Return to search

A probabilistic prediction of rogue waves from a spectral WAVEWATCH III ® wave model for the Northeast Pacific

Rogue waves are unexpected, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures when encountered in large seas. Rogue waves have gone from seafarer’s folktales to an actively researched and debated phenomenon. In this work an easily derived spectral parameter, as an indicator of rogue wave risk, is presented, and further evidence for the generation mechanism responsible for these abnormal waves is provided. With the additional goal of providing a practical rogue wave forecast, the ability of a standard wave model to predict the rogue wave probability is assessed. Current forecasts, like those at the European Centre for Medium-Range Weather Forecasts (ECMWF), rely on the Benjamin Feir Index (BFI) as a rogue wave predictor, which reflects the nonlinear process of modulation instability as the generation mechanism for rogue waves. However, this analysis finds BFI has little predictive power in the real ocean. From the analysis of long term sea surface elevation records in nearshore areas and hourly bulk statistics from open ocean and coastal buoys in the Northeast Pacific, crest-trough correlation shows the highest correlation with rogue wave probability. These results provide evidence in support of a probabilistic prediction of rogue waves based on random linear superposition and should replace forecasts based on modulation instability. Crest-trough correlation was then forecast by a regional WAVEWATCH III ® wave model with moderate accuracy compared with the high performance of forecasting significant wave height. Results from a case study of a large fall storm October 21-22, 2021, are presented to show that the regional wave model produces accurate forecasts of significant wave height at high seas and presents a potential rogue wave probability forecast. / Graduate

Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/14257
Date22 September 2022
CreatorsCicon, Leah
ContributorsGemmrich, Johannes, Klymak, Jody Michael
Source SetsUniversity of Victoria
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf
RightsAvailable to the World Wide Web

Page generated in 0.0023 seconds