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 %.
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Outbreaks of Ulva prolifera green tides reduce the network complexity and stability of cooccurring planktonic microbial communities.
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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.
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