Electron density fluctuations from Swarm as a proxy for ground-based scintillation data: A statistical perspective

Kotova, Daria; Jin, Yaqi; Spogli, Luca; Wood, Alan; Urbář, Jaroslav; Rawlings, James T.; Whittaker, Ian; Alfonsi, Lucilla; Clausen, L. B. N.; Høeg, Per; Miloch, W. J.

doi:10.1016/j.asr.2022.11.042

Cited by 11 publications

(5 citation statements)

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“…Indeed, the Swarm-VIP models are conceived to contribute to the knowledge of ionospheric behaviour and to support the development of possible operational tools which can provide ionospheric information in regions scarcely covered by ground-based instrumentation. The latter follows also from the hints about the usability of IPIR parameters to support the identification of plasma irregularities, their level of structuring and their effect on L-band signals provided by Kotova et al (2023).…”

Section: Introductionmentioning

confidence: 84%

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: Introductionmentioning

confidence: 84%

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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“…This study demonstrates that the EOF method is very suitable and efficient for developing models for ionospheric irregularities. The EOF method is very straightforward and it can also be applied to other irregularity indices, such as the GNSS amplitude and phase scintillation indices from ground-based GNSS scintillation receivers, as well as in-situ irregularity indices obtained from Low Earth Orbiting satellites (Jin et al, 2022b;Kotova et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Modeling TEC Irregularities in the Northern Hemisphere Using Empirical Orthogonal Function Method

Jin

Miloch

Kotova

et al. 2023

Preprint

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We develop a climatological model for the Northern Hemisphere based on a long-term dataset (2010-2021) of the rate of change of the total electron content (TEC) index (ROTI) maps from the International GNSS Service (IGS). The IGS ROTI maps are daily averaged in magnetic latitude and local time coordinates. To develop a climatological model, the ROTI maps are decomposed into a few base functions and coefficients using the empirical orthogonal function (EOF) method. The EOF method converges very quickly, and the first four EOFs reflect the majority (96%) of the total data variability. Furthermore, different EOF components can reflect different drivers of ionospheric irregularities. The first EOF reflects the averaged ROTI activity and the impact of the solar radiation and geomagnetic activity; the 2nd EOF reflects the impact of the interplanetary magnetic field (IMF) Bz and electric field; the 3rd and 4th EOFs reflect the dawn-dusk asymmetry around the auroral oval and polar cap, and they can be related to the IMF By. To build an empirical model, we fit the EOF coefficients using helio-geophysical indices from four different categories (solar activity; geomagnetic indices; IMF; the solar wind coupling function). The final EOF model is dependent on seven selected indices (F10.7P, Kp, Dst, Bt, By, Bz and Ekl). The statistical data-model comparisons show satisfactory results with a good correlation coefficient. However, the model cannot capture the significant expansion of the dayside ROTI activity during strong geomagnetic storms. Future effort is needed to provide corrections to the model for severe storms.

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“…This study demonstrates that the EOF method is very suitable and efficient for developing models for ionospheric irregularities. The EOF method is very straightforward and it can also be applied to other irregularity indices, such as the GNSS amplitude and phase scintillation indices from ground‐based GNSS scintillation receivers, as well as in situ irregularity indices obtained from Low Earth Orbiting satellites (Jin, Kotova, et al., 2022; Kotova et al., 2022).…”

Section: Discussionmentioning

confidence: 99%

Modeling TEC Irregularities in the Arctic Ionosphere Using Empirical Orthogonal Function Method

Jin,

Miloch,

Kotova

et al. 2023

Space Weather

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We develop a climatological model for the Arctic ionosphere based on more than one solar cycle of data (2010–2021) of the rate of change of the total electron content index (ROTI) maps from the International GNSS Service (IGS). The IGS ROTI maps are daily averaged in magnetic latitude and local time coordinates. To develop a climatological model, the ROTI maps are decomposed into base functions and coefficients using the empirical orthogonal function (EOF) method. The EOF method converges very quickly, and the first four EOFs reflect the majority (96%) of the total data variability. Furthermore, different EOF components can reflect different drivers of ionospheric irregularities. The first EOF reflects the averaged ROTI activity and the impact of the solar radiation and geomagnetic activity; the second EOF reflects the impact of the interplanetary magnetic field (IMF) Bz and electric field; the third and fourth EOFs reflect the dawn‐dusk asymmetry around the auroral oval and polar cap, and they can be related to the IMF By. To build an empirical model, we fit the EOF coefficients using helio‐geophysical indices from four different categories (solar activity; geomagnetic indices; IMF; the solar wind coupling function). The final EOF model is dependent on seven selected indices (F10.7P, Kp, Dst, Bt, By, Bz, and EKL). The statistical data‐model comparisons show satisfactory results with a good correlation coefficient. However, the model cannot capture the significant expansion of the dayside ROTI activity during strong geomagnetic storms. Future effort is needed to provide corrections to the model during severe storms.

show abstract

Electron density fluctuations from Swarm as a proxy for ground-based scintillation data: A statistical perspective

Cited by 11 publications

References 50 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 TEC Irregularities in the Northern Hemisphere Using Empirical Orthogonal Function Method

Modeling TEC Irregularities in the Arctic Ionosphere Using Empirical Orthogonal Function Method

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