Year: 2025
Type: article
Abstract: Predicting global ionospheric total electron content (TEC) is critical for high-precision GNSS applications, but some existing models fail to jointly capture spatial heterogeneity and multiscale temporal trends. To address the problem, this work proposes a spatio-temporal graph neural network (STGNN) that addresses these limitations through (1) a trainable positional attention mechanism to dynamically infer node dependencies without fixed geographical constraints and (2) a GRU–Transformer sequential module to hierarchically model local and global temporal patterns. The proposed network is validated by different solar and geomagnetic activities. With a training dataset with a time span between 2008 and 2018, the proposed model is tested in a high solar phase for the year 2015 and a low solar phase for the year 2018. For 2015, the experimental results show a 21.9% RMSE reduction at low latitudes compared to the results of the iTransformer model. For the geomagnetic storm event, the proposed STGNN achieves 16.0% higher stability. For the one-week (84 step) prediction test, the STGNN shows a 27.0% lower error compared to the MLPMultivariate model. The model’s self-adaptive spatial learning and multiscale temporal modeling uniquely enable TEC forecasting under diverse geophysical conditions.
Source: Atmosphere
Authors: Yifei Chen, Yang Liu, Kunlin Yang, Lanhao Li, Chao Xiong, Jinling Wang
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: Beihang University
OpenAlex ID: W4411339008