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.

Satellite altimetry observations and tide gauge data are invaluable tools to diagnose and resolve tidal constituents over the Oceans and Seas. The aim of this study is to introduce a new purely empirical tide model named TM-IR01 in the Persian Gulf, Oman Sea, and North Indian Ocean. The observations of three altimeter sensors including TOPEX/POSIDON, JASON1, and JASON2 and 13 coastal tide gauge (TG) stations are processed and analyzed in this research. First of all, the least square spectral analysis is utilized to recover the significant tide components and consequently the amplitude and phases of the constituents are found during the tide modeling. Finally, the analysis results are interpolated into a grid of 1/4° using the Kriging method. TM-IR01 model is validated by comparing with TG stations and global tide models. It is shown that for main tidal frequencies M2, S2, K1, and O1 the root mean square error (RMSE) between TM-IR01 and TG stations results are 0.372, 0.130, 0.141, and 0.084 m, respectively, and also the RMSE between TM-IR01 and FES2004 models are 0.231, 0.087, 0.027, and 0.042 m, respectively. Validating with FES2012 and Tpxo7.2, the results obtained are close to the above values.

Sea surface currents are often modeled using numerical models without adequately addressing the issue of model calibration at the regional scale. The aim of this study is to calibrate the MIKE 21 numerical ocean model for the Persian Gulf and the Oman Sea to improve the sea surface currents obtained from the model. The calibration was performed through data assimilation of the model with altimetry and hydrographic observations using variational data assimilation, where the weights of the objective functions were defined based on the type of observations and optimized using metaheuristic optimization methods. According to the results, the calibration of the model generally led the model results closer to the observations. This was reflected in an improvement of about 0.09 m/s in the obtained sea surface currents. It also allowed for more accurate evaluations of model parameters, such as Smagorinsky and Manning coefficients. Moreover, the root mean square error values between the satellite altimetry observations at control stations and the assimilated model varied between 0.058 and 0.085 m. We further showed that the kinetic energy produced by sea surface currents could be used for generating electricity in the Oman Sea and near Jask harbor.

The purpose of this paper is to model tidal conditions in the Caspian Sea using data from coastal tide gauges of Anzali, Noshahr, and Neka Ports. Harmonic analysis method was used to identify and examine 40 tidal components. The results illustrate that the annual (Sa) and semiannual solar (Ssa) components on all of the ports listed have the highest range in comparison with the other components which are, respectively, 16 cm, 18 cm, and 15 cm for annual components and 2.8 cm, 5.4 cm, and 3.7 cm for semiannual components.

Dynamic topography (DT) refers to the time-varying component of the sea surface height influenced by factors like ocean currents, temperature, and salinity gradients. Accurate estimation of DT is crucial for comprehending oceanic circulation patterns and their impact on climate. This study introduces two approaches to estimating DT: (1) utilizing satellite altimetry to directly observe sea surface height and (2) considering the steric and non-steric components of sea level anomalies. The steric term is calculated using salinity and temperature data obtained from local buoy data, Argo observations, and the World Ocean Atlas model. The non-steric term is calculated using GRACE Satellite gravimetry data. To estimate the assimilated DT, four methods are utilized, including variance component estimation (VCE), Bayesian theory, Kalman filter, and 3D variational (3DVAR). These methods assimilate the two aforementioned schemes. The validity of the estimated DT is assessed by comparing the calculated sea surface current, derived from the obtained DT, with observations from local current meter stations. The results indicate that the VCE method outperforms other methods in determining the final DT. Furthermore, incorporating the steric and non-steric terms of sea level in determining DT in coastal areas enhances the accuracy of estimating sea surface currents.