Deep Learning Application for Classification of Ionospheric Height Profiles Measured by Radio Occultation Technique

Abstract

Modern space missions provide a great number of height profiles of ionospheric electron density, measured by the remote sensing technique of radio occultation (RO). The deducing of the profiles from the RO measurements suffers from bias, resulting in negative values of the electron density. We developed a machine learning technique that allows automatic identification of ionospheric layers and avoids the bias problem. An algorithm of convolutional neural networks was applied for the classification of the height profiles. Six classes of the profiles were distinguished on the base of prominent ionospheric layers F2, Es, E, F1 and F3, as well as distorted profiles (Sc). For the models, we selected the ground truth of more than 712 height profiles measured by the COSMIC/Formosat-3 mission above Taiwan from 2011 to 2013. Two different models, a 1D convolutional neural network (CNN) and fully convolutional network (FCN), were applied for classification. It was found that both models demonstrate the best classification performance, with the average accuracy around 0.8 for prediction of the F2 layer-related class and the E layer-related class. The F1 layer is classified by the models with good performance (>0.7). The CNN model can effectively classify the Es layer with an accuracy of 0.75. The FCN model has good classification performance (0.72) for the Sc-related profiles. The lowest performance (<0.4) was found for the F3 layer-related class. It was shown that the more complex FCN model has better classification performance for both large-scale and small-scale variations in the height profiles of the ionospheric electron density.

Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning

“…The CNN network contains layers that perform convolutional operations, pooling (reducing the spatial dimensions) and non-linear activation functions to learn and extract important features from the input sequence (Kiranyaz et al, 2021;Qazi et al, 2022). 1D-CNN including multilayer 1D-CNN and long-short term memory (LSTM) are used in several time series sequence to sequence prediction in space weather studies (Hsieh et al, 2022;Kaselimi et al, 2020;Landa & Reuveni, 2022;Saajasto et al, 2023;Zewdie et al, 2021). Ionospheric TEC forecasting using CNN-LSTM machine learning algorism using F10.7, Bz, Kp and Dst indices as predictors attained R-squared up to 0.91 in Tang et al (2022).…”

Study of Ionospheric Bending Angle and Scintillation Profiles Derived by GNSS Radio-Occultation with MetOp-A Satellite

“…Usually, GNSS RO measurements are validated against external data sources. Ionosonde stations and incoherent scatter radars, for instance, are commonly used as a reference to the validation [12][13][14][15][16][17][18][19][20][21][22] since they provide accurate observations of the electron density. In-situ measurements provided by external satellite missions are also extensively used to assess the RO measurements [23][24][25][26][27][28][29][30].…”