A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia

Abstract

Abstract Equatorial plasma bubble (EPB) irregularities can significantly impact satellite‐based communication and navigation systems. Accurate prediction of EPB occurrence is essential for mitigating these impacts. Using the GNSS receiver network and ionosonde data from East and Southeast Asia during 2010–2021, and the rate of TEC change index to characterize the occurrence of EPB irregularities, we developed a novel Spatio‐Temporal deep learning model for regional EPB irregularities short‐term Prediction (STEP). The model integrates the convolutional neural network and long short‐term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities. The results show that for 5‐min forecast, the STEP model achieves a root mean square error (RMSE) of 0.062 TECU/min and an R2 of 0.818, reducing RMSE by 19.48% compared to LSTM and 27.06% compared to gated recurrent unit model. For 60‐min prediction, the STEP model can still achieve reasonable accuracy with an RMSE of 0.110 TECU/min and an R2 of 0.482, showing significant improvement over traditional models. The equatorial F layer height and regional TEC fluctuations were identified as the most critical factors for predicting the generation and duration of EPB irregularities, respectively. The spatial and temporal distributions of EPB irregularities, including their latitudinal variation and delayed onset after sunset, and the occurrence across different days in East and Southeast Asia, were well predicted by the STEP. It is expected that the STEP model would provide a valuable tool for improving the resilience of GNSS against ionospheric scintillations induced by EPB irregularities.

Keywords

• equatorial plasma bubble
• ionospheric irregularity
• spatio‐temporal deep learning
• prediction model