The Short-Term Prediction of Low-Latitude Ionospheric Irregularities Leveraging a Hybrid Ensemble Model

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-15
• Main Authors: Liu, Hang, Yang, Pengxin, Ren, Xiaodong, Mei, Dengkui, Le, Xuan, Zhang, Xiaohong, Freeshah, Mohamed
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Communication; Ensemble model; Equatorial regions; F 2 region; Forecasting; Geomagnetism; Global navigation satellite system; global navigation satellite systems (GNSS); Indexes; Interplanetary magnetic field; Ionization; ionospheric irregularities; Irregularities; Latitude; Machine learning; Magnetic field; Magnetic fields; Mathematical models; Navigation; Navigation systems; Navigational satellites; Prediction algorithms; Predictive models; rate of total electron content index (ROTI); Receivers; short-term prediction
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3346449

Accurate and timely forecasting of ionospheric irregularities is of great significance for the reliable and stable operation of global high-precision communication and navigation systems at low latitudes. In this study, we implement a hybrid ensemble model (HEM) that combines multiple machine learning models for forecasting the occurrence and intensity of ionospheric irregularities instead of considering ionospheric irregularity forecasting as classifications. Meanwhile, this model is trained with the GNSS-derived rate of total electron content index (ROTI) maps from approximately 147 ground-based global navigation satellite systems (GNSS) receivers in Brazil sector (35° S-5° N, 30° W-75° W). Meanwhile, a diverse set of input features, including interplanetary magnetic field (IMF) components, F2 layer critical frequency (foF2), peak height F2-layer (hmF2), F10.7, flow pressure, and SYM-H indices, are carefully selected during January 1, 2022 to 15 October 31, 2022. Regarding the relative importance of various input features, results demonstrate that the performance of the HEM model trained by the ROTI and hmF2 observations for predicting ionospheric irregularities is superior to that of other input features. Furthermore, the deviations of forecasting ionospheric irregularities from the HEM model occur mainly in the southern equatorial ionization anomaly (EIA) regions, and the accuracy of the HEM model with daily standard deviation (STD) and root mean square (rms) is less than 0.1 TECU/min. Hence, the HEM model is more stable and greater than other predicted models. Additionally, the HEM algorithm can forecast the ionospheric irregularity structures and intensity for 30 min over Brazilian territory. It is expected to improve the accuracy of short-term ionospheric irregularities forecasting at low latitudes.