电离层电子总含量不同时间尺度的预报模型研究

Research on different time-scale prediction models for the total electron content

电离层对无线电通信、卫星导航有重要的影响,因此对电离层电子总含量(total electron content,TEC)的预报研究十分重要,而目前国际上的各种经验电离层预报模型的精度只有60%左右,不能满足实际需求.本文提出一种新的TEC预报模型:利用经验正交函数对TEC数据进行时空分解,利用遗传算法结合混沌预测的思想对时间场系数进行非线性时间序列预测,从而达到对TEC数据预报的目的.实验结果表明,此方法可较好地对TEC数据进行不同时间尺度的预测,提前1,2,4,7 d的预报精度分别达到0.32,0.48,0.68,0.94 TECU.

In the solar-terrestrial space environment, the ionosphere couples tightly with the upper magnetic layer as well as the lower middle atmosphere in various forms. Meanwhile, the ionosphere can affect radio-communication and satellite navigations, so the research on ionosphere prediction model is very important. Now, the accuracy of statistic prediction mode is about 60%, but cannot meet the practical requirements. In order to solve the problem, the prediction model of total electron content （TEC） data is achieved in three major phases： decomposition of the spatiotemporal variability of the TEC data, noise reduction of the encoded space, and time variability and the prediction, by a nonlinear forecasting technique of the time variability. Experiments show that the new prediction model is better than traditional prediction model. The prediction data shows realistic features and a reliable physical distribution, and the relative accuracies of prediction for 1, 2, 4, and 7 d obtained by our method is 0.32, 0.48, 0.68 and 0.94 TECU.