Abstract

This article investigates combining a WRF-ADCIRC ensemble with track clustering to evaluate how uncertainties in tropical cyclone-induced storm tide (surge + tide) predictions vary in space and time and to explore whether this method can help elucidate inundation hazard scenarios. The method is d... Show moreThis article investigates combining a WRF-ADCIRC ensemble with track clustering to evaluate how uncertainties in tropical cyclone-induced storm tide (surge + tide) predictions vary in space and time and to explore whether this method can help elucidate inundation hazard scenarios. The method is demonstrated for simulations of Hurricane Irma (2017) initialized at 1200 UTC 5 September, approximately 5 days before Irmas Florida landfalls, and 1200 UTC 8 September. Mixture models are used to partition the WRF ensemble tracks from 5 and 8 September into six and five clusters, respectively. Inundation is evaluated in two affected regions: southwest (south and west Florida) and northeast (northeast Florida through South Carolina). For the 5 September simulations, inundation in the southwest region varies significantly across the ensemble, indicating low forecast confidence. However, clustering highlights the areas of inundation risk in south and west Florida associated with different storm tracks. In the northeast region, every cluster has high inundation probabilities along a similar coastal stretch, indicating high confidence at a similar to 5-day lead time that this area will experience inundation. For the 8 September simulations, track and inundation in both regions vary less across the ensemble, but clustering remains useful for distinguishing among flooding scenarios. These results demonstrate the potential of dynamical TC-surge ensembles to illuminate important aspects of storm surge risk, including highlighting regions of high forecast confidence where preparations can reliably be initiated early. The analysis also shows how clustering can augment probabilistic hazard forecasts by elucidating inundation scenarios and variability across a surge ensemble. Show less