Avantika Gori

Accurately assessing tropical cyclone (TC) flood risk requires capturing the dynamic interactions between rainfall‐driven and coastal flood processes. We simulate flooding from over 2,800 synthetic TCs impacting five HUC6 watersheds in eastern North and South Carolina under historical and future (SSP5‐8.5) conditions using climatologically consistent data derived from physics‐based numerical models. Projected future TCs in our data set exhibit slower translation speeds and produce higher rainfall rates, driving substantial increases in flood extent and depth despite a slight decrease in average wind speeds and storm surge. The 1% annual flood hazard grows by 36%, with the contribution of compound flooding nearly doubling from 12% to 25%. When sea level rise is included, coastal and compound flooding expand further inland and upriver. Importantly, we show that inundation is underestimated when runoff and coastal flood hazards are modeled in isolation. Additionally, we find that the joint‐probability of the peak or average boundary conditions for a storm does not equal the probability of the overland flood extent at the HUC6 scale. By leveraging a probabilistic framework, our results highlight that using process‐based models to capture the spatiotemporal dynamics of flooding is essential for accurate watershed‐scale TC flood hazard assessment. These findings underscore the growing importance of compound flooding under climate change and sea level rise and provide a foundation for improved risk‐informed planning, infrastructure design, and adaptation to future hazards in coastal areas.

This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI‐based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025, we assess the ability of two AI models (Pangu‐Weather and Aurora) to reproduce observed TC track density, climatology of storm characteristics, and physical consistency with TC theory. By comparing AI‐simulated TCs with ERA5 reanalysis, we benchmark the distributions of intensity, size, forward speed, and evaluate the model's ability to credibly simulate extratropical transition. Results show that both Pangu and Aurora perform well in reproducing storm track density, forward speed distribution, and outer size distribution. Aurora shows an improved performance in simulating storm intensity compared to Pangu, with less bias in the distribution of minimum central pressure and maximum wind speed. However, both models overestimate the distribution of storm inner size (radius of maximum winds), especially for extreme events. AI models capture the relative frequency and temporal evolution of extratropical transition patterns with reasonable accuracy. The AI‐simulated TCs are also less likely to conform to gradient wind balance compared to ERA5, indicating that the AI TCs may not be physically realistic in many cases. This benchmarking identifies systematic biases that can guide future corrections and support extended applications of AI models for TC hazard and risk assessment. Our work establishes a foundation for future studies using AI weather models in the context of TC climatological and hazard research.

Accurately assessing tropical cyclone (TC) flood risk requires capturing the dynamic interactions between rainfall-driven and coastal flood processes. We simulate flooding from over 2,800 synthetic TCs impacting five HUC6 watersheds in eastern North and South Carolina under historical and future (SSP5-8.5) conditions using climatologically consistent data derived from physics-based numerical models. Projected future TCs in our dataset exhibit slower translation speeds and produce higher rainfall rates, driving substantial increases in flood extent and depth despite a slight decrease in average wind speeds and storm surge. The 1% annual flood hazard grows by 36%, with the contribution of compound flooding nearly doubling from 12% to 25%. When sea level rise is included, coastal and compound flooding expand further inland and upriver. Importantly, we show that inundation is underestimated when runoff and coastal flood hazards are modeled in isolation. Additionally, we find that the joint-probability of the peak or average boundary conditions for a storm does not equal the probability of the overland flood extent at the HUC6 scale. By leveraging a probabilistic framework, our results highlight that using process-based models to capture the spatiotemporal dynamics of flooding is essential for accurate watershed-scale TC flood hazard assessment. These findings underscore the growing importance of compound flooding under climate change and sea level rise and provide a foundation for improved risk-informed planning, infrastructure design, and adaptation to future hazards in coastal areas.

Tropical cyclone (TC) hazards coupled with dense urban development along the coastline have resulted in trillions in US damages over the past several decades, with an increasing trend in losses in recent years. So far, this trend has been driven by increasing coastal development. However, as the climate continues to warm, changing TC climatology may also cause large changes in coastal damages in the future. Approaches to quantifying regional TC risk typically focus on total storm damage. However, it is crucial to understand the spatial footprint of TC damage and ultimately the spatial distribution of TC risk. Here, we quantify the magnitude and spatial pattern of TC risk (in expected annual damage (EAD)) across the US from wind, storm surge, and rainfall using synthetic TCs, physics-based hazard models, and a county-level statistical damage model trained on historical TC data. We then combine end-of-century TC hazard simulations with US population growth and wealth increase scenarios (under the SSP2 4.5 emission scenario) to investigate the sensitivity of changes in TC risk across the US Atlantic and Gulf coasts. We find that not directly accounting for the effects of rainfall and storm surge results in much lower risk estimates and smaller future increases in risk. TC climatology change and socioeconomic change drive similar magnitude increases in total EAD across the US (roughly 160%), and that their combined effect (633% increase) is much higher.

The emerging tropical cyclone (TC)-blackout-heatwave compound risk under climate change is not well understood. In this study, we employ projections of TCs, sea level rise, and heatwaves, in conjunction with power system resilience modeling, to evaluate historical and future TC-blackout-heatwave compound risk in Louisiana, US. We find that the return period for a compound event comparable to Hurricane Ida (2021), with approximately 35 million customer hours of simultaneous power outage and heatwave exposure in Louisiana, is around 278 years in the historical climate of 1980–2005. Under the SSP5-8.5 emissions scenario, this return period is projected to decrease to 16.2 years by 2070–2100, a ~17 times reduction. Under the SSP2-4.5 scenario, it decreases to 23.1 years, representing a ~12 times reduction. Heatwave intensification is the primary driver of this increased risk, reducing the return period by approximately 5 times under SSP5-8.5 and 3 times under SSP2-4.5. Increased TC activity is the second driver, reducing the return period by 40% and 34% under the respective scenarios. These findings enhance our understanding of compound climate hazards and inform climate adaptation strategies.

Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks.

Tropical cyclones (TCs) that undergo rapid intensification (RI) before landfall are notoriously difficult to predict and have caused tremendous damage to coastal regions in the United States. Using downscaled synthetic TCs and physics‐based models for storm tide and rain, we investigate the hazards posed by TCs that rapidly intensify before landfall under both historical and future mid‐emissions climate scenarios. In the downscaled synthetic data, the percentage of TCs experiencing RI is estimated to rise across a significant portion of the North Atlantic basin. Notably, future climate warming causes large increases in the probability of RI within 24 hr of landfall. Also, our analysis shows that RI events induce notably higher rainfall hazard levels than non‐RI events with equivalent TC intensities. As a result, RI events dominate increases in 100‐year rainfall and storm tide levels under climate change for most of the US coastline.

Sediment cores from blue holes have emerged as a promising tool for extending the record of long‐term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological‐hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias‐correct the climatological‐hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500‐year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias‐corrected climatological‐hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500‐year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2‐4.5 and SSP5‐8.5, respectively by the end of the 21st century.

Tropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme wind, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics‐based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics‐based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high‐resolution wind data sets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950 to 2020 using a basin‐scale hydrodynamic model and show that our reconstructions agree well with observed peak storm tide and storm surge. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides/surges can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts.

Accurate delineation of compound flood hazard requires joint simulation of rainfall‐runoff and storm surges within high‐resolution flood models, which may be computationally expensive. There is a need for supplementing physical models with efficient, probabilistic methodologies for compound flood hazard assessment that can be applied under a range of climate and environment conditions. Here we propose an extension to the joint probability optimal sampling method (JPM‐OS), which has been widely used for storm surge assessment, and apply it for rainfall‐surge compound hazard assessment under climate change at the catchment‐scale. We utilize thousands of synthetic tropical cyclones (TCs) and physics‐based models to characterize storm surge and rainfall hazards at the coast. Then we implement a Bayesian quadrature optimization approach (JPM‐OS‐BQ) to select a small number (∼100) of storms, which are simulated within a high‐resolution flood model to characterize the compound flood hazard. We show that the limited JPM‐OS‐BQ simulations can capture historical flood return levels within 0.25 m compared to a high‐fidelity Monte Carlo approach. We find that the combined impact of 2100 sea‐level rise (SLR) and TC climatology changes on flood hazard change in the Cape Fear Estuary, NC will increase the 100‐year flood extent by 27% and increase inundation volume by 62%. Moreover, we show that probabilistic incorporation of SLR in the JPM‐OS‐BQ framework leads to different 100‐year flood maps compared to using a single mean SLR projection. Our framework can be applied to catchments across the United States Atlantic and Gulf coasts under a variety of climate and environment scenarios.