AI-driven approach to ionospheric modelling

Accurate representation of ionospheric electrodynamic forcing is essential for modeling the coupled thermosphere–ionosphere system, particularly at high latitudes where Joule heating and ion drag dominate the energy and momentum budget. These processes play a central role in controlling thermospheric temperature, neutral winds, and density variability. In the ICOsahedral Nonhydrostatic (ICON) model, ionospheric forcing is typically parameterized using empirical inputs or simplified formulations, which do not fully capture the complex spatial and temporal variability associated with magnetosphere–ionosphere coupling.

In this work, we develop a machine learning–based parameterization of Joule heating and ion drag using a convolutional neural network architecture. The model is trained on outputs from the Whole Atmosphere Community Climate Model with thermosphere–ionosphere extension (WACCM-X), enabling it to learn nonlinear relationships between electrodynamic drivers and the resulting thermospheric response. A U-Net architecture is employed to capture both local structures and large-scale spatial patterns, which are essential for representing high-latitude electrodynamic variability.

To enable deployment within a different dynamical core, a grid conversion framework is developed to map data between the structured latitude–longitude grid of WACCM-X and the unstructured triangular grid used in ICON. This mapping ensures physical consistency of the input and output fields while preserving spatial coherence across resolutions. The trained model is evaluated on independent time periods outside the training dataset and demonstrates strong skill in reproducing both the spatial distribution and magnitude of Joule heating and ion drag across multiple altitude levels.

The proposed approach provides a computationally efficient alternative to traditional parameterizations, reducing reliance on empirical inputs while retaining high accuracy. By capturing complex electrodynamic variability, this method offers improved representation of high-latitude forcing in global models. This work represents a step toward hybrid modeling frameworks in which machine learning augments first-principles approaches to enhance the fidelity of upper-atmosphere simulations.