Geomagnetic storms are one of the leading causes of ionospheric irregularities, depending on their intensity. The 6–10 September 2017 geomagnetic storm, the most severe geomagnetic event of the year, resulted from an X9 solar flare and a subsequent coronal mass ejection (CME), with the first sudden storm commencements (SSC) occurring at 23:43 UT on day 06, coinciding with a Sym-H value of approximately 50 nT, triggered by a sudden increase in the solar wind. The interplanetary magnetic field (IMF) and disturbance storm time (Dst) increased when the first SSC occurred at 23:43 UT on 6 September. The second SSC occurred with a more vigorous intensity at 23:00 UT on 7 September, with the Kp index reaching 8 and the auroral electrojet (AE) 2500 nT. In this study, we investigated this phenomenon using data from Swarm, FORMOSAT-3/COSMIC, and ground-based GNSS networks in East Africa to measure ionospheric irregularities near the equatorial ionization anomaly (EIA). In this procedure, the total electron content (TEC), amplitude scintillation (S4), and rate of TEC Index (ROTI) were implemented to recognize ionospheric irregularities appearing during the geomagnetic storm. In addition, the Langmuir plasma probes of the Swarm satellites were employed to identify the rate of electron density index (RODI). The results obtained from the different techniques indicate the effects of geomagnetic storms in terms of increased ionospheric irregularities indicated by geophysical ionospheric parameters. This study demonstrates the potential of using space-based measurements to detect the effects of a geomagnetic storm on ionospheric irregularities for regions where ground-based ionospheric observations are rarely available, such as above the oceans.

This study investigates the relationship between geomagnetic activities and ionospheric scintillations, focusing on how solar and geomagnetic parameters influence ionospheric disturbances across varying time frames and latitudes. Utilizing indices such as Kp, Dst, sunspot numbers, and the F10.7 solar flux, we elucidate the dynamics influencing ionospheric conditions, which are vital for the reliability of satellite communications, particularly in low-latitude regions. Our analysis demonstrates a clear correlation between periods of high solar activity and increased geomagnetic disturbances, leading to heightened ionospheric scintillations, such as occurred during the solar maximum of 2015. In contrast, 2020—a solar minimum period—exhibited fewer disturbances, highlighting the impact of solar activity levels on ionospheric conditions. Innovatively employing ConvGRU networks, this research advances the modeling and prediction of ionospheric scintillations by integrating deep learning techniques suited to the spatiotemporal complexities of ionospheric data. The ConvGRU model effectively captures both temporal sequences and spatial distributions, offering enhanced accuracy in depicting ionospheric scintillation patterns crucial for satellite-based navigation and communication systems. Ground-based GNSS data from 121 stations across South America, collected during 2015 and 2020, provide a robust dataset for our analysis. The study highlights the influence of the solar cycle on ionospheric scintillations, with the years of maximum and minimum solar activity showing significant differences in scintillation intensity and frequency. Our evaluation of the ConvGRU models using statistical parameters demonstrated their potential for reliable ionospheric scintillation prediction. The research underscores the necessity of integrating adaptive mechanisms within models to effectively handle the dynamic nature of ionospheric disturbances influenced by external geomagnetic and solar factors. This study enhances the understanding of ionospheric scintillations and significantly advances predictive modeling capabilities using advanced machine learning techniques. The potential establishment of real-time alert systems for ionospheric disturbances could significantly benefit civilian applications, enhancing the operational efficiency of technologies reliant on accurate ionospheric information.

The calibration of satellite radar altimetry has been extremely important for altimetry community and studying sea level changes. The main purpose of this contribution is to provide ongoing absolute calibration of altimeter bias near the Southern seas of Iran using the Iranian tide gauge network that equipped with GPS receivers to measure the sea surface heights synchronously in the same geocentric reference frame as the corresponding altimetry records. The sea level time series of coastal tide gauges have been used to estimate the bias, drift and annual/semiannual constituents of altimeter range measurements using (i) linear regression and (ii) combination of linear regression and harmonic analysis. To this end, three Iranian tide gauges located at Bushehr, Bandar Abbas and Chahbahar ports as well as Geophysical Data Records (GDR) products of Topex/Poseidon, Jason-1and Jason-2 have been considered. The numerical results have indicated that the mean absolute biases of Topex/Poseidon, Jason-1 and Jason-2 are about –26.23, 120.21 and 205.17 mm, respectively. The reliability of method has been assessed via GPS vessel at the altimeter bin nearby the Bushehr tidal stations. The presented method is viable to perfectly estimate the systematic errors, and as such, it can address the demands of high-accurate applications.