European Space Weather Week 2025

Modeling of ionospheric electron density and electron temperature profiles using machine learning

Not scheduled

20m

Idun

Poster CD1 - Combination of physics-based and data-driven methods for space weather forecasting CD1 - Combination of physics-based and data-driven methods for space weather forecasting

Speaker

Jean de Dieu Nibigira (University of Alberta)

Description

Ionospheric electron density and electron temperature affect the telecommunication and navigation/surveying systems such as the Global Navigation Satellite Systems (GNSS). In this study, their inference is based on the Nearest Neighbor (NNB) and Radial Basis Function (RBF) regression models. Synthetic data sets are constructed using data from 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 show that the inference of ionospheric electron density profiles is better with the NNB model than with the RBF model. However, the RBF model is better at inferring the electron temperature profiles than the NNB model. An unexpected finding of this research is the ability to infer full electron temperature profiles that are not provided by ionosondes using the same truncated electron density dataset used to infer electron density profiles. The 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.