{"title":"Research on the ensemble forecasting method for green tide paths in the Yellow Sea based on parameter perturbation","authors":"Yinlin Zhu, Yuheng Wang, Liang Zhao","doi":"10.1016/j.marpolbul.2025.117717","DOIUrl":null,"url":null,"abstract":"<div><div>Yellow Sea green tides have become recurring marine ecological disasters, making accurate forecasting essential for early warning and preventive measures. This study incorporates a stochastic perturbed parameterization scheme into the Yellow Sea Green Tide Drift Model to create an ensemble forecast of the drifting path of green tides, using the 2016 Yellow Sea green tide event as a case study. The ensemble forecast experiment demonstrates that this approach effectively simulates the drift characteristics of the 2016 green tide. Validation with Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data reveals that the ensemble forecast significantly enhances the forecasting performance for periods exceeding 15 days, with the average absolute error in the forecast path reduced by 32 %.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"214 ","pages":"Article 117717"},"PeriodicalIF":5.3000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Marine pollution bulletin","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0025326X25001924","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Yellow Sea green tides have become recurring marine ecological disasters, making accurate forecasting essential for early warning and preventive measures. This study incorporates a stochastic perturbed parameterization scheme into the Yellow Sea Green Tide Drift Model to create an ensemble forecast of the drifting path of green tides, using the 2016 Yellow Sea green tide event as a case study. The ensemble forecast experiment demonstrates that this approach effectively simulates the drift characteristics of the 2016 green tide. Validation with Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data reveals that the ensemble forecast significantly enhances the forecasting performance for periods exceeding 15 days, with the average absolute error in the forecast path reduced by 32 %.
期刊介绍:
Marine Pollution Bulletin is concerned with the rational use of maritime and marine resources in estuaries, the seas and oceans, as well as with documenting marine pollution and introducing new forms of measurement and analysis. A wide range of topics are discussed as news, comment, reviews and research reports, not only on effluent disposal and pollution control, but also on the management, economic aspects and protection of the marine environment in general.