Ionospheric Electron Density and Temperature Profiles Using Ionosonde-Like Data and Machine Learning

Predicting the behaviour of the Earth's ionosphere is crucial for ground-based and space borne technologies relying on it. This paper presents a novel way of inferring the ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Nearest Neighbor (NNB) and Radial Basis Function (RBF) regression models. Synthetic data sets used to train and validate these two inference models are constructed using the International Reference Ionosphere (IRI 2020) model with randomly chosen years (1987-2022), months (1-12), days (1-31), latitudes (-60 to 60°), longitudes (0, 360°), times (0-23h), at altitudes ranging from 95 to 600 kilometres. The NNB and RBF models use the constructed ionosonde-like profiles to infer complete ISR-like profiles. The results presented show that the inference of ionospheric electron density profiles is better with the NNB model than with the RBF model while the RBF model is better at inferring the electron temperature profiles than the NNB model. A major and unexpected finding of this research is the ability of the two models in inferring full electron temperature profiles that are not provided by ionosondes using the same truncated electron density dataset used to infer electron density profiles. NNB and RBF models generally overestimate or underestimate the inferred electron density and electron temperature values, especially at higher altitudes, but they tend to produce good matches at lower altitudes.