A Low-Latitude Three-Dimensional Ionospheric Electron Density Model Based on Radio Occultation Data Using Artificial Neural Networks With Prior Knowledge

The accurate estimation of electron density in the ionosphere is crucial for various applications including remote sensing systems, communication, satellite positioning, and navigation. Previous ionospheric models using artificial neural networks (ANN) are only data-driven, whose effects are entirely affected by observational data. In this paper, we utilize the results of International Reference Ionosphere (IRI)-2016 as prior knowledge to develop a low-latitude (30 degrees N-30 degrees S) three-dimensional ionospheric electron density model using ANN, namely ANN-IRI, based on COSMIC radio occultation ionospheric profiles during 2006-2020. The prior knowledge helps ANN-IRI get a better prediction effect and obtain convergence more quickly. The COSMIC data sets above the altitude 150 km are divided into three sets, a training set (2006-2014 and 2018-2020), a validation set (2016), and a test set (2015 and 2017). For the test set, ANN-IRI shows a good performance for predicting the electron density, better than ANN (without prior knowledge) and IRI-2016. And ANN-IRI behaves better during quiet than disturbed times as well as during low solar activity than high solar activity years. In addition, we corrected effectively the error of ANN-IRI in the lower ionosphere source from COSMIC data based on IRI-2016 and spline interpolation. Compared with completely independent data sets from incoherent scatter radars (ISRs) and ROCSAT-1 in situ observations, the electron density predicted by corrected ANN-IRI exhibits good similarity. Generally, ANN-IRI behaves better than ANN and IRI-2016 for predicting electron density. Our work demonstrates a new possibility of applying deep learning methods with prior knowledge to a broader field of geosciences, particularly for problems of prediction.