Citation

Kyprioti AP, Adeli E, Taflanidis AA, Westerink JJ, Tolman HL. J. Marine Sci. Eng. 2021; 9(12): e1322.

Copyright

(Copyright © 2021, Australia New Zealand Marine Biotechnology Society, Publisher MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/jmse9121322

PMID

unavailable

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 fac- torial 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 seam- less extension to the current NHC framework, adopting a quasi-Monte Carlo (QMC) sampling im- plementation 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 ad- vantages QMC can offer.
Keywords: landfalling storms; probabilistic storm surge estimation; forecast errors; quasi-Monte Carlo

Language: en

Keywords

n/a