AI-Based Improvement of IRI-2020 Electron Density Profiles With COSMIC Radio Occultation Data

Abstract

In this study, we propose an AI-based method to improve the electron density profiles generated by the International Reference Ionosphere (IRI)-2020 model using the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) data. Specifically, we employ a Multi-Layer Perceptron (MLP), a type of artificial neural network (ANN), which learns to transform IRI-2020 profiles into COSMIC-like profiles using paired data collected between 2007 and 2019. The data set is divided into training (2007-2013), validation (2014, 2019), and test (2015-2018) subsets. Using the test set, we evaluate the performance of our model by calculating the correlation coefficient (CC) and root mean square error (RMSE) between the model outputs and COSMIC electron density profiles. The results show that our model outperforms the IRI model, yielding higher CCs and lower RMSEs. The model demonstrates consistent improvements across various geomagnetic conditions and geographic regions, particularly in low- and mid- latitudes. Further evaluation using incoherent scatter radar (ISR) data from two stations indicates that both our model and the IRI model show comparable performance in capturing the vertical structure of the ionosphere. These findings demonstrate that machine learning techniques, such as MLPs, provide an effective means of leveraging satellite-based observational data to improve the performance of empirical models such as the IRI model.