赤道电离异常区电离层VTEC高精度估算与预报新技术——以海口站为例

Remote. Sens. Pub Date : 2023-07-04 DOI:10.3390/rs15133394
Haining Wang, Qinglin Zhu, Xiang Dong, Dongsheng Sheng, Yong-feng Zhi, Chen Zhou, Bin Xu
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引用次数: 0

摘要

本文介绍了一种利用GNSS观测数据利用克里格-卡尔曼方法估算电离层垂直总电子含量(VTEC)的新技术。该技术为验证赤道电离异常区内电离层VTEC分析的准确性提供了一种方法。该技术将GNSS VTEC与太阳射电通量(F10.7 cm)、扰动风暴时间(Dst)等太阳参数以及长短期记忆(LSTM)网络相结合,预测电离层赤道异常发生时间和电离层VTEC变化。将LSTM方法应用于海口站GNSS数据。与神经网络(NN)模型和国际参考电离层模型的比较表明,LSTM在VTEC估计和预测方面优于它们。赤道距平区外GNSS VTEC与GIM VTEC的均方根误差(RMSE)为1.42 TECU,赤道电离距平区内北斗静止轨道卫星GNSS VTEC与GIM VTEC的均方根误差为1.92 TECU。该方法可用于实时空间作战中VTEC的预测和估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Technique for High-Precision Ionospheric VTEC Estimation and Prediction at the Equatorial Ionization Anomaly Region: A Case Study over Haikou Station
This paper introduces a novel technique that uses observation data from GNSS to estimate the ionospheric vertical total electron content (VTEC) using the Kriging–Kalman method. The technique provides a method to validate the accuracy of the Ionospheric VTEC analysis within the Equatorial Ionization anomaly region. The technique developed uses GNSS VTEC alongside solar parameters, such as solar radio flux (F10.7 cm), Disturbance Storm Time (Dst) and other data, and Long Short Term Memory (LSTM) Networks to predict the occurrence time of the ionospheric equatorial anomaly and ionospheric VTEC changes. The LSTM method was applied to GNSS data from Haikou Station. A comparison of this technique with the neural network (NN) model and International Reference Ionosphere model shows that the LSTM outperforms all of them at VTEC estimation and prediction. The results, which are based on the root mean square error (RMSE) between GNSS VTEC and GIM VTEC outside the equatorial anomaly region, was 1.42 TECU, and the results of GNSS VTEC and VTEC from Beidou geostationary orbit satellite, which lies inside the equatorial ionization anomaly region, was 1.92 TECU. The method developed can be used in VTEC prediction and estimation in real time space operations.
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