Abstract
During landfalling tropical storms, predictions of the expected storm surge are critical for guiding evacuation and emergency response/preparedness decisions, both at regional and national levels. Forecast errors related to storm track, intensity, and size impact these predictions and, thus, should be explicitly accounted for. The Probabilistic tropical storm Surge (P-Surge) model is the established approach from the National Weather Service (NWS) to achieve this objective. Historical forecast errors are utilized to specify probability distribution functions for different storm features, quantifying, ultimately, the uncertainty in the National Hurricane Center advisories. Surge statistics are estimated by using the predictions across a storm ensemble generated by sampling features from the aforementioned probability distribution functions. P-Surge relies, currently, on a full factorial sampling scheme to create this storm ensemble, combining representative values for each of the storm features. This work investigates an alternative formulation that can be viewed as a seamless extension to the current NHC framework, adopting a quasi-Monte Carlo (QMC) sampling implementation with ultimate goal to reduce the computational burden and provide surge predictions with the same degree of statistical reliability, while using a smaller number of sample storms. The definition of forecast errors adopted here directly follows published NWS practices, while different uncertainty levels are considered in the examined case studies, in order to offer a comprehensive validation. This validation, considering different historical storms, clearly demonstrates the advantages QMC can offer.
Highlights
Prediction of storm-related impacts within prevention, emergency management, and post-disaster settings has emerged as a key priority in natural hazard risk mitigation efforts [1,2]
This paper investigates an alternative sampling for the storm ensemble, using a quasiMonte Carlo (QMC) formulation [23,24], with ultimate goal to reduce the computational burden: Provide statistical estimates for the surge with the same degree of accuracy, while using a smaller number of sample storms
Two different storm ensemble sampling implementations are considered: the quasi-Monte Carlo (QMC), utilizing the already defined set Xqmc, and the full factorial sampling adopted in Probabilistic tropical storm Surge (P-Surge), utilizing the set X ps
Summary
Prediction of storm-related impacts within prevention (pre-disaster), emergency management, and post-disaster settings has emerged as a key priority in natural hazard risk mitigation efforts [1,2]. P-Surge uses the National Hurricane Center’s (NHC) official advisory for the storm current/forecasted features (such as track/intensity/size information), along with historical errors associated with the forecasts to provide probabilistic estimates for the anticipated storm surge. The focus of this paper is strictly on the uncertainty propagation component of this problem, i.e., the sample-based estimation of the storm surge statistics of interest, and not on the characterization of these uncertainties (forecast error description), or on the accuracy of the storm surge numerical predictions. The use of a storm surge surrogate model is able to accommodate the described challenges for the validation case studies that are considered here It should be stressed, though, that the authors are not suggesting there is a need to replace the currently used SLOSH model with a surrogate for the probabilistic NWS predictions.
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