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1. Assessing North Atlantic Tropical Cyclone Rainfall Hazard Using Engineered-Synthetic Storms and a Physics-Based Tropical Cyclone Rainfall Model

   https://doi.org/10.1175/JAMC-D-22-0131.1  

   Xi, Dazhi; Lin, Ning; Nadal-Caraballo, Norberto C.; Yawn, Madison C. (August 2023, Journal of Applied Meteorology and Climatology)

   Abstract In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment. 

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2. Tropical cyclone climatology change greatly exacerbates US extreme rainfall–surge hazard

   https://doi.org/10.1038/s41558-021-01272-7  

   Gori, Avantika; Lin, Ning; Xi, Dazhi; Emanuel, Kerry (February 2022, Nature Climate Change)

   Abstract Tropical cyclones (TCs) are drivers of extreme rainfall and surge, but the current and future TC rainfall–surge joint hazard has not been well quantified. Using a physics-based approach to simulate TC rainfall and storm tides, we show drastic increases in the joint hazard from historical to projected future (SSP5–8.5) conditions. The frequency of joint extreme events (exceeding both hazards’ historical 100-year levels) may increase by 7–36-fold in the southern US and 30–195-fold in the Northeast by 2100. This increase in joint hazard is induced by sea-level rise and TC climatology change; the relative contribution of TC climatology change is higher than that of sea-level rise for 96% of the coast, largely due to rainfall increases. Increasing storm intensity and decreasing translation speed are the main TC change factors that cause higher rainfall and storm tides and up to 25% increase in their dependence. 

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3. Projecting Compound Flood Hazard Under Climate Change With Physical Models and Joint Probability Methods

   https://doi.org/10.1029/2022EF003097  

   Gori, Avantika; Lin, Ning (December 2022, Earth's Future)

   Abstract 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. 

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4. Sensitivity of tropical cyclone risk across the US to changes in storm climatology and socioeconomic growth

   https://doi.org/10.1088/1748-9326/add60d  

   Gori, Avantika; Lin, Ning; Chavas, Daniel_R; Oppenheimer, Michael; Xian, Siyuan (May 2025, Environmental Research Letters)

   Abstract 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) 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 expected annual damage across the US (roughly 160%), and that their combined effect (633% increase) is much higher. 

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5. Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity

   https://doi.org/10.1175/JCLI-D-23-0475.1  

   Xi, Dazhi; Lin, Ning; Jing, Renzhi; Harr, Patrick; Oppenheimer, Michael (June 2024, Journal of Climate)

   North Atlantic tropical cyclone (TC) activity under a high-emission scenario is projected using a statistical synthetic storm model coupled with nine Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The ensemble projection shows that the annual frequency of TCs generated in the basin will decrease from 15.91 (1979-2014) to 12.16 (2075-2100), and TC activity will shift poleward and coast-ward. The mean of lifetime maximum intensity will increase from 66.50 knots to 75.04 knots. Large discrepancies in TC frequency and intensity projections are found among the nine CMIP6 climate models. The uncertainty in the projection of wind shear is the leading cause of the discrepancies in the TC climatology projection, dominating the uncertainties in the projection of thermodynamic parameters such as potential intensity and saturation deficit. The uncertainty in the projection of wind shear may be related to the different projections of horizontal gradient of vertically integrated temperature in the climate models, which can be induced by different parameterizations of physical processes including surface process, sea ice, and cloud feedback. Informed by the uncertainty analysis, a surrogate model is developed to provide the first-order estimation of TC activity in climate models based on large-scale environmental features. 

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