Six-hour Prediction of Interplanetary Magnetic Field Bz Profiles for Strong Southward Cases by Deep Learning

Abstract

In this study, we develop deep learning models to forecast the 6 hr interplanetary magnetic field (IMF) Bz 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 (Bt, Bx, By, Bz). 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 Bz data as target. To focus on strong geomagnetic conditions, we consider periods where Bz 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 perception and multiple linear regression. Our model can capture both decreasing and increasing phases of Bz, showing reliable performance across varying geomagnetic conditions. Our results suggest a sufficient possibility for predicting Bz 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.