Physics-Informed Machine Learning for Predicting Ionospheric Peak Parameters Using Ground- and Space-Based Observations

This study presents a comprehensive nowcasting and forecasting approach for ionospheric peak parameters, including foF2, NmF2, and TEC, using a Physics Informed Neural Network (PINN). This approach integrates multiple datasets, utilizing extensive ground-based ionosonde station measurements and COSMIC satellite observations to model and predict these parameters in relation to ionospheric conditions and space weather dynamics.

Our work also explores the response of ionospheric peak parameters to extreme solar eruptions and geomagnetic storms, providing critical insights into the behaviour of the ionosphere under these challenging conditions. The PINN incorporates fundamental physical laws as constraints, including the Chapman function, continuity equation, ion production rates as a function of F10.7, recombination reactions, geomagnetic and electric fields, and Abel inversion effects. Our model utilizes a comprehensive set of input features, including COSMIC satellite foF2, NmF2, TEC measurements, temporal and spatial parameters, and various solar and geomagnetic indices. Data normalization and a deep neural network architecture with multiple dense layers and batch normalization were employed to capture complex, non-linear relationships in the ionospheric data. These constraints enable highly accurate predictions  achieving a high average correlation of 0.92 between COSMIC satellite and ionosonde measurements.

A detailed random forest parameter importance analysis identified key contributors to ionospheric variability, revealing that atmospheric dynamics (meridional and zonal winds) and solar activity (notably F10.7) play dominant roles. Spatial and temporal factors were also considered critical compared to other space weather parameters.

These findings highlight the potential of physics-informed machine learning as a robust tool for advancing our understanding of ionospheric behaviour and improving predictive capabilities for space weather applications. Furthermore, this study underscores the value of integrating ground- and space-based observations with physical principles to achieve accurate and reliable forecasts of ionospheric peak parameters.