一种基于历史气象数据的蒸发波导高度实时预测方法OA

To achieve rapid and accurate prediction of evaporation duct height(EDH)over the South China Sea,this paper proposes a real-time prediction method based on historical meteorological data.The method consists of a two-stage framework combining a time-series forecasting module and a physics-informed diagnostic module.A hybrid optimizer integrating northern goshawk optimization(NGO)and Bayesian optimization(BO),termed NGO-BO,is used to automatically tune the hyperparameters of the Informer model for high-precision meteorological forecasting.The physical equations of the classical NPS model are then embedded into the loss function of a physics-informed neural network(PINN)to construct the PINN-NPS diagnostic model,which estimates EDH using the predicted meteorological parameters.Experiments on meteorological data from Yongxing Island in the South China Sea show that the proposed NGO-BO-Informer achieves a mean absolute error(MAE)of 0.458 2 m and a coefficient of determination(R2)of 0.995 9,while the PINN-NPS model attains an MAE of 1.32 m and an R2 of 0.73.The results confirm the effectiveness and practical value of the proposed approach for real-time EDH prediction under complex marine boundary layer conditions,providing useful references for maritime communication,radar warning,and electromagnetic environment monitoring.