A novel neural network model of Earth’s topside ionosphere

Smirnov, Artem; Shprits, Yuri; Prol, Fabricio dos Santos; Lühr, H.; Berrendorf, Max; Zhelavskaya, Irina; Xiong, Chao

doi:10.1038/s41598-023-28034-z

Cited by 17 publications

(10 citation statements)

References 55 publications

(103 reference statements)

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“…As shown in Figure 1, signals from GNSS satellites penetrate the whole ionosphere, whereas signals from LEO satellites travel only through a portion of it. Furthermore, the topside ionosphere has a greater electron concentration than the bottomside ionosphere, which is around 75% and 25% of the total TEC for the topside and bottomside, respectively [20,26,27]. This indicates that LEO signals experience less interference than GNSS signals.…”

Section: Sources Of Errors In Satellite-based Positioningmentioning

confidence: 96%

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Imad,

Grenier,

Zhang

et al. 2023

Computers

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Low Earth Orbit (LEO) constellations have ecently gained tremendous attention in the navigational field due to their arger constellation size, faster geometry variations, and higher signal power evels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric ayers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their ower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects epeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and eflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing eal-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS eceivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant eduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

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Section: Sources Of Errors In Satellite-based Positioningmentioning

confidence: 96%

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Imad,

Grenier,

Zhang

et al. 2023

Computers

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“…(b) The process will no longer be only about the relative level of depletions, but the end result is going to be expressed in terms of ionospheric plasma density and/or TEC values. (3) The background plasma density and/or TEC may be obtained from ionosphere models such as IRI, NeQuick, NET, TIE‐GCM, or WAM‐IPE (Bilitza et al., 2022; Coisson et al., 2006; Fang et al., 2018; Nava et al., 2008; Qian et al., 2014; Smirnov et al., 2023). Aside from these few modifications, the process would be quite straightforward: the relative depletion profile is going to be stamped onto the smooth background plasma density and/or TEC profile.…”

Section: Illustrative Examplesmentioning

confidence: 99%

A 2‐Dimensional Data Detrending Technique for Equatorial Plasma Bubble Studies Using GOLD Far Ultraviolet Observations

Pradipta,

Huang,

Groves

2024

JGR Space Physics

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We formulate a numerical data detrending technique that can be used to help reveal large‐scale equatorial plasma bubble (EPB) structures in 2‐dimensional data from the Global‐scale Observations of the Limb and Disk (GOLD) mission. This GOLD data detrending technique is inspired by and is a generalization of a previous rolling‐barrel data detrending method for 1‐dimensional total electron content (TEC) observations on individual global positioning system (GPS) satellite passes. This 2‐dimensional GOLD data detrending technique treats the observed 135.6 nm radiance as a function of longitude and latitude as an uneven terrain, where EPBs appear as deep but narrow elongated valleys. The unperturbed background radiance is inferred by rolling a ball on the 2‐dimensional terrain to skip over the EPB valleys. The two degrees‐of‐freedom possessed by the rolling ball allow it to smoothly trace the edges of EPB depletions, without falling into the deep valleys. Surface interpolation of radiance values at the ball's contact points onto the whole domain produces the baseline radiance. Subtracting the baseline from the original radiance data yields the net detrended radiance. As a result of the detrending, sharper contrast is present between EPB depletions and the ambient surroundings. As such, this new 2‐dimensional GOLD data detrending may potentially open the door to the development of other more advanced techniques for automated EPB detection and tracking, or data assimilation into low‐latitude space domain awareness (SDA) information ecosystems.

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“…An existing technique for topside ionospheric modeling uses 4 different networks to learn NmF2, HmF2, H0, and dHs/dh (Smirnov et al., 2023). Those networks are trained on inputs similar to ours, including a mix of global index values and geographical features.…”

Section: Existing Neural Network Modelsmentioning

confidence: 99%

Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions

Dutta,

Cohen

2023

Space Weather

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We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E‐CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid‐latitude performance with absolute errors lower than the IRI by 2.5 × 109 to 2.5 × 1010 e−/m3, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 109 e−/m3 at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E‐CHAIM.

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A novel neural network model of Earth’s topside ionosphere

Cited by 17 publications

References 55 publications

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

A 2‐Dimensional Data Detrending Technique for Equatorial Plasma Bubble Studies Using GOLD Far Ultraviolet Observations

Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions

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