Storm surge and overland flooding can be predicted with computational models at high levels of resolution. To improve efficiency in forecasting applications, surge models often use atmospheric forcing from parametric vortex models, which represent the surface pressures and wind fields with a few storm parameters. The future of storm surge prediction could involve real-time coupling of surge and full-physics atmospheric models; thus, their accuracies must be understood in a real hurricane scenario. The authors compare predictions from a parametric vortex model (using forecast tracks from the National Hurricane Center) and a full-physics coupled atmosphere-wave-ocean model during Hurricane Isaac (2012). The predictions are then applied within a tightly coupled, wave and surge modeling system describing the northern Gulf of Mexico and the floodplains of southwest Louisiana. It is shown that, in a hindcast scenario, a parametric vortex model can outperform a data-assimilated wind product, and given reasonable forecast advisories, a parametric vortex model gives reasonable surge forecasts. However, forecasts using a full-physics coupled model outperformed the forecast advisories and improved surge forecasts. Both approaches are valuable for forecasting the coastal impacts associated with tropical cyclones.

Autonomous underwater glider observations collected during and after 2017 Hurricanes Irma, Jose, and Maria show two types of transient response within the Gulf Stream. First, anomalously fresh water observed near the surface and within the core of the Gulf Stream offshore of the Carolinas likely resulted from Irma's rainfall being entrained into the Loop Current‐Gulf Stream system. Second, Gulf Stream volume transport was reduced by as much as 40% for about 2 weeks following Jose and Maria. The transport reduction had both barotropic and depth‐dependent characteristics. Correlations between transport through the Florida Straits and reanalysis winds suggest that both local winds in the Florida Straits and winds over the Gulf Stream farther downstream may have contributed to the transport reduction. To clarify the underlying dynamics, additional analyses using numerical models that capture the Gulf Stream's transient response to multiple tropical cyclones passing nearby in a short period are needed.

Estimates of flood inundation generated by runoff and coastal flood processes during tropical cyclones (TCs) are needed to better understand how exposure varies inland and at the coast. While reduced‐complexity flood models have been previously shown to efficiently simulate TC flood processes across large regions, a lack of detailed validation studies of these models, which are being applied globally, has led to uncertainty about the quality of the predictions of inundation depth and extent and how this translates to exposure. In this study, we complete a comprehensive validation of a hydrodynamic model (SFINCS) for simulating pluvial, fluvial, and coastal flooding. We hindcast Hurricane Florence (2018) flooding in North and South Carolina, USA using high‐resolution meteorologic data and coastal water level output from an ocean recirculation model (ADCIRC). Modeled water levels are compared to traditional validation datasets (e.g., water level gages, high water marks) as well as property‐level records of insured flood damage to draw conclusions about the model's performance. SFINCS shows skill in simulating runoff and coastal processes of TC flooding (peak error of 0.11 m with an RMSE of 0.92 m) at large scales with minimal computational requirements and limited calibration. We use the validated model to attribute flood extent and building exposure to flood processes (e.g., runoff, coastal, compound) during Hurricane Florence. The results highlight the critical role runoff processes have in TC flood exposure and support the need for broader implementation of models capable of realistically representing the compound effects resulting from coastal and runoff processes.

[1] Strong and strategic collaborations among experts from academia, federal operational centers, and industry have been forged to create a U.S. IOOS Coastal and Ocean Modeling Testbed (COMT). The COMT mission is to accelerate the transition of scientific and technical advances from the coastal and ocean modeling research community to improved operational ocean products and services. This is achieved via the evaluation of existing technology or the development of new technology depending on the status of technology within the research community. The initial phase of the COMT has addressed three coastal and ocean prediction challenges of great societal importance: estuarine hypoxia, shelf hypoxia, and coastal inundation. A fourth effort concentrated on providing and refining the cyberinfrastructure and cyber tools to support the modeling work and to advance interoperability and community access to the COMT archive. This paper presents an overview of the initiation of the COMT, the findings of each team and a discussion of the role of the COMT in research to operations and its interface with the coastal and ocean modeling community in general. Detailed technical results are presented in the accompanying series of 16 technical papers in this special issue.