Alireza Azmoudeh Ardalan

This study introduces a technique to uncover concealed patterns in satellite altimetry data, reflecting sea level variations in inland water bodies. We applied the methodology to the Caspian Sea, using altimetry data from four satellite missions over 27 years (1993–2020): TOPEX/Poseidon, Jason1, Jason2, and Jason3. The approach involves two steps: estimating sea level trends and identifying breakpoints that indicate trend shifts; and leveraging time lags between breakpoints at various locations to classify water drainage areas according to their degree of influence on each period. By revealing concealed patterns in the altimetry data, this technique can provide insights to understand the impacts of global warming and local climate changes on sea level fluctuations in the Caspian Sea. We hope that our study can facilitate subsequent interdisciplinary research on the intricate interplay between climate change and hydrological processes in inland water bodies.

This study introduces a technique to uncover concealed patterns in satellite altimetry data, reflecting sea level variations in inland water bodies. We applied the methodology to the Caspian Sea, using altimetry data from four satellite missions over 27 years (1993-2020): TOPEX/Poseidon, Jason1, Jason2, and Jason3. The approach involves two steps: estimating sea level trends and identifying breakpoints that indicate trend shifts; and leveraging time lags between breakpoints at various locations to classify water drainage areas according to their degree of influence on each period. By revealing concealed patterns in the altimetry data, this technique can provide insights to understand the impacts of global warming and local climate changes on sea level fluctuations in the Caspian Sea. We hope that our study can facilitate subsequent interdisciplinary research on the intricate interplay between climate change and hydrological processes in inland water bodies.

SUMMARY: High-precision and high-resolution satellite altimetry data from CryoSat-2 are widely utilized for marine gravity inversion. The vertical gravity gradient is a crucial parameter of the Earth's gravity field. To evaluate the performance of vertical gravity gradient determined from CryoSat-2 altimeter data, the pre-processed along-track sea surface heights (SSHs) are obtained through error correction. The study area focused on the Arabian Sea and its surrounding region, where the along-track geoid was derived by subtracting the mean dynamic topography of the ocean from the along-track SSH of CryoSat-2. The residual along-track geoidal gradients were obtained by adjusting the along-track geoid gradients calculated from CryoSat-2 altimeter data using the remove-restore method. This was done by subtracting the geoid gradients calculated by the gravity field model XGM2019e_2159. After obtaining the residual along-track geoidal gradients, the residual gridded deflections of the vertical (DOV) are calculated using the least-squares collocation (LSC) method. The residual gridded DOV are then used to compute the residual gridded gravity anomaly gradients in the study area using the finite-difference method. After restoring the gravity anomaly gradients computed by the XGM2019e_2159 model, a high-resolution gravity anomaly gradient model with a resolution of 1′ ×1′ is obtained for the Arabian Sea and its surrounding area. To evaluate the accuracy of the gravity anomaly gradient model derived from CryoSat-2, it was compared with the SIO V32.1 gravity anomaly gradient model released by the Scripps Institution of Oceanography. The comparison showed that the root mean square (RMS) of the differences between the two models is 7.69E, demonstrating the high accuracy and precision of the vertical gravity gradient determined from CryoSat-2 altimeter data.

Global geopotential models (GGMs) are widely used in earth sciences, especially in geodesy and geophysics. In this paper, we aim at studying GGMs' capability in the geoidal height estimation at the geographical region of Iran. This will include verification of both satellite-only and combined GGMs. To compute the geoidal height at a point, keeping the longitude and latitude constant, we change its ellipsoidal height until the GGM-derived gravity potential at the resulting height equals the geoid’s potential value. In this process, we consider the topographic bias effect once the computation of the potential is inside the topographic masses. As the benchmark for the performance analysis of GGMs, we use the known geoidal heights of 841GPS/Levelling stations with a country-wide distribution. The results indicate that the XGM2019e_2159 (n_max=2190) GGM is the best combined and the GO_CONS_GCF_2_DIR_R5 (n_max=330) GGM is the best satellite-only model for the computation of geoidal heights in the study area, Iran.

Altimetry satellites of the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 missions have provided the ocean study community with an unprecedented long-term time series of sea level observations from space. In this paper, we aim to investigate the mean sea surface (MSS) data given by the GDR files of the mentioned missions starting from the 1992 TOPEX/Poseidon mission to the latest GDR files of Jason-3. Due to climate change, it is expected that there be changes in the MSS value and furthermore, its trend is detectable in the time series of thirty years of mentioned altimetry observations. More specifically, we would like to know within long-term altimetry observations how the effect of sea level change is handled in the GDR files of TOPEX/Poseidon, Jason-1, 2, and 3 missions, and whether there are cares to be considered when using the MSS data given in the GDR files of these missions? Answer to this question and other related issues are discussed in the paper. Our benchmark for verifications of MSS data in the GDR files is the time series of the sea level heights (SSH) at 30 passes of the mentioned missions over the Baltic Sea.

The aim of this study is to estimate reliable velocities along with their realistic uncertainties based on a robust time series analysis including analysis of deterministic and stochastic (noise) models. In the deterministic model analysis part, we use a complete station motion model comprised of jump effects, linear and nonlinear trend, periodic components, and post-seismic deformation model. This part also consists of jump detection, outlier detection, and statistical significance of jumps. We perform the deterministic model analysis in an iterative process to elevate its efficiency. In the noise analysis part, first, we remove the spatial correlation of observations using the weighted stacking method based on the common mode error (CME) parameter. Next, a combination of white and flicker noises is used to determine the stochastic model. This time series analysis is applied for 11-year time series of 25 permanent GNSS stations from 2006 to 2016 in the northwest network of Iran. We reveal that there is a nonlinear trend in some stations, although most stations have a linear trend. In addition, we found that a combination of logarithmic and exponential functions is the most appropriate post-seismic deformation model in our study region. The result of the noise analysis shows that the spatial filtering reduces the norm of post-fit residual vector by 19.34%, 17.51%, and 12.44% on average for the east, north, and up components, respectively. Furthermore, the uncertainties obtained from the combination of white and flicker noises at the east, north, and up components are 5.0, 4.8, and 4.4 times greater than those of the white noise model, respectively. The results indicate that the stations move horizontally with an average velocity of 36.0 ± 0.3 mm/yr in the azimuth of 52.66° NE which is compatible with velocities obtained from MIDAS. We obtained the vertical velocity of most stations in the range of -5 to 5 mm/yr. However, in three stations of GGSH, ORYH, and BNAB, which are in the proximity of Lake Urmia, the vertical velocities are estimated to be -80.9 mm/yr, -50.6 mm/yr, and -11.4 mm/yr, respectively. Moreover, we found that these three stations possess large periodic signal amplitudes in all three coordinate components as well as a nonlinear trend in the up component.

Altimetry by using GNSS (Global Navigation Satellite Systems) reflectometry is regarded as a new promising technique. One important step to utilize this technique is modeling the interferometric path (the difference between the direct and the reflected signal paths). This paper evaluates three models: the planar model, the planar model with tropospheric correction and a model based on ray-tracing. If decimeter level accuracy for water surface fluctuation is required, the planar model cannot be used when a receiver is at an altitude of a few hundred meters and observations are taken at low elevation angles. On the other hand, depending on the mapping function and zenith total delay in the tropospheric correction, the planar model with tropospheric correction can provide decimeter level accuracy for low altitude stations. If simulated observations with a 1 cm accuracy have been employed to estimate Sea Surface Height (SSH) by the model based on ray-tracing the numerical results present 1 cm as RMSE for phase retrieval and 5 cm for Doppler retrieval. The planar model with tropospheric correction does not yield RMSE better than some decimeters for the same condition.