tropical and subtropical regions. Annually, around 50 cyclones reach hurricane strength, causing flooding through storm surges and heavy rainfall, threatening communities and ecosystems. Climate change and human activities exacerbate these risks. Accurately predicting coastal flooding due to these cyclones is challenging due to their complex features, limited historical data, and forecasting uncertainties.

This dissertation aims to enhance the reliability of coastal flood forecasts and risk analysis by improving the descriptions of tropical cyclone wind geometry and pathways. It addresses both operational (short-term) and strategic (long-term) flood risk analyses. Operational risk analysis involves forecasting days before and after a cyclone, while strategic analysis deals with climate variability over decades. Both are crucial for comprehensive climate risk management, offering different time frames for preparedness and prevention.

A key element in both types of analyses is accurately representing tropical cyclone conditions in computational models. By examining historical best-track data, empirical relationships for two tropical cyclone geometry parameters—the radius of maximum winds and the radius of gale-force winds—were derived, improving estimates by up to 25%. This improvement is significant, particularly for cyclones outside the United States. These parameters, either observed or derived, are essential for computing surface wind distributions using the Holland wind model, which is critical for coastal flood evaluations.

Strategic risk analyses often suffer from a lack of sufficient historical tracks for reliable flood hazard assessment. To address this, an empirical track model based on Markov chains was introduced, capable of simulating thousands of synthetic storm pathways. The Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) generates these tracks, showing good agreement with historical data and extreme wind speeds. This methodology enhances the estimation of extreme cyclone conditions for strategic risk analysis.

The combined data-driven and physics-based methods were used to quantify coastal flooding in the Southeast Atlantic Coastal Zone of the United States. Comparing cyclone-induced flooding to non-cyclonic flooding revealed that extratropical cyclones are responsible for frequent flooding, while tropical cyclones cause the majority of infrequent but severe floods. For example, with current sea levels, extratropical cyclones contributed to half the flooded area, but tropical cyclones accounted for ~96% of the flooded area for 100-year events, affecting significantly more people. At higher sea levels, tropical cyclone-specific flood risk diminished as areas became uniformly susceptible to flooding. This analysis highlights the importance of considering both cyclone and non-cyclone flood factors in future research.

Operational risk assessments, critical for protecting lives and minimizing economic impacts, involve simulating numerous ensemble members to account for uncertainties in cyclone track, speed, and intensity. The Tropical Cyclone Forecasting Framework (TC-FF) integrates major physical drivers such as tide, surge, and rainfall, using Gaussian error distributions and autoregressive techniques. A case study of Cyclone Idai in Mozambique demonstrated the need for a large number of ensemble members for reliable forecasts. TC-FF showed less than 10% difference from operational ensembles, suggesting its utility in data-scarce environments.

This dissertation provides new insights into tropical cyclone wind geometry, pathways, and their role in compound flooding. Future research should enhance data collection, particularly from satellites, to validate models and understand storm characteristics better. Incorporating overlooked processes, leveraging data assimilation, and exploring more efficient methods, including Deep Learning, are essential for advancing flood risk assessment and capturing tropical cyclone variability.

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Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

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Tropical-cyclone impacts can have devastating effects on the population, infrastructure, and natural habitats. However, predicting these impacts is difficult due to the inherent uncertainties in the storm track and intensity. In addition, due to computational constraints, both the relevant ocean physics and the uncertainties in meteorological forcing are only partly accounted for. This paper presents a new method, called the Tropical Cyclone Forecasting Framework (TC-FF), to probabilistically forecast compound flooding induced by tropical cyclones, considering uncertainties in track, forward speed, and wind speed and/or intensity. The open-source method accounts for all major relevant physical drivers, including tide, surge, and rainfall, and considers TC uncertainties through Gaussian error distributions and autoregressive techniques. The tool creates temporally and spatially varying wind fields to force a computationally efficient compound-flood model, allowing for the computation of probabilistic wind and flood hazard maps for any oceanic basin in the world as it does not require detailed information on the distribution of historical errors. A comparison of TC-FF and JTWC operational ensembles, both based on DeMaria et al. (2009), revealed minor differences of <10 %, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. The method was applied to Cyclone Idai in Mozambique. The underlying physical model showed reliable skill in terms of tidal propagation, reproducing the storm surge generation during landfall and flooding near the city of Beira (success index of 0.59). The method was successfully applied to forecasting the impact of Idai with different lead times. The case study analyzed needed at least 200 ensemble members to get reliable water levels and flood results 3 d before landfall (<1 % flood probability error and <20 cm sampling errors). Results showed the sensitivity of forecasting, especially with increasing lead times, highlighting the importance of accounting for cyclone variability in decision-making and risk management.@en