电波科学学报2026，Vol.41Issue(4)：736-747,757,13.DOI:10.12265/j.cjors.2025211
一种基于历史气象数据的蒸发波导高度实时预测方法
A real-time prediction method for evaporation duct height based on historical meteorological data
摘要
Abstract
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.关键词
蒸发波导/超参数优化/Informer模型/NPS模型/NGO-BO算法/物理信息神经网络(PINN)模型Key words
evaporation duct/hyperparameter optimization/Informer model/NPS model/northern goshawk optimization and Bayesian optimization(NGO-BO)algorithm/physics-informed neural network(PINN)model分类
天文与地球科学引用本文复制引用
史振东,刘祎,王翔,张玉强..一种基于历史气象数据的蒸发波导高度实时预测方法[J].电波科学学报,2026,41(4):736-747,757,13.基金项目
国家自然科学基金青年科学基金(42204161) （42204161）
湖北省重点研发计划项目(2025BAB061) （2025BAB061）
湖北省重大攻关项目(2025BEA001) （2025BEA001）
武汉市自然科学基金聚力计划(2026040101030022)National Natural Science Foundation of China for Young Scientists(42204161) （2026040101030022）
Hubei Provincial Key Research and Development Program(2025BAB061) （2025BAB061）
Hubei Provincial Major Science and Technology Program(2025BEA001) （2025BEA001）
Wuhan Natural Science Foundation Focused Program(2026040101030022) （2026040101030022）
