Leah Cicon, Johannes Gemmrich, Benoit Pouliot, Natacha Bernier
{"title":"从WAVEWATCH III®模式对东北太平洋异常浪的概率预测","authors":"Leah Cicon, Johannes Gemmrich, Benoit Pouliot, Natacha Bernier","doi":"10.1175/waf-d-23-0074.1","DOIUrl":null,"url":null,"abstract":"Abstract Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir Index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth controlled linear superposition. Recent studies have shown that the bandwidth parameter crest-trough correlation, r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the Northeast Pacific. This work further demonstrates that crest-trough correlation can be forecast by a regional WAVEWATCHIII ® wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r . Semi-logarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm October 21-22, 2021.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"11 1","pages":"0"},"PeriodicalIF":3.0000,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A probabilistic prediction of rogue waves from a WAVEWATCH III® model for the Northeast Pacific\",\"authors\":\"Leah Cicon, Johannes Gemmrich, Benoit Pouliot, Natacha Bernier\",\"doi\":\"10.1175/waf-d-23-0074.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir Index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth controlled linear superposition. Recent studies have shown that the bandwidth parameter crest-trough correlation, r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the Northeast Pacific. This work further demonstrates that crest-trough correlation can be forecast by a regional WAVEWATCHIII ® wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r . Semi-logarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm October 21-22, 2021.\",\"PeriodicalId\":49369,\"journal\":{\"name\":\"Weather and Forecasting\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Weather and Forecasting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1175/waf-d-23-0074.1\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Weather and Forecasting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1175/waf-d-23-0074.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
A probabilistic prediction of rogue waves from a WAVEWATCH III® model for the Northeast Pacific
Abstract Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir Index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth controlled linear superposition. Recent studies have shown that the bandwidth parameter crest-trough correlation, r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the Northeast Pacific. This work further demonstrates that crest-trough correlation can be forecast by a regional WAVEWATCHIII ® wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r . Semi-logarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm October 21-22, 2021.
期刊介绍:
Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.