Sensitivity of Storm Surge Predictions to Atmospheric Forcing during Hurricane Isaac

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

JC Dietrich, A Muhammad, M Curcic, A Fathi, CN Dawson, SS Chen, RA Luettich (2018). “Sensitivity of Storm Surge Predictions to Atmospheric Forcing during Hurricane Isaac.Journal of Waterway, Port, Coastal, and Ocean Engineering, 144(1), DOI: 10.1061/(ASCE)WW.1943-5460.0000419

Seminar: UNC Department of Geological Sciences

News: Prediction of and Resilience Against Extreme Events

2017/09/12 – National Science Foundation
In wake of hurricanes … NSF awards $18.7 million in natural hazards research grants

In the decade from 2003 to 2013, natural disasters around the globe caused $1.5 trillion in economic damages and took the lives of almost 1.2 million people. Over that same 10-year period, the U.S. lost nearly $650 billion due to such disasters.

How can scientists better predict or prevent such catastrophes? How can they help people recover more quickly?

To find answers to these questions, the National Science Foundation (NSF) has awarded 15 new grants totaling $18.7 million through its PREEVENTS (Prediction of and Resilience Against Extreme Events) program. PREEVENTS is part of NSF’s Risk and Resilience portfolio.

PREEVENTS’ goals are to improve predictability and risk assessments of natural hazards, increase resilience to these events, and reduce their effects on human lives, societies and economies. PREEVENTS also supports research that will improve the understanding of the processes underlying natural hazards and extreme events.