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<p>Sea fog is a critical meteorological phenomenon that affects production safety and operational efficiency at Ningbo–Zhoushan Port. To improve fine-grained sea fog forecasting in the port area, this study constructs a dynamic graph by leveraging the strengths of graph neural networks in processing irregularly distributed meteorological station data. The graph is designed based on station locations and wind direction/speed, incorporating the advection term in the atmospheric motion equation. On this basis, a novel forecasting framework is developed that integrates graph attention networks, long short-term memory, and multistep classification output, to train a sea fog forecasting model. The proposed model provides hourly rolling 24-h sea fog forecasts for 72 stations across the port area. Operational validation in 2023 demonstrates its robust performance in forecasting fog occurrence. For 24-h fog occurrence forecasts, the model achieves a threat score (TS) of 0.369 (F1 = 0.539), with an average TS of 0.190 for fixed-time forecasts. The forecasting performance exhibits a typical temporal decay pattern, reaching its highest TS (0.441) at the 1-h lead time. Furthermore, the model demonstrates superior skill in predicting localized patchy fog and fog evolution, outperforming conventional machine learning approaches such as random forest and Gaussian naive Bayes.</p>

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An Hourly Rolling Sea Fog Forecasting Model for Ningbo–Zhoushan Port Based on Dynamic Graph Neural Networks

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Junbo Sun,
  • Yue Sun,
  • Yanzhen Qian,
  • Yanying Fang,
  • Dongdong Wang,
  • Changyu Zhao

摘要

Sea fog is a critical meteorological phenomenon that affects production safety and operational efficiency at Ningbo–Zhoushan Port. To improve fine-grained sea fog forecasting in the port area, this study constructs a dynamic graph by leveraging the strengths of graph neural networks in processing irregularly distributed meteorological station data. The graph is designed based on station locations and wind direction/speed, incorporating the advection term in the atmospheric motion equation. On this basis, a novel forecasting framework is developed that integrates graph attention networks, long short-term memory, and multistep classification output, to train a sea fog forecasting model. The proposed model provides hourly rolling 24-h sea fog forecasts for 72 stations across the port area. Operational validation in 2023 demonstrates its robust performance in forecasting fog occurrence. For 24-h fog occurrence forecasts, the model achieves a threat score (TS) of 0.369 (F1 = 0.539), with an average TS of 0.190 for fixed-time forecasts. The forecasting performance exhibits a typical temporal decay pattern, reaching its highest TS (0.441) at the 1-h lead time. Furthermore, the model demonstrates superior skill in predicting localized patchy fog and fog evolution, outperforming conventional machine learning approaches such as random forest and Gaussian naive Bayes.