Efficient Probabilistic Prediction and Uncertainty Quantification of Hurricane Surge and Inundation

This study proposes a methodology for efficient probabilistic prediction of near-landfall hurricane-driven storm surge, tide, and inundation. We perturb forecasts of hurricane track, intensity, and size according to quasi-random low-discrepancy Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations, and the resulting set of maximum water surface elevations are used as a training set to develop a Polynomial Chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with 3rd order PCs using Elastic Net penalized regression with Leave-One-Out cross-validation provides the most robust fit across training and validation sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate at 48-hour lead time for three past U.S. landfalling hurricanes (Irma 2017, Florence 2018, and Laura 2020) are found to be reliable when compared to best-track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. This methodology is built into an open-source Python framework available from https://github.com/noaa-ocsmodeling/EnsemblePerturbation.