Six-hour Prediction of Interplanetary Magnetic Field Bz Profiles for Strong Southward Cases by Deep Learning - Jihyeon Son; Yong-Jae Moon; Young-Sil Kwak; Kyung Sun Park; Hyun-Jin Jeong (The Astrophysical Journal)
In this study, we develop deep learning models to forecast the 6 hr interplanetary magnetic field (IMF) B _z component for southward cases. The models are based on a bidirectional long short-term memory method, and input parameters are solar wind data ( V , N , T ) and IMF components ( B _t , B _x , B _y , B _z ). The data are obtained from OMNI, whose period is from 2000 to 2022. We use the preceding 12 hr of data as input and the subsequent 6 hr of B _z data as target. To focus on strong geomagnetic conditions, we consider periods where B _z values drop below the negative standard deviation (approximately −3 nT) for at least 6 hr. The models are trained and validated using a 12-fold cross-validation process, with each model trained over 8 months of data and tested over 4 months. The ensemble model, which averages 12-fold model results, achieves an RMSE ranging from 1.75 (30 minutes prediction) to 2.55 nT (6 hr prediction), significantly outperforming two baseline methods: multilayer perceptron and multiple linear regression. Our model can capture both decreasing and increasing phases of B _z , showing reliable performance across varying geomagnetic conditions. Our results suggest a sufficient possibility for predicting B _z under noticeable southward conditions. We expect that our model can be used for subsequent space weather predictions, such as global magnetohydrodynamic simulations in the magnetosphere.