Ionospheric response to solar EUV variations: Preliminary results

Vaishnav, Rajesh; Jacobi, Ch.; Berdermann, Jens; Schmölter, Erik; Codrescu, M.

doi:10.5194/ars-16-157-2018

Cited by 19 publications

(31 citation statements)

References 50 publications

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“…Only recently, Schmölter et al (2020) used SDO EVE and GOES EUV fluxes to calculate the ionospheric delay of about 17 h as a mean value based on hourly time resolution data. This observed delay was also confirmed by numerical physics based models (Ren et al, 2018;Vaishnav et al, 2018).…”

Section: Cross Correlation and Delay Estimationsupporting

confidence: 73%

Ionospheric Response to Solar EUV Radiation Variations: Comparison based on CTIPe Model Simulations and Satellite Measurements

Vaishnav

Schmölter

Jacobi

et al. 2020

Preprint

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Abstract. The ionospheric Total Electron Content (TEC) provided by the International GNSS Service (IGS), and the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model simulated TEC have been used to investigate the delayed ionospheric response against solar flux and its trend during the years 2011 to 2013. The analysis of the distinct low and mid-latitudes TEC response over 15° E shows a better correlation of observed TEC and the solar radio flux index F10.7 in the Southern Hemisphere compared to the Northern Hemisphere. Thus, a significant hemispheric asymmetry is observed. The ionospheric delay estimated using model simulated TEC is in good agreement with the delay estimated for observed TEC against Solar Dynamics Observatory (SDO) EUV Variability Experiment (EVE) measured flux. The average delay for the observed (modeled) TEC is 17(16) h. The average delay calculated for observed and modeled TEC is 1 and 2 h longer in the Southern Hemisphere compared to the Northern Hemisphere. Furthermore, the observed TEC is compared with the modeled TEC simulated using the SOLAR2000 and EUVAC flux models within CTIPe over Northern and Southern Hemispheric grid points. The analysis suggests that TEC simulated using the SOLAR2000 flux model overestimates the observed TEC, which is not the case when using the EUVAC flux model.

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Section: Cross Correlation and Delay Estimationsupporting

confidence: 73%

Ionospheric Response to Solar EUV Radiation Variations: Comparison based on CTIPe Model Simulations and Satellite Measurements

Vaishnav

Schmölter

Jacobi

et al. 2020

Preprint

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“…As shown in Figure 5, there might be ROTI anomaly after 1.0–1.5 days when X‐ray flux anomaly occurred. As we know, the ionosphere is built by absorbing soft X‐rays and solar extreme ultraviolet (EUV) mainly at wavelengths below 105 nm (Vaishnav et al, 2018).…”

Section: Space Weather and Ionospheric Conditions Around The St Patrmentioning

confidence: 99%

Assessing the Positioning Performance Under the Effects of Strong Ionospheric Anomalies With Multi‐GNSS in Hong Kong

Lü

Wang

et al. 2020

Radio Science

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Global navigation satellite system (GNSS) precise positioning performance will be strongly affected under severe ionospheric anomaly conditions. The combination of multi‐GNSS can increase the available observations and improve the geometry of continuously tracked satellites. This paper focuses on assessing the positioning performance with the combination of Global Positioning System (GPS), Global'naya Navigatsionnaya Sputnikova Sistema (GLONASS), and BeiDou System (BDS) around the St. Patrick's Day geomagnetic storm (9–18 March) in 2015 in Hong Kong. The rate of total electron content (TEC) index (ROTI) indicates severe ionospheric anomalies before the superstorm, while it was absent during the main phase of the storm in Hong Kong. Furthermore, strong scintillation events on signal‐to‐noise ratio (SNR) and multipath (MP) observables are observed during ionospheric anomalies period. Then the performance of single‐point positioning (SPP) and precise point positioning (PPP) with multi‐GNSS is shown. The ionospheric scintillation events may reduce pseudorange accuracy but affect SPP performance a little in this study, while the PPP accuracy is vastly decreased due to the subsequent reconvergence caused by frequent cycle slip (CS). Compared to PPP solutions with GPS only, the accuracy is improved significantly with the combination of multi‐GNSS.

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“…During solar maxima, the T-I regime can partially be controlled by the solar wind activity superseding the solar radiation impact. However, during periods of low solar activity, the local variability in the ionosphere is also not only regulated by the solar radiation but can be influenced by lower atmospheric forcing (e.g., Forbes et al, 2000;Knížová et al, 2015) and by the solar wind, in particular from coronal holes (e.g., Zurbuchen et al, 2012;Verkhoglyadova et al, 2013).…”

Section: Long-term Variations Of Tec and Euv Fluxmentioning

confidence: 99%

Long-term trends in the ionospheric response to solar extreme-ultraviolet variations

2019

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Abstract. The thermosphere–ionosphere system shows high complexity due to its interaction with the continuously varying solar radiation flux. We investigate the temporal and spatial response of the ionosphere to solar activity using 18 years (1999–2017) of total electron content (TEC) maps provided by the international global navigation satellite systems service and 12 solar proxies (F10.7, F1.8, F3.2, F8, F15, F30, He II, Mg II index, Ly-α, Ca II K, daily sunspot area (SSA), and sunspot number (SSN)). Cross-wavelet and Lomb–Scargle periodogram (LSP) analyses are used to evaluate the different solar proxies with respect to their impact on the global mean TEC (GTEC), which is important for improved ionosphere modeling and forecasts. A 16 to 32 d periodicity in all the solar proxies and GTEC has been identified. The maximum correlation at this timescale is observed between the He II, Mg II, and F30 indices and GTEC, with an effective time delay of about 1 d. The LSP analysis shows that the most dominant period is 27 d, which is owing to the mean solar rotation, followed by a 45 d periodicity. In addition, a semi-annual and an annual variation were observed in GTEC, with the strongest correlation near the equatorial region where a time delay of about 1–2 d exists. The wavelet variance estimation method is used to find the variance of GTEC and F10.7 during the maxima of the solar cycles SC 23 and SC 24. Wavelet variance estimation suggests that the GTEC variance is highest for the seasonal timescale (32 to 64 d period) followed by the 16 to 32 d period, similar to the F10.7 index. The variance during SC 23 is larger than during SC 24. The most suitable proxy to represent solar activity at the timescales of 16 to 32 d and 32 to 64 d is He II. The Mg II index, Ly-α, and F30 may be placed second as these indices show the strongest correlation with GTEC, but there are some differences in correlation during solar maximum and minimum years, as the behavior of proxies is not always the same. The indices F1.8 and daily SSA are of limited use to represent the solar impact on GTEC. The empirical orthogonal function (EOF) analysis of the TEC data shows that the first EOF component captures more than 86 % of the variance, and the first three EOF components explain 99 % of the total variance. EOF analysis suggests that the first component is associated with the solar flux and the third EOF component captures the geomagnetic activity as well as the remaining part of EOF1. The EOF2 captures 11 % of the total variability and demonstrates the hemispheric asymmetry.

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Ionospheric response to solar EUV variations: Preliminary results

Cited by 19 publications

References 50 publications

Ionospheric Response to Solar EUV Radiation Variations: Comparison based on CTIPe Model Simulations and Satellite Measurements

Ionospheric Response to Solar EUV Radiation Variations: Comparison based on CTIPe Model Simulations and Satellite Measurements

Assessing the Positioning Performance Under the Effects of Strong Ionospheric Anomalies With Multi‐GNSS in Hong Kong

Long-term trends in the ionospheric response to solar extreme-ultraviolet variations

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