Volume 19, Issue 1

Earth Orientation Parameters (EOP) are essential for monitoring Earth’s rotational irregularities, impacting satellite navigation, space exploration, and climate forecasting. This study introduces a hybrid prediction model combining least-squares (LS) and vector autoregression (VAR) to improve Earth’s Pole Coordinates ( x , y ) forecast accuracy. Using daily sampled IERS EOP 20 C04 data from 2013 to 2023, we conducted 1,000 yearly random trials, performing 48 forecasts per year. Our method evaluates six data combinations, including primary variables ( x , y ) and their derivatives ( x ̇ , y ̇ $\dot{x},\dot{y}$ ). Results show a systematic improvement in prediction accuracy, especially for ultra-short-term forecasts (10 days into future), with derivative information stabilizing the solutions. The best-performing combination ( x , y , x ̇ , y ̇ $x,y,\dot{x},\dot{y}$ ) achieved a mean absolute prediction error (MAPE) reduction (with respect to the reference data combination – x , y ) of up to 8 % for the y and 7 % for the x over a whole 30-day forecast horizon. These findings highlight the effectiveness of incorporating derivatives of polar motion time series into prediction procedure.

On 6 February 2023, at 01:17:35 and 10:24:49 UTC (LT = UTC + 03:00) two earthquakes with magnitude 7.8 (37.166° N, 37.042° E, depth ∼ 17.9 km) and 7.5 (38.024° N, 37.203° E, depth ∼ 10 km), respectively, heavily struck southern and central Turkey and northern and western Syria. The purpose of this study is to investigate the relation between pre-earthquake anomalies observed in different layers of the earth system and explore the earthquake mechanism of LAIC (Lithospheric Atmospheric Ionospheric Coupling) associated with earthquake precursors. To achieve this goal, electron density and temperature variations obtained from CSES-01 data in the Dobrovolsky’s area the Turkey earthquakes are analyzed in the period from November 1, 2022 to February 10, 2023. Since investigating the LAIC mechanism requires multi-precursor analysis, anomalies obtained from CSES-01 data were compared with the behavior of anomalies obtained from other lithospheric, atmospheric and ionospheric precursors in the same location and time of the study area. These anomalies that were analyzed in the previous study are: (1) TEC data obtained from GPS-GIM maps, (2) electron density and temperature variations obtained from Swarm satellites (Alpha, Bravo and Charlie) measurements, (3) Atmospheric data including water vapour, methane, ozone, CO and AOD obtained from the measurements of OMI and AIRS satellites, and (4) Lithospheric data including number of earthquakes obtained from USGS and also surface temperature obtained from the measurements of AIRS satellite. It should be noted that clear anomalies are observed between 1 and 5 days before the earthquake in electron density and temperature variations measured by CSES-01 during the day and night and they are in good agreement with the variations in the Swarm satellites data and GPS-TEC. The interesting and significant finding is that lithospheric anomalies are detected in the land surface temperature data in the time interval of 19–12 days before the earthquake, and then most of the atmospheric anomalies are observed in the time period of 10–5 days prior to the earthquake and at the end striking ionospheric anomalies are revealed during 5–1 days preceding the earthquake. Therefore, the results of this study confirm the sequence of appearing of earthquake precursors from the lower layers of the lithosphere to the upper layers of the ionosphere during 1–15 days before the earthquake, and finally proving the LAIC mechanism can significantly contribute to the efficiency and lower uncertainty of earthquake early warning systems in the future.

To evaluate the performance of the global geopotential models (GGMs) in a more unbiased way, ground-based gravity and GNSS/levelling datasets are highly required. In this study, the eight latest releases of the satellite-only and combined GGMs are evaluated on the regional scale using the available terrestrial gravity and GNSS/Levelling data over Sudan, considering the spectral consistency issue by applying the spectral enhancement method (SEM). The evaluation process consists of three stages: firstly, the eight GGMs are evaluated globally with each other by using different degree variances in terms of geoid heights, gravity anomalies, and signal-to-noise ratio (SNR); secondly, the GGMs are compared against the Earth Gravitational Model 2008 (EGM2008) on a regional scale over Sudan; thirdly, apply the SEM strategy by incorporating high (SEM_WITHOUT_RTM technique) and very-high (SEM technique) frequencies of the gravity field spectrum from the EGM2008 and high-resolution residual terrain model (RTM), respectively. For reliable robustness of the latter evaluation process, three different DEMs are used, namely, SRTM30, ASTER30, and GTOPO30. Our findings on the evaluation process using SEM_WITHOUT_RTM technique show improved gravity anomalies solutions regarding differences of standard deviations (STD) from 19–20.7 mGal to about 14 mGal. When applying the SEM technique, more improvements are achieved, providing STD differences in gravity anomalies and geoid heights of about 12 mGal and 45 cm, respectively. Among the three applied DEMs, it has been found that despite the slight refinements, the ASTER30 and GTOPO30 models show better performance than the SRTM30 model.

The real-time position accuracy of the Global Navigation Satellite System (GNSS)/Navigation with Indian Constellation (NavIC) receiver is limited by the dynamic behavior of the ionosphere, particularly in adverse conditions like solar flares and geomagnetic storms. The NavIC satellites broadcast dual coherent radio beacon signals on L5 (1,164.5 MHz) and S (2,472.5 MHz) bands for providing position, velocity, and timing services in all weather conditions. The Total Electron Content (TEC) and Rate of TEC Index (ROTI) are the potential indicators for characterizing the ionosphere and its irregularities. In this research work, the TEC and ROTI are computed from the code and carrier phase observations of the NavIC receiver located at Kurnool low latitude station (15.79° N, 78.07° E) with geomagnetic coordinates (7.30° N, 151.65° E). This paper presents a statistical study of TEC, ROTI, and the correlation between ROTI and NavIC positioning error during highly intense solar flares (X9.3 and X2.2) and geomagnetic storm conditions. Compared to quiet days mean TEC, the enhancement is 3 TECU due to X9.3 flares, and the maximum peak of TEC on storm day (September 8, 2017) is 80.92 TECU. Moreover, the correlation coefficient between ROTI and position error is 0.76 on a quiet day (September 4, 2017), 0.54 on an intense solar flares day (September 6, 2017), and 0.24 on a storm day (September 8, 2017), this indicates positional accuracy degradation on a geomagnetic storm day. The outcome of this research work would be helpful for investigating characteristics of the northern low latitude ionospheric irregularities and, in turn, useful for developing suitable ionospheric nowcasting/prediction models for GNSS applications.

The Wadi Hagul region in the eastern desert of Egypt is facing seismic hazards and increased human activity. This study uses remote sensing and geodetic methods to monitor and analyze recent deformation in the area. Interferometric Synthetic Aperture Radar (InSAR) data from the Sentinel-1A satellite and Global Navigation Satellite System (GNSS) data were combined to track surface movements and deformations accurately. The study analyzed InSAR data from February 4, 2020, to February 07, 2024, and GNSS data from the Wadi Hagul geodetic network established in July 2022 and monitored until January 2024. Despite the relatively short GNSS monitoring period, it provided valuable insights into recent deformation trends. By integrating data from ten GNSS stations, including International Geodetic stations (IGS), and InSAR scenes from the Sentinel-1A mission, the study estimated recent ground deformation in the region. The main objectives were to analyze recent crustal movements by identifying spatial and temporal patterns of deformation and assess implications for geological processes. In Key Findings, horizontal movement fluctuates between 0.5 and 2.5 ± 0.1 mm annually across the geodetic network. The estimated velocity of the area was 1.5–2 ± 0.5 mm per year. Integrating GNSS and InSAR data helped calculate movement rates along fault lines and create a fault map. In conclusion, the results suggest that while current deformation rates are moderate, they could increase significantly due to human activity, leading to higher seismic activity and potential earthquakes. Limiting human activity in the region is advisable to prevent negative impacts on nearby populated areas.

Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.

The Modified Ambiguity Function Approach (MAFA) implicitly conducts the search procedure of carrier phase GNSS integer ambiguity resolution (IAR) in the coordinate domain using the integer least squares (ILS) principle, i.e. MAFA-ILS. One of the still open scientific problems is an accurate definition of the search region, especially in the context of instantaneous IAR. In doing so, the float solution results, which encompass float position (FP) and its variance-covariance (VC) matrix, must be improved as these are necessary for defining the search region. For this reason, the ambiguity parameters are separately regularized, and then the baseline parameters are conditioned on regularized float ambiguities. The conditional-regularized estimation is thus designed, obtaining the regularized FP (RFP) and its VC-matrix. This solution is promising because its accuracy is enhanced in the sense of mean squared error (MSE) thanks to the improved precision at the cost of regularized bias. The optimal regularization parameter (RP) values obtained for ambiguity parameters balance the contributions of improved precision and bias in the regularized float baseline solution’s MSE. Therefore, the regularized search region is defined accurately in the coordinate domain to contain such approximate coordinates that more frequently give the correct ILS solution. It also contains fewer MAFA-ILS candidates, improving the search procedure’s numerical efficiency. The regularized ILS estimator performs well with the presence of bias, increasing the probability of correct IAR in the coordinate domain.

Relative GNSS positioning, a longstanding measurement standard, often incurs high manpower and equipment costs. Online Precise Point Positioning (PPP) presents a cost-effective alternative by minimizing these expenses. However, transitioning from the ITRF2014 to the TWD97[2010] coordinate system presents unique challenges. This study evaluates the efficacy of five PPP services – AUSPOS, OPUS, CSRS-PPP, magicGNSS, and RTX-PP – using 2018 Continuously Operating Reference Stations (CORS) data. Following a seven-parameter transformation, we systematically compared these services to identify the optimal solution for Taiwan’s geodetic survey needs. Our analysis reveals that RTX-PP offers superior performance, achieving 1 cm accuracy over 24 h and 4 cm accuracy over 1 h. Most stations met these accuracy standards, even during short observation intervals from September 9, 2018. Nonetheless, there is a 24.1 % likelihood of exceeding the 6 cm accuracy threshold due to variations in GNSS data quality. To address this, we employed G-Nut software to analyze station data quality across regions, recommending station selection based on data ratio and cycle slips to improve PPP solution accuracy effectively.

The ionospheric post-sunset irregularities are responsible for the discrepancies in the received global navigation satellite system (GNSS) signals to fluctuate the phase and amplitude resulting in scintillations in the respective components. Ionospheric scintillations reduce the signal quality and alter the signal reception time inducing position errors which is not preferable for the precise position applications. The level of ionospheric amplitude scintillation, quantified by the amplitude scintillation index ( S 4), is analyzed during the year 2022, which accentuates the ascending phase of solar cycle 25. For this, we analyzed scintillation intensity and occurrence percentage at a low latitude Indian location in India by employing all the available frequencies of the global positioning system (GPS) constellation. The scintillation distribution for each month is also observed which reveals that the autumn equinox seasons has high scintillation occurrence compared to the vernal equinox seasons. The impact of the scintillation on the three civilian signals (L1, L2 and L5) of the GPS constellation is also observed in terms of the scintillation intensity distribution. The cross-correlation of the S 4 index for these three signals reveals a strong correlation existing among them during strong scintillations whereas L2 and L5 signals portray a high correlation irrespective of signal intensities. In brief, the strong scintillation occurrence percentage is higher in the L5 signal compared to the L1 and L2 in contrast with weak scintillation, which is high in L1, followed by L2 and L5. Further, the analysis shows that the autumnal equinox has the highest percentage occurrence of strong scintillations (less than 10 % of the scintillation cases) compared to the vernal equinox whereas among solstice seasons June solstice presented the least scintillation occurrence at the location. The outcomes of this study instigate further analysis of scintillation occurrences from diverse GNSS frequencies covering diverse solar activity conditions for complementing the development of robust scintillation mitigation strategies across the low latitudes during the diverse scintillation conditions.