ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 51 Issue 8 > 2025,51(8):954-963. DOI:10.7519/j.issn.1000-0526.2025.041602 Prev Next
Sea Fog Characteristics and Forecast Evaluation of Classification Decision Tree Models at Yangshan Port
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Abstract:
Based on the data from automatic weather stations and buoy observation stations, and ERA5 reanalysis data from 2015 to 2023, this article analyzes the characteristics of sea fog at Yangshan Port. Classification decision tree models are trained and constructed based on a comprehensive case database of sea fog events. Their forcast results are compared with those of the ECMWF model. The results indicate that the year 2016 has the highest number of foggy days, with spring and early summer being the peak season, followed by winter. During dense fog events at Yangshan Station, the dominant wind directions change from northeast to north and southeast. Southeast winds prevail during non-precipitation periods, while north winds dominate during precipitation. Monthly wind patterns alter from predominantly northerly in winter to northeasterly and southeasterly in spring. Wind direction and speed varied at different stages of sea fog. In the developing stage of sea fog, southeast winds are dominant; during the mature stage, northeast winds prevail; and during the dissipating stage, north winds dominat with high speeds. Fog events accompanied by precipitation are more frequent and long time lasting. The classification decision tree models have identified the temperature-dewpoint spread as a key factor in the formation of various sea fog types. Decision tree models demonstrate a lower miss rate and higher prediction performance than the ECMWF model, particularly in forecasting the formation and duration of advection fog. This can provide valuable insights for forecasting frontal and radiation fog events.
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