Climatology of ionospheric amplitude scintillation on GNSS signals at south American sector during solar cycle 24

Macho, Eduardo Perez; Correia, E.; Spogli, Luca; Muella, M. T. A. H.

doi:10.1016/j.jastp.2022.105872

Cited by 6 publications

(6 citation statements)

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“…To assess the equatorial model, the period 2-6 February 2022 in the Southern Hemisphere has been selected, as it has the right seasonality (close to the equinox and local summer) and solar flux conditions (ascending phase of the solar cycle) to have enhanced probability of formation of ionospheric irregularities embedded in the post-sunset EPBs (see, e.g. Cesaroni et al, 2015;Muella et al, 2017;Li et al, 2021;Macho et al, 2022). This variability, coupled with the proximity to the South Atlantic Anomaly, represents a particularly challenging set of conditions for the model.…”

Section: Performance Assessment Against Ground-based Gnss Observationsmentioning

confidence: 99%

Statistical models of the variability of plasma in the topside ionosphere: 2. Performance assessment

Spogli,

Jin,

Urbář

et al. 2024

J. Space Weather Space Clim.

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Statistical models of the variability of plasma in the topside ionosphere based on the Swarm data have been developed in the “Swarm Variability of Ionospheric Plasma” (Swarm-VIP) project within the European Space Agency's Swarm+4D-Ionosphere framework.. The models can predict the electron density, its gradients for three horizontal spatial scales – 20, 50 and 100 km – along the North-South direction and the level of the density fluctuations. Despite being developed by leveraging on Swarm data, the models provide predictions that are independent of these data, having a global coverage, fed by various parameters and proxies of the helio-geophysical conditions. Those features make the Swarm-VIP models useful for various purposes, which includes the possible support for already available ionospheric models and to proxy the effect of ionospheric irregularities of the medium scales that affect the signals emitted by Global Navigation Satellite Systems (GNSS). The formulation, optimisation and validation of the Swarm-VIP models are reported in Paper 1 (Wood et al., 2024). This paper describes the performance assessment of the models, by addressing their capability in reproducing the known climatological variability of the modelled quantities, and the ionospheric weather as depicted by ground-based GNSS, as a proxy for the ionospheric effect on GNSS signals. Additionally, we demonstrate that, under certain conditions, the model can better reproduce the ionospheric variability than a physics-based model, namely the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM).

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Section: Performance Assessment Against Ground-based Gnss Observationsmentioning

confidence: 99%

Statistical models of the variability of plasma in the topside ionosphere: 2. Performance assessment

Spogli,

Jin,

Urbář

et al. 2024

J. Space Weather Space Clim.

Self Cite

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“…Ionospheric Plasma Irregularities (PI) are plasma density disturbances that primarily occur in the ionospheric F‐region, predominantly at night but also during the daytime (Aol et al., 2020; Balan, Liu, & Le, 2018; Kil et al., 2020; Seba et al., 2018; Xie et al., 2023). These irregularities manifest as plasma depletions in the ionosphere, leading to signal fading or scintillation of GPS signals that transit through them (Gentile et al., 2006; Macho et al., 2022; Seba & Gogie, 2015; Sreeja, 2016). Satellites observing the topside ionosphere can detect these irregularities as field‐aligned plasma depletions.…”

Section: Introductionmentioning

confidence: 99%

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

Seba,

Lapenta

2024

Space Weather

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This study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. Our objective is to simulate monthly global PI characteristics using a multilayer 1D‐CNN model trained on 12 space weather and ionospheric parameters. In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. Our findings indicate that non‐equinox months exhibit the highest equatorial PI magnitude over the American‐African longitudinal sector, contrary to the expected higher Rayleigh‐Taylor instability growth rate during equinox months. Winter months display the most intense and widespread vertically and horizontally distributed equatorial PI patterns. We also observe double peaks across geomagnetic latitudes and longitudinally varying wavelike irregularity structures, particularly in May, August, and predominantly in September. Furthermore, north‐south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance‐related factors play a crucial role in predicting global PI variabilities. These findings emphasize the significance of these variables in controlling the strongest PI characteristics observed in the Atlantic sector, which has garnered considerable attention in PI research. The employed 1D‐CNN model demonstrates exceptional predictive capabilities, exhibiting a strong correlation of 0.98 for global PI characteristics across all months and satellites.

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“…A comprehensive investigation of the irregularity occurrence derived from 20 years of incoherent scatter radar data at the Jicamarca radio observatory has been provided by Zhan et al (2018). Accordingly, based on 10 years of ground-based GPS observations distributed in South America, Macho et al (2022) indicated some activity of weak scintillations also during low solar flux years, while moderate or intense scintillations did only occur during moderate or high solar flux years.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, based on 10 years of ground‐based GPS observations distributed in South America, Macho et al. (2022) indicated some activity of weak scintillations also during low solar flux years, while moderate or intense scintillations did only occur during moderate or high solar flux years.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Model of the Occurrence Rate of Low Latitude Post‐Sunset Plasma Irregularities Derived From CHAMP and Swarm Magnetic Observations

Stolle,

Siddiqui,

Schreiter

et al. 2024

Space Weather

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The prediction of post‐sunset equatorial plasma depletions (EPDs), often called ionospheric plasma bubbles, has remained a challenge for decades. In this study, we introduce the Ionospheric Bubble Probability (IBP) model, an empirical model to predict the occurrence probability of EPDs derived from 9 years of CHAMP and 9 years of Swarm magnetic field measurements. The model predicts the occurrence probability of EPDs for a given longitude, day of year, local time and solar activity, for the altitude range of about 350–510 km, and low geographic latitudes of ±45°. IBP has been found to successfully reconstruct the distribution of EPDs as reported in previous studies from independent data. IBP has been further evaluated using 1‐year of untrained data of the Ionospheric Bubble Index (IBI). IBI is a Level 2 product of the Swarm satellite mission used for EPD identification. The relative operating characteristics (ROC) curve shows positive excursion above the no‐skill line with Hanssen and Kuiper's Discriminant (H&KSS) score of 0.52, 0.51, and 0.55 at threshold model output of 0.16 for Swarm A, B, and C satellites. Additionally, the reliability plots show proximity to the diagonal line with a decent Brier Skill Score (BSS) of 0.249, 0.210, and 0.267 for Swarm A, B, and C respectively at 15% climatological occurrence rate. These tests indicate that the model performs significantly better than a no‐skill forecast. The IBP model offers compelling glimpses into the future of EPD forecasting, thus demonstrating its potential to reliably predict EPD occurrences. The IBP model is publicly available.

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Climatology of ionospheric amplitude scintillation on GNSS signals at south American sector during solar cycle 24

Cited by 6 publications

References 73 publications

Statistical models of the variability of plasma in the topside ionosphere: 2. Performance assessment

Statistical models of the variability of plasma in the topside ionosphere: 2. Performance assessment

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

An Empirical Model of the Occurrence Rate of Low Latitude Post‐Sunset Plasma Irregularities Derived From CHAMP and Swarm Magnetic Observations

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