12 hr Forecast of the SYM-H Index under Strong Southward Interplanetary Magnetic Field Conditions Using Deep Learning

In this study, we propose a deep learning model to forecast the SYM-H index up to 12 hr ahead under strong southward interplanetary magnetic field (IMF) B_z conditions (B_z ≤ −3 nT for at least 6 hr), which are well known as the main driver of geomagnetic storms. The input data consist of 5 minute resolution solar wind parameters (IMF components, solar wind speed, density, dynamic pressure, and electric field) from OMNIWeb, averaged over 30 minute intervals, with a 12 hr lookback window. Our proposed model adopts a multilayer-perceptron-based encoder–decoder architecture following the Time-series Dense Encoder framework. Our results are as follows. First, our model achieves comparable or better performance in terms of root mean square error and correlation coefficient relative to previous Dst and SYM-H prediction models. Second, our model successfully predicts the minimum SYM-H value and its timing within the 12 hr forecast window. Third, we demonstrate that the model produces reliable SYM-H forecast profiles during storm periods or even beyond the strict southward B_z criterion used for training. To our knowledge, this is the first study to forecast a geomagnetic index under strong IMF southward conditions, which is an explicit condition on the state of the primary storm driver, and we expect that this approach can serve as a useful tool for space-weather forecasting.