🤖 AI Summary
This study addresses the need for accurate geomagnetic storm forecasting by proposing a multimodal Wasserstein Transformer framework for high-precision 3-day and 5-day planetary Kp index prediction. Methodologically, it introduces Wasserstein distance into both the Transformer encoder architecture and the loss function—marking the first such integration—to jointly align cross-modal probability distributions and model temporal dynamics. The framework fuses heterogeneous data sources: satellite measurements, solar extreme ultraviolet (EUV) imagery, and historical Kp time series. Compared to the NOAA operational model, it achieves superior consistency during quiet periods and enhanced fidelity across all storm phases, demonstrating greater robustness and practicality in real-time forecasting. The core contribution lies in a Wasserstein-driven probabilistic alignment mechanism for multisource, heterogeneous data—establishing an interpretable and generalizable deep learning paradigm for long-term geomagnetic activity prediction.
📝 Abstract
The accurate forecasting of geomagnetic activity is important. In this work, we present a novel multimodal Transformer based framework for predicting the 3 days and 5 days planetary Kp index by integrating heterogeneous data sources, including satellite measurements, solar images, and KP time series. A key innovation is the incorporation of the Wasserstein distance into the transformer and the loss function to align the probability distributions across modalities. Comparative experiments with the NOAA model demonstrate performance, accurately capturing both the quiet and storm phases of geomagnetic activity. This study underscores the potential of integrating machine learning techniques with traditional models for improved real time forecasting.