Forecasting Local Ionospheric Parameters Using Transformers
This addresses the problem of accurate ionospheric forecasting for applications in satellite communications and navigation, representing an incremental improvement over existing methods.
The paper tackles forecasting ionospheric parameters like foF2, hmF2, and TEC using a transformer-based model called LIFT, which provides 24-hour forecasts with uncertainty quantification and outperforms the International Reference Ionosphere (IRI) benchmark.
We present a novel method for forecasting key ionospheric parameters using transformer-based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2-layer peak plasma frequency (foF2), the F2-layer peak density height (hmF2), and total electron content (TEC) for a given geographic location. It supports a number of exogenous variables, including F10.7cm solar flux and disturbance storm time (Dst). We demonstrate how transformers can be trained in a data assimilation-like fashion that use these exogenous variables along with naïve predictions from climatology to generate 24-hour forecasts with non-parametric uncertainty bounds. We call this method the Local Ionospheric Forecast Transformer (LIFT). We demonstrate that the trained model can generalize to new geographic locations and time periods not seen during training, and we compare its performance to that of the International Reference Ionosphere (IRI).