Short-term Forecasting Method of f0F2 in the Ionosphere over China Based on Deep Learning

As a key parameter of the ionosphere, the critical frequency of the F 2 layer of the ionosphere ( f 0 F 2 ) is of great significance for ensuring the stable operation of systems such as high-frequency radar and short-wave communication. This paper proposes a short-term forecasting method for the ionospheric f 0 F 2 based on deep learning. By using the Bidirectional Long Short-term Memory model with attention mechanism (BiLSTM-Attention) algorithm and combining the observed values of the ionospheric f 0 F 2 at the ionosonde station for the previous 7 days, Universal Time (UT), solar activity index, and geomagnetic activity index as inputs, the forecasting of the ionospheric f 0 F 2 in the Chinese region is realized. The results of the comparative analysis of the model show that: The forecasting errors for low-latitude stations were significantly higher than those for mid-latitude stations. The BiLSTM-Attention model demonstrated superior performance, followed by the Long Short-Term Memory (LSTM) model. Compared to the International Reference Ionosphere (IRI) model, the BiLSTM-Attention model achieved a 44.2% reduction in Root Mean Square Error (RMSE), 47% decrease in Mean Absolute Error (MAE), and 21.3% improvement in the Coefficient of Determination ( R 2 ). During geomagnetic storms, the BiLSTM-Attention model successfully captured the negative storm effects (characterized by f 0 F 2 depletion) in China’s regional ionosphere, showing excellent consistency with observational data. However, even when operating in storm mode, the IRI model still exhibited noticeable deviations between predicted and observed f 0 F 2 values. As the forecasting window extended from 1 hour to 24 hours, the model errors showed a systematic increasing trend: RMSE rose from 0.99 MHz to 2.05 MHz, MAE increased from 0.69 MHz to 1.57 MHz, while R 2 decreased from 0.93 to 0.75. Relevant research provides high-precision ionospheric parameter forecasting support for space weather warning and short-wave communication system optimization.