Mahmoud Pirooznia

1. Accurate estimation of seafloor depth remains a complex and critical challenge, primarily due to inherent uncertainties in seabed topography data. Such information is essential for various field...

The detailed knowledge of bathymetry pattern represents a key factor in the deep understanding of ocean processes, physical oceanography, biology, ecohydraulics, and marine geology. However, the accuracy of bathymetry modeling is still low from satellite altimetry, gravity model, and shipborne gravity data. In this paper, a novel scheme is proposed based on black-box theory for regional bathymetry modeling in the Persian Gulf and the Oman Sea via geodetic data sources such as satellite altimetry, gravity model, and shipborne gravity data. Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Local Linear Model Tree (LOLIMOT) algorithms are used as nonlinear black-box tools to identify the basic mathematical model. The geoid height, gravity gradient, and gravity anomaly are used as inputs to these artificial intelligence models, with the GEBCO bathymetry model as the output. The derived basic model is further improved by assimilating with the shipborne bathymetry measurements using the 3D variational optimization method to determine the final bathymetry model. The model is validated by the shipborne bathymetry in control tracks of regions Chabahar, Genaveh, and Alamshah, and the results show high accuracy and reliability with root mean square errors (RMSEs) of about 4, 0.8, and 0.92 m, respectively. The proposed approach is valuable for various uses in marine science.

The efficiency of satellite altimetry in monitoring coastal areas and lakes is limited due to the contaminated waveform caused by non-water features included in the satellite footprint. Therefore, to mitigate these limitations, waveforms need to be retracked. In this research, the Generalized Logistic Function (GLF) has been introduced with Analytical (GLFA) and Numerical (GLFN) approaches to retrack the first sub-waveform. The results have been compared with those obtained from on-board retrackers existing in Level-2 altimetry data, the retracking of the full-waveform, the first sub-waveform, and the mean of the sub-waveforms using the threshold retracker. The Level-2 and Level-1B data of the Sentinel-3A (SRAL) mission for passes 141, 700, 244, and 311, respectively, passing over Vättern and Hjälmaren Lakes in Sweden, and 0–2 km distance from the coasts of the Bay of Alcudia and the Northeast Gulf of Bothnia from January 2019 to December 2022, were investigated. The results of the retracking approaches used in this study were evaluated against tide gauge data in terms of RMSE and its improvement percentage. The results demonstrate the superiority of the GLFA over the GLFN in coastal areas, while over lakes, the results are nearly equivalent. The improvement percentages of RMSE for the GLFA and GLFN compared to on-board retrackers, respectively, are as follows: for Vättern Lake, 53% and 58%; for Hjälmaren Lake, 40% and 33%; for the Bay of Alcudia, 81% and 46%; and for the Northeast Gulf of Bothnia, the GLFA shows a 36% improvement, while the GLFN yields results equivalent to on-board retrackers. The GLF has shown better performance compared to other approaches, except for Vättern Lake, which yields results almost equivalent to the first sub-waveform retracking approach. Additionally, the mean of the sub-waveform retracking approach by making use of the threshold algorithm has mostly demonstrated weaker performance compared to other methods.

Harnessing ocean kinetic energy has emerged as a promising renewable energy solution in recent years. However, identifying optimal locations for extracting this energy remains a significant challenge. This study presents a novel scheme to estimate the total surface current (TSC) as permanent surface current by integrating geodetic data and in-situ measurements. The TSC is typically a combination of the geostrophic current, derived from dynamic topography, and the Ekman current. We utilize NOAA’s Ekman current data to complement the geostrophic current and obtain the TSC. To further enhance the accuracy of the TSC estimates, we employ a 3DVAR data assimilation method, incorporating local current meter observations. The results are verified against two control current meter stations. The data-assimilation process resulted in an improvement of 4 to 15 cm/s in the precision of calculated TSC. Using the assimilated TSC data, we then assess the kinetic energy potential and identify six regions with the most significant promise for marine kinetic energy extraction. This innovative approach can assist researchers and policymakers in targeting the most suitable locations for harnessing renewable ocean energy.

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.

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.

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.

In this paper, a combination of satellite altimetry (SA) and tide-gauge (TG) observations is used to determine the pattern of vertical deformation at southern coasts of Caspian Sea. Satellite altimetry measures the variations of sea water surface with a respect to a reference ellipsoid, while tide gauge data gives the sea level relative to a particular ground station; consequently, the difference between these two observations will give the vertical motion at the ground station. However, altimeter satellites still have problems at shallow waters or coastal areas which leads to inaccurate and corrupted sea surface height at tide gauge stations. To minimize this imprecision, two strategies are applied for the selection of the suitable SA points near the TG stations. The First one is to choose those close passes to the TG stations, provided that the SA data is not corrupted (range observation is available). The second strategy is to extrapolate the SA points at TG stations using Spatio- Temporal Kriging method. In both point of views, use is made of re-tracking methods to improve the ranges of SA observations near the coast. Amplitudes of tidal components are removed from both SA and TG observations to derive the residual tide free signals. Afterward, a linear trend is fitted to the residual signals in the sense of least square; the difference between these two, supplies the vertical deformation of the ground station. The results illustrate that the average vertical crustal motion at Anzali, Noshahr and Neka are -1.387, -1.903 and -3.14 mm/year, respectively.