Variability of Ionospheric Plasma: Results from the ESA Swarm Mission

Wood, Alan; Alfonsi, Lucilla; Clausen, L. B. N.; Jin, Yaqi; Spogli, Luca; Urbář, Jaroslav; Rawlings, James T.; Whittaker, Ian; Dorrian, Gareth; Høeg, Per; Kotova, Daria; Cesaroni, Claudio; Cicone, Antonio; Miedzik, Jan; Gierlach, Ewa; Kochańska, Paula; Wojtkiewicz, Pawel; Shahtahmassebi, Golnaz; Miloch, W. J.

doi:10.1007/s11214-022-00916-0

Cited by 15 publications

(10 citation statements)

References 199 publications

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“…Another source of information used in the present study is the electron and neutral density data set at an altitude of ∼450–500 km based on in situ Langmuir probe (LP) data on board the European Space Agency (ESA) Swarm satellites that have been in operation since 2013 (Friis‐Christensen et al., 2008; Wood et al., 2022). The Langmuir probes on board low Earth orbit (LEO) satellites facilitate in situ electron density monitoring at a fixed altitude.…”

Section: Discussionmentioning

confidence: 99%

Ionosphere Heterogeneities at Dawn−Dusk Terminator Related to the Starlink Satellites Launch Disaster on 3−8 February 2022

Gulyaeva,

Lukianova,

Haralambous

2023

JGR Space Physics

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We present an analysis of the evolution of the ionospheric Total Electron Content (TEC) and its variability index Vσ during the Starlink launch disaster on 3–8 February 2022 two‐phase geomagnetic storm. JPL GIM‐TEC global maps with increased spatial (1° in latitude and longitude) and temporal (15 min cadence) resolution as well as SWARM satellite data have been used in the analysis. Comparison of data for five time periods including Starlink launches with geomagnetic Dst index demonstrates thermospheric mass densities increasing by about 50% during the development of the storm on 3–5 February. A spectrum periodogram analysis is applied on TEC at sunrise and sunset solar terminator (STh) at an altitude h = 300 km. Periodicities in the solar terminator TEC generated waves are estimated as 24 hr, 12 hr, 1 hr, 30 min, 20 min, and 15 min. A significant morning−evening asymmetry resulted in the density anomalies concentrated in a certain—evening—sector of terminator when plasma is shifted to the dusk side forming a global asymmetry.

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Section: Discussionmentioning

confidence: 99%

Ionosphere Heterogeneities at Dawn−Dusk Terminator Related to the Starlink Satellites Launch Disaster on 3−8 February 2022

Gulyaeva,

Lukianova,

Haralambous

2023

JGR Space Physics

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“…These satellites measure in‐situ plasma density and different ionospheric parameters at different locations. The two satellites, Swarm A and C fly side by side approximately at 462 km while Swarm C satellite flies at upper altitude at about 511 km altitude (Wood et al., 2022). These plasma density data in‐situ measurements are used to calculate the rate of density (ROD) and the rate of density index (RODI) which are used as a proxy to measure the existence of ionospheric irregularities in the ionosphere.…”

Section: Methodsmentioning

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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“…Despite being mainly conceived as a magnetic mission, Swarm also observes the ionospheric plasma. A large number of papers have been published in this field and these have been reviewed by Wood et al (2022). The configuration of the Swarm satellites, their near-polar orbits and the data products developed, enable studies of the spatial variability of the ionosphere at multiple scale sizes (Kotova et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Statistical models of the variability of plasma in the topside ionosphere: 1. Development and optimisation

Wood,

Donegan-Lawley,

Clausen

et al. 2024

J. Space Weather Space Clim.

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This work presents statistical models of the variability of plasma in the topside ionosphere based on observations made by the European Space Agency’s (ESA) Swarm satellites. The models were developed in the “Swarm Variability of Ionospheric Plasma” (Swarm-VIP) project within the European Space Agency's Swarm+4D-Ionosphere framework. The configuration of the Swarm satellites, their near-polar orbits and the data products developed, enable studies of the spatial variability of the ionosphere at multiple scale sizes. The statistical modelling technique of Generalised Linear Modelling was used to create models of both the electron density and measures of the variability of the plasma structures at horizontal spatial scales between 20 km and 100 km. Despite being developed using the Swarm data, the models provide predictions that are independent of these data. Separate models were created for low, middle, auroral and polar latitudes. The models make predictions based on heliogeophysical variables, which act as proxies for the solar and geomagnetic processes. The first and most significant term in the majority of the models was a proxy for solar activity. The most common second term varied with the latitudinal region. This was the Solar Zenith Angle (SZA) in the polar region, a measure of latitude in the auroral region, solar time in the mid-latitude region and a measure of latitude in the equatorial region. Other, less significant terms in the models covered a range of proxies for the solar wind, geomagnetic activity and location. In this paper the formulation, optimisation and evaluation of these models is discussed. The models show very little bias, with a mean error of zero to two decimal places in 14 out of 20 cases. The models capture some, but not all, of the trends present in the data, with Pearson correlation coefficients of up to 0.75 between the observations and the model predictions. The models also capture some, but not all, of the variability of the ionospheric plasma, as indicated by the precision, which ranged between 0.20 and 0.83. The addition of the thermospheric density as an explanatory variable in the models improved the precision in the polar and auroral regions. It is suggested that, if the thermosphere could be observed at a higher spatial resolution, then even more of the variability of the plasma structures could be captured by statistical models. The formulation and optimisation of the models are presented in this paper. The capability of the model in reproducing the expected climatological features of the topside ionosphere, in supporting GNSS-based ionospheric observations and the performance of the model against TIE-GCM, are provided in a companion paper (Spogli et al., 2023).

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Variability of Ionospheric Plasma: Results from the ESA Swarm Mission

Cited by 15 publications

References 199 publications

Ionosphere Heterogeneities at Dawn−Dusk Terminator Related to the Starlink Satellites Launch Disaster on 3−8 February 2022

Ionosphere Heterogeneities at Dawn−Dusk Terminator Related to the Starlink Satellites Launch Disaster on 3−8 February 2022

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

Statistical models of the variability of plasma in the topside ionosphere: 1. Development and optimisation

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