EGU General Assembly 2024  

The precision and accuracy of GPS/GNSS positioning has improved by considerably more than an order of magnitude over the course of my career, and the amount of data readily available (GNSS sites) has increased by several orders of magnitude. Over the last 40 years, geodesists have exploited this dramatic (and still continuing) increase in measurement capability to discover and study an ever-increasing set of phenomena. In the 1980s and early 1990s, the GPS satellite constellation was incomplete and there was only a sparse global tracking network. As a result, measurement noise limited rate accuracy to a few to several mm/yr, whereas today we are approaching accuracies of a few tenths of 1 mm/yr for long-term rates, and likely approaching the limit at which variability in surface loading makes motions fundamentally non-linear.

In this talk I will take a historical perspective, highlighting the improvement in measurement capabilities and our understanding of tectonic and other earth processes. At the beginning of my career, we focused on estimating rates of steady processes like rigid plate motions, the distribution of strain across rapidly-deforming plate boundary zones, or the displacements due to large earthquakes. We thought that over most of the Earth, motion and deformation mostly occurred linearly with time. The noise level in position solutions at that time was very high, and most non-linear variations in observed time series were considered to be noise either due to positioning error or to unstable geodetic monuments. While the deformation due to changing surface loads was recognized as a physical signal, knowledge of the changing loads was rudimentary and the signal was below the noise level in most cases. Today we recognize a wide variety of signals that produce a mix of linear and non-linear motions of the Earth, and positioning geodesy has become the essential tool for studying most of them. I have had the good fortune to work on measuring and understanding many of these processes, and I will discuss some of the highlights of the evolutionary path of positioning geodesy along with future perspectives. We have reached, or nearly reached, the point at which the approximation of linear motion breaks down because the measurement precision is now comparable to or smaller than the non-linear surface loading deformation over most of the planet. The coming years should see more exciting discoveries, but we must think broadly about the full range of geophysical signals that are contained within our data.

How to cite: Freymueller, J.: The Evolution of Positioning Accuracy and Linear vs. Non-linear Motions of the Earth, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4118, https://doi.org/10.5194/egusphere-egu24-4118, 2024.

The Gravity Recovery and Climate Experiment Satellite mission has provided estimates of spatiotemporal changes in the Earth’s gravity field, which represents mass transport near the surface of the Earth. This unique satellite mission has been used to study groundwater depletion, lake volume changes, sea level rise, and the viscoelastic response of solid Earth to glacial cycles; glacial isostatic adjustment. In this talk, I will share my experiences: published, unpublished, and even incomplete, in using GRACE data for Earth system science and emphasize the power and limitations of this unique satellite mission. 

How to cite: Vishwakarma, B. D.: GRACE for Earth system science: novel insights into hydrology, sea level rise, and solid Earth uplift, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11422, https://doi.org/10.5194/egusphere-egu24-11422, 2024.

A multitude of basis functions is available for modelling the gravitational field based on satellite data. The Regularized Functional Matching Pursuit (RFMP) algorithm, which has been developed by the Geomathematics Group Siegen, proved to be able to combine different sets of such trial functions. For this purpose, a dictionary is built as a redundant union of different established basis systems (such as spherical harmonics, radial basis functions and Slepian functions). In an iterative scheme, a best basis is selected by minimizing a Tikhonov-Phillips functional. In a recent add-on (the LRFMP), the dictionary does not have to be discretely predefined but can be learned as part of the algorithm. This is implemented as a non-linear optimization problem. The LRFMP has several benefits, which will be demonstrated in the presentation, where we show numerical tests regarding the inversion of noisy gravity data on real satellite orbits.

References:

N. Schneider, V. Michel and N. Sneeuw, High-dimensional experiments for the downward continuation using the LRFMP algorithm, preprint available at http://arxiv.org/abs/2308.04167, 2023.

N. Schneider, V. Michel: A dictionary learning add-on for spherical downward continuation, Journal of Geodesy, 96 (2022), article 21 (22pp). 

Source Code:

N. Schneider, (L)IPMP source code for gravitational field modelling, v2-dc-2023. Zenodo. https://doi.org/10.5281/zenodo.8223771, 2023. 

How to cite: Michel, V., Schneider, N., and Sneeuw, N.: Dictionary learning for downward continuation of gravity data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1903, https://doi.org/10.5194/egusphere-egu24-1903, 2024.

Integral transformations are a useful mathematical apparatus for modelling the gravitational field. They represent the mathematical basis for the formulation of integral estimators of gravity field values, including error propagation. The theoretical and practical aspects of integral transformations traditionally used for the calculation of geoid/quasi-geoid heights in geodesy, such as Stokes’ and Hotine’s integral transformations, have already been studied. However, theoretical and practical concepts regarding other integral transformations, including non-isotropic (azimuth-dependent) transformations, have not yet been explored. One of the basic assumptions of integral transformations is global data coverage. However, the availability of ground measurements is frequently limited. In practice, the global integral is divided into two complementary regions, namely the near and far zones. Non-negligible systematic effects of data in the far zone require accurate evaluation. For this purpose, a new software library entitled FarZone4IT is being created in the MATLAB environment to calculate far-zone effects in integral transformations for gravitational potential gradients up to the third order. The library contains scripts for the calculation of integral kernels, error kernels, truncation error coefficients, and far zone effects for a selected set of input parameters. This contribution concerns the implementation of theoretical equations defining far zone effects and the subsequent numerical testing of the library functionality. Closed-loop tests were carried out using gravitational potential functionals generated from a synthetic model of the Earth's gravity field.

How to cite: Pitonak, M., Trnka, P., Belinger, J., Novák, P., and Sprlak, M.: FarZone4IT: A new software for the calculation of far–zone effects for spherical integral, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2824, https://doi.org/10.5194/egusphere-egu24-2824, 2024.

The numerical integration method has been routinely used by major institutions worldwide (for example, NASA Goddard Space Flight Center and GFZ) to produce global gravitational models from satellite tracking measurements. Such Earth’s gravitational products have found widest possible multidisciplinary applications. The method is essentially implemented by solving the differential equations of the partial derivatives of the orbit of a satellite with respect to the unknown force parameters under the zero initial conditions. From the statistical point of view, satellite gravimetry from satellite tracking is essentially to estimate the unknown parameters in the Newton’s nonlinear differential equations from satellite tracking measurements --- the mathematical foundation for satellite gravimetry from tracking. From this perspective, it is rather trivial to prove that the numerical integration method, originating from Gronwall on Ann Math almost 100 years ago and currently implemented and used in mathematics/statistics, chemistry/physics, and satellite gravimetry, is groundless, even though, up to this moment, many researchers in the geoscientific community still have problems in understanding this side point of my research. In this talk, we focus on presenting three different methods to derive local solutions to the Newton’s nonlinear differential equations of motion of satellites, given unknown initial values and unknown force parameters. They are mathematically correct and can be used to estimate unknown differential equation parameters, with applications in gravitational modelling from satellite tracking measurements as a typical example in geodesy. These solution methods are generally applicable to any differential equations with unknown parameters. More precisely, we develop the measurement-based perturbation theory and construct global uniformly convergent solutions to the Newton’s nonlinear differential equations of motion of satellites, given unknown initial values and unknown force parameters. From the physical point of view, the global uniform convergence of the solutions implies that they are able to exploit the complete/full advantages of unprecedented high accuracy and continuity of satellite orbits of arbitrary length and thus will automatically guarantee theoretically the production of a high-precision high-resolution global standard gravitational models from satellite tracking measurements of any types. Finally, we develop an alternative method by reformulating the problem of estimating unknown differential equation parameters, or the mixed initial-boundary value problem of satellite gravimetry with unknown initial values and unknown force parameters as a standard condition adjustment model with unknown parameters.
Xu P (2018) Measurement-based perturbation theory and differential equation parameter estimation with applications to satellite gravimetry. Commun Nonlinear Sci Numer Simulat, 59, 515-543. DOI 10.1016/j.cnsns.2017.11.021
Xu P (2008) Position and velocity perturbations for the determination of geopotential from space geodetic measurements. Celest Mech Dyn Astr, 100, 231–249.
Xu P (2009) Zero initial partial derivatives of satellite orbits with respect to force parameters violate the physics of motion of celestial bodies. Sci China Ser D, 52, 562–566.
Xu P (2012) Mathematical challenges arising from earth-space observation: mixed integer linear models, measurement-based perturbation theory and data assimilation for ill-posed problems. Invited talk, joint mathematical meeting of American mathematical society, Boston, January 4–7.

How to cite: Xu, P.: Statistical estimation of differential equation parameters: the mathematical foundation for satellite gravimetry from tracking, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3550, https://doi.org/10.5194/egusphere-egu24-3550, 2024.

Integral transformations of the gravitational field gradients are defined over the entire solid angle on the surface of the sphere. Despite the indisputable progress in satellite gravimetry and gradiometry, gravity field focused satellite missions allow accurate determination of the gravity field with a spatial resolution of 100 km, i.e. only in its long-wavelength part. However, there is also a need for high-resolution gravity field models at regional, national or continental scales, especially concerning the determination of the quasi-geoid or geoid. On the other hand, potential weakness of ground-based data is the long-wavelength gravity field accuracy and limited availability due to several constraints (e.g. deserts, lakes and large rivers, forests, or lack of goodwill between neighboring countries to share sensitive data). The ideal scenario combines ground and satellite data that complement each other.

In this contribution, relations defining the estimation of the global root mean square errors of selected gravitational field functionals using integral transformations will be derived and presented. For practical calculation, knowledge about the accuracy of measured terrestrial data and formal errors of global satellite models of the Earth's gravity field will be utilized.

How to cite: Belinger, J., Pitonak, M., Trnka, P., Novak, P., and Sprlak, M.: Estimation of the Global Root Mean Square Error of Selected Gravitational Field Functionals Calculated by Integral Transforms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4098, https://doi.org/10.5194/egusphere-egu24-4098, 2024.

The need for the determination of the parameters of an equipotential rotational ellipsoid of revolution that approximates closer and closer to the Earth requires analysis of the most currently available data. The main objective of this study is to examine such new data and to perform an adjustment technique to estimate the parameters and their standard deviations of the mean Earth ellipsoid. The parameters considered are the geocentric gravitational constant, the angular velocity, the geoidal potential, the dynamical form factor, and the major and minor semi-axes. A-priori estimates of these quantities, which may be determined independently, are treated as “observations” and their adjusted values from a weighted least-squares procedure are presented. Since a level ellipsoid of revolution and its gravity field are completely determined by four constants, we use two non-linear condition equations to relate the six parameters for performing the adjustment. Among the products of the adjustment is the correlation matrix of the adjusted values of parameters, which helps us in the selection of a consistent set of four parameters for the definition of a new Geodetic Reference System (GRS). After the selection of the four parameters, we compute by Newton’s method the two derived parameters and estimate their standard deviations by applying the law of propagation of variances. Furthermore, all the derived geometric and physical constants are computed from the defining constants of a GRS, by means of closed formulas. Finally, in order to yield numerical results of high precision, all computations are executed in computer algebra system software using variable precision floating point numbers.

How to cite: Panou, G. and Marti, U.: Current adjustment of the mean Earth ellipsoid parameters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4313, https://doi.org/10.5194/egusphere-egu24-4313, 2024.

It is widely known that the geodetic networks (both terrestrial and space) suffer from the so-called rank deficiency. This is the algebraic expression of the weakness of the observations to sense all the necessary information for the reference system definition (in terms of origin scale and orientation). For example, the SLR technique is sensitive to the origin and scale, while the orientation can be only externally defined. On the other hand, the traditional quasar-based VLBI does not sense either origin and orientation but only scale.

In general, the rank deficiency is remedied by the use of the so-called constraints. The constraints can be divided into two major categories: a. The minimum constraints, where they just treat the rank deficiency problem (as the word minimum dictates) and do not interfere with the shape of the network, and b. the over-constraints, which do not only solve the rank deficiency but alter the shape of the geodetic network.

While the minimum constraint solutions are widely discussed in the geodetic literature, regarding their nature, the over-constraints' physical meaning is not so clear (if not vague). The present study aims to provide a physical meaning of the over-constraints solution, under the prism of its stochastic interpretation.

How to cite: Ampatzidis, D., Balidakis, K., and Tsimerikas, A.: On the physical meaning of geodetic networks’ over-constraints solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5214, https://doi.org/10.5194/egusphere-egu24-5214, 2024.

The study presents along-track nonlinear diffusion filtering of the airborne gravity data. At first, the provided airborne gravity disturbances from the GRAV-D campaign are transformed into the airborne complete Bouguer disturbances (CBD). This aims to reduce a correlation of the filtered data with the topography. Then the nonlinear diffusion filtering in 1D based on the Perona-Malik model is applied. In this model, a diffusivity coefficient depends on the edge detector, which allows reducing noise while preserving important gradients in the filtered data. As a numerical method we use the finite volume method (FVM). The derived semi-implicit numerical scheme leads to a three-diagonal system matrix that is solved in every iterative step. Here the diffusivity coefficients are updated in every step by new values of the edge detector recomputed from the previous solution.

The numerical experiment presents the along-track nonlinear filtering of the airborne CBD in high mountainous area of the ‘Colorado geoid experiment’. Afterwards, the along-track filtered data are gridded into a 2D map of the airborne CBD. The obtained results show that an appropriate choice of a sensitivity parameter of the diffusivity coefficient can better detect significant structures in the airborne CBD, especially their edges that are usually smoothed by the Gaussian filtering. Finally, the filtered and gridded airborne CBD are backward transformed into the airborne gravity disturbances.

How to cite: Cunderlik, R., Zahorec, P., and Papčo, J.: Along-track nonlinear filtering of airborne gravity data from the GRAV-D campaign: case study for the ‘Colorado geoid experiment’, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5215, https://doi.org/10.5194/egusphere-egu24-5215, 2024.

Gravity field modelling of irregularly shaped bodies such as the Earth's Moon is a challenging task that can reveal both the strong and weak points of each modelling technique. In our approach we will develop a numerical approach based on the finite element method (FEM) and compare the obtained solutions with the solution by the spectral approach that relies on spherical harmonics. In this way, we aim to study whether the numerical methods such as FEM can overcome the limitations of the spherical-harmonic-based approaches, namely their divergence in the vicinity of the gravitating body. We hope that the presented developed approach could form a valuable alternative to the spherical harmonics.

How to cite: Macak, M., Šprlák, M., and Minarechová, Z.: Modelling gravity field of irregularly shaped bodies by numerical methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5272, https://doi.org/10.5194/egusphere-egu24-5272, 2024.

Studying the gravitational effects of the Earth's topography and crustal layers is a fundamental topic in gravity field modeling in geodesy and geophysics. The introduction of gravitational curvatures (GC), which are the third-order derivatives of the gravitational potential (GP), has recently broadened theoretical research on gravitational effects. Using tensor analysis, this paper comes up with a general formula for the physical parts of the third-order tensor of the potential in cylindrical coordinates. Then, the expressions for the GC of a vertical cylindrical prism are accordingly derived in cylindrical coordinates. Based on the relation among the vertical cylindrical prism, cylindrical shell, and cylinder, the analytical expressions for gravitational effects up to the GC of a vertical cylindrical shell and a cylinder are derived when the computation point is located on the Z-axis no matter whether it is situated below, inside, or above the cylindrical shell and cylinder. Laplace's equation has been adopted to confirm the correctness of the newly derived formulas of the GC. In addition, a benchmark of a cylindrical shell discretized into cylindrical prisms is proposed to reveal the numerical properties of derived GC formulas with the computation point located on the Z-axis. Numerical results reveal that when the computation point's height increases, the relative and absolute errors of the GP, gravitational vector (GV), gravitational gradient tensor (GGT), and GC decrease, in which the relative errors in log₁₀ scale of the nonzero GP, GV, GGT, and GC components are approximately less than -2 when the computation is located below, inside, and above the cylindrical shell. These newly derived formulas lay the theoretical foundation for the GC in cylindrical coordinates and help to investigate the potential applications of the GC in geodesy and geophysics. This new benchmark can become the standard for testing the correctness of the gravitational effects of the cylindrical prism using different numerical algorithms in cylindrical coordinates in practical applications.

How to cite: Deng, X.: A benchmark for gravitational potential up to its third-order derivatives of a vertical cylindrical shell discretized into cylindrical prisms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5992, https://doi.org/10.5194/egusphere-egu24-5992, 2024.

Atmospheric refraction is the main source of deviations for laser-based sensors. Having a profound understanding of refraction, and the knowledge of the geometry of the line of sight, assists in identifying an accurate model to correct this error. The height of the point, similar to two other planar coordinates, is also impacted as a result of the refracted beam line. The height can be obtained through numerous geodetic measurement approaches such as spirit levelling or trigonometric levelling. An empirical refraction model was proposed in 1984 to better quantify the observations of trigonometric levelling. In this research, we propose a developed empirical model for the La Valette Landslide (Southeast French Alps) to determine the height of the target benchmarks in a landslide zone. The landslide is located in the Ubaye Valley, where the thrust fault of clay-shale sediments at the bottom and sandstone and limestone competent rocks at the top, control the occurrence of landsliding in this region. The deformation is attributed to the low resistance of the slope material and the increase in pore-fluid pressure resulting from the different hydraulic conductivities of the two geological units. The landslide has been monitored over many years, with several remote sensing techniques, and the task is undertaken as a part of the French Landslide Observation Service - OMIV. Since September 2019, an automated total station Long-Range Trimble S9 has been monitoring 54 reflectors’ positions every 1 to 3 hours with respect to three reference control points. The targets have been uniformly distributed over the landslides at distance from 350m to 2300m from the monitoring station, and at elevations varying from 1300m to 2100m. The research determined the point heights using the empirical trigonometric levelling model with the addition of an improved refraction model incorporating the development refraction correction for the observed angles of the control points.

How to cite: Sabzali, M., Ferhat, G., Pilgrim, L., Khaki, M., and Malet, J.-P.: Developed empirical refraction model for precise trigonometric levelling of the La Valette Landslide, France, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6872, https://doi.org/10.5194/egusphere-egu24-6872, 2024.

The numerical approach for solving the nonlinear geodetic boundary value problem based on the finite element method with mapped infinite elements and itterative procedure is developed and implemented. In this approach, the 3D semi-infinite domain outside the Earth is bounded only by the triangular discretization of the whole Earth's surface and extends to infinity. Then the BVP consists of the Laplace equation for unknown disturbing potential which holds in the domain, the nonlinear boundary condition given directly at computational nodes on the Earth's surface, and regularity of the disturbing potential at infinity. In experiments, a convergence of the proposed numerical scheme to the exact solution is tested and then the numerical study is focused on a reconstruction of the harmonic function above the Earth's topography.

How to cite: Minarechová, Z., Macák, M., Čunderlík, R., and Mikula, K.: On solving the nonlinear geodetic boundary value problem using mapped infinite elements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7346, https://doi.org/10.5194/egusphere-egu24-7346, 2024.

Tesseroid and radial columns decomposition of the undersea relief strategies have been considered to recover the seafloor topography by Kalman Filter (KF) inversion of gravity data in the case of the Great Meteor seamount located in the North Atlantic ocean. These both modeling approaches are shown to be equivalent at high grid sampling rate (<1'). Different types of gravity data functionals for geoid height anomaly, vertical gravity component and gravity gradient (or tensor) are analyzed by spectral decomposition and combined to retrieve most detailed 3-D seafloor topography solutions, as gravity gradient data provide short-wavelength information to have access to high-resolution details. Besides only the vertical gravity tensor Vzz is usually inverted in previous field-related studies, considering up to six components of the gravity gradient is tested for improving the accuracy of the KF solution. The iterative KF scheme has been optimized and parallelized using C++ Armadillo software to accelerate the determination of a very large number of juxtaposed topographic heights.

How to cite: fuseau, D., Seoane, L., Ramillien, G., Darrozes, J., Plazolles, B., Rouxel, D., Schmitt, T., and Salaün, C.: Seafloor topography recovery improved by combination of different gravity data functionals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7800, https://doi.org/10.5194/egusphere-egu24-7800, 2024.

The Earth’s system is warming due to natural and human driven climate change. Observing, analyzing and understanding the associated geophysical processes is important in order to improve prediction of future changes and mitigate impacts on society and infrastructure. Investigating individual climate processes, such as sea level change, often requires partitioning of the total signal for identifying sub-signals and drivers; in the sea level example these could be trend and seasonal signals or impacts from the El Niño Southern Oscillation (ENSO).

A commonly applied method is the (real) Principal Component Analysis (PCA), which factorizes a given input dataset into time-invariant Empirical Orthogonal Functions (EOF), i.e. spatial patterns, and time-variable Principal Components (PC) based on the most dominant eigenvalues. However, this real-EOF analysis assumes more or less static patterns over time and, thus, lacks the ability to capture temporal variations in the patterns. This can be circumvented by the application of complex or Hermitian EOF analysis, which also enables capturing phase shifts or in other words allows for time-varying spatial patterns.

Here, we present first results from a novel approach utilizing dynamic time warping (DTW) for extracting dominant modes in the form of spatially distributed amplitudes and lags with respect to a ‘base curve’. While classic PCA methods are sensitive to outlier influence on the partitioning, our approach represents a robust alternative. Furthermore, base curves are computed that represent spatial modes via traversal matrices, which act as extensions of the base curves to capture individual lag. We introduce our new approach, compare to complex/Hermitian EOF, explain the numerical scheme, and present some first results based on gridded sea level change data.

How to cite: Uebbing, B., Höckendorff, J., Jungheim, C., Driemel, A., Sohler, C., and Kusche, J.: An alternative to PCA utilizing Dynamic Time Warping, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8993, https://doi.org/10.5194/egusphere-egu24-8993, 2024.

During the last decades, several inversion approaches have been proposed to derive sea floor topography from satellite-based gravity data. Unfortunately, the most accurate non linear ones are based on iterative schemes that remain very time-consuming, especially if the number of topographic heights to be fitted is very important, e.g. when the oceanic domain is large and/or the gravity data is geographically dense and thus the maximum grid resolution to be accessible is high. Our strategy of computation is to decompose the total area into geographical cells that are overlapped to cancel the edge effects. The reference ocean depth given by GEBCO and the elastic thickness for regional compensation in function of the square root of the age of the oceanic crust are assumed to be constant in each cell. The initial inversion code has been translated into C++ and optimized using Armadillo software and LAPACK library to obtain a gain of speed of 1000 for a large region such as the complete North Atlantic Ocean (-54,-26,18,37). Post-fit and absolute errors are typically less than 200 m and 50 m r.m.s. respectively. These new detailed maps of bathymetry represent a precious source of information for geophysical applications. 

How to cite: Seoane, L., Fuseau, D., Ramillien, G., Darrozes, J., Plazolles, B., Rouxel, D., Salaün, C., and Schmitt, T.: Fast seafloor topography mapping of large oceanic provinces by optimization/parallelization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9318, https://doi.org/10.5194/egusphere-egu24-9318, 2024.

Although centralized coordinates are applied in geodetic coordinate transformations implicitly or explicitly, the centering strategy has not been comprehensively investigated from the theoretical perspective. We rigorously model and extend the empirically used three center strategies based on different models:

• Original model: Based on the partition representations of the solution, we propose a modified iteration policy, which reduces the parameter number and improves numerical stability during iteration. Also, its simplified version is analyzed when the cofactor matrix has the Kronecker product structures. It can be regarded as the extension of the work of Teunissen, since we essentially follow the same idea of partitioning the transformation parameters and the translation parameters, but more general covariance matrix structures are investigated in our consideration.
• Shifting model: With the partitioned solution forms, we prove the estimated transformation matrix and the residual vector are translational invariant. For iteration, with the classical iteration policy, the shifts should be chosen properly; with the modified iteration policy, there is no restriction since it is numerically equivalent to the original model. In addition, this model shows the feasibility of conducting the adjustment with the centralized coordinates and the original stochastic model.
• Translation elimination model: By multiplying the transformation relation with a specific matrix from both sides, we formulate the translation elimination model with the coordinates centralized and the translation parameters eliminated. With this model reduction, the covariance matrix has also been transformed since the observation equations are comprised of coordinate combinations. In addition, Leick’s model reduction strategy is a special case of this model, which is conducted by subtracting one particular observation equation from the remaining equations. 

Test computations with different weight structures show the validity of these strategies.

How to cite: Zhan, W., Fang, X., and Zeng, W.: Center strategies for universal geodetic transformations: modified iteration policy and two alternative models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13902, https://doi.org/10.5194/egusphere-egu24-13902, 2024.

The geoid model over dry land areas is determined from data observed at or above the Earth’s surface. Observable quantities include various functional of the disturbing potential defined as the difference between the real and model (normal) gravity potential. Transformation of the measured data into the sought-after, but directly unobservable gravity potential is often carried out using mathematical tools of the potential theory. An example of such a transformation is the well-known Hotine integral transform that transforms disturbing gravity, i.e., the first-order vertical gradient of the disturbing potential (Stokes formula is applied to anomalous gravity which is still often encountered in geodesy). Higher-order gradients of the Earth's gravitational potential have been collected by sensors on board aircraft or low-orbiting satellites. This advancement in data availability has resulted in the formulation of new tools based on Green's integral transforms and equations. Associated deterministic models have been well developed, tested, and successfully implemented. However, stochastic models for estimating uncertainties in sought values have only been partially developed. These uncertainties should reflect the inevitable implementation and approximation errors, the propagation of formal errors, as well as external accuracy estimation if relevant independent reference values are available. This contribution discusses mathematical models that can be used to estimate various types of uncertainties related to integral-based transformations of the measured potential gradients into the disturbing potential.

How to cite: Novak, P., Eshagh, M., and Pitoňák, M.: Uncertainties associated with integral-based transforms of measured potential gradients, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15596, https://doi.org/10.5194/egusphere-egu24-15596, 2024.

The problem of determining the anomalous potential T on the earth's surface can be solved on the basis of various initial available data: gravity anomalies Δg and gravity disturbances δg, their vertical derivatives ∂(Δg)/∂H, ∂(δg)/∂H, gravity gradient anomalies Δ(∂g/∂H) etc. Existing methods of such BVP solution use the integral kernels, elaborated for the sphere and ellipsoid. The attempts to determine the real geoid are closely related to the direct problems of the potential theory, when the mass distribution is assumed to be approximately known (in Molodensky's theory the earth's crust density is used in topographic reductions only for better anomalies interpolation).

Using of the two tipes of related gravity data could be considered as a control, e.g., the anomalous potential T from the gravity anomalies Δg can be used to obtain the gravity disturbances δg, from which we must also get the same anomalous potential T. For the real Earth's surface more flexible is the method of integral equations.

(The prime sign indicates a point on the telluroid.)

Molodensky's integral equation for the simple layer density (distributed on the Earth's surface) using the gravity disturbances (1) and gravity anomalies (2) is known, but is usually solved indirectly with an introduction of the small parameter (the Molodensky's parameter k or/and ellipsoid eccentricity e), that lead to the series solution with the well-known integrals. Being the Fredholm equation, the Molodensky's integral equation itself can be solved directly by successive approximations in the ellipsoidal coordinate systems as well as in the spherical one. The integration procedure is probably longer, but any step is of the same type. Then the anomalous potential can be calculated by integration in the form (3).

Figure 1. Simple layers distributed with densities φ on the Earth’s surface S (green). Auxiliary simple layer density χ is distributed on the mean Earth’s sphere Ω  with radius R. In general case, the normal n to the surface, inclination angle α and the radius-vector ρ are slightly different in the two cases. E - reference ellipsoid (blue), the telluroid Σ (red), g - plumb-line.

Some real estimates are possible on the surface and gravity field models. In this study we use the Earth's model in the form of mascons for the surface and gravity field, see Fig. 2. We know all the elements of the anomalous field, the precise coordinates of the points with data and so we can estimate the real theoretical accuracy of the formulas and the number of iterations.

Figure 2. The scheme of the mass forming the anomalous field

In case of gravity anomalies the integration procedure can be considered as an integration over the successively refined  boundary surface. It is enough to find the density distribution of a simple layer on a smoothed surface constructed from the heights of points in the form of the sum of the normal height (from leveling) and the height anomaly from the Stokes approximation.

How to cite: Popadyev, V. and Sermiagin, R.: Control of the accuracy of the Molodensky's integral equation for the gravity anomalies and disturbances on the Earth's models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18996, https://doi.org/10.5194/egusphere-egu24-18996, 2024.

Current theory of integer inference, which is key to many carrier-phase driven observing systems, consists of a rich variety of different estimation principles, each with their own optimality and statistical properties. The various estimators can be classified into different classes of estimators, of which the integer estimation class is the smallest and the integer equivariant class the largest. Although the estimation theory for mixed integer models has matured significantly, there are still some important identifiable open unsolved problems. One of those concerns the way in which in practice the integer-equivariant baseline maximizer of the carrier-phase integer ambiguity function is resolved. Most of the methods employed in practice use rather ad hoc, brute-force grid search techniques, whereby proper considerations of the intrinsic properties of the objective function are lacking. As a result none of the available techniques have a demonstrated proven guarantee of global convergence. In this contribution we will present a novel algorithm for the numerical maximization of the multivariate carrier-phase integer ambiguity function. Our proposed method, which has finite termination with a guaranteed user-defined tolerance, is developed from combining the branch-and-bound principle with the projected-gradient-descent methodology, for which a special continuous differentiable convex-relaxation of the critical elements of the ambiguity objective function is constructed. The methodology of these three constituents is described in an integrated manner and numerical results are provided to illustrate the theory.

How to cite: Massarweh, L. and Teunissen, P.: A novel globally convergent maximizer for the multivariate carrier-phase integer ambiguity function, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19367, https://doi.org/10.5194/egusphere-egu24-19367, 2024.

Digital Elevation Models (DEMs) are fundamental components in geodetic computations, serving as key inputs in geoid modelling processes. Mostly, the freely available DEMs are used for geoid modelling without considering its impact on the developed geoid. Considering the criticality of terrain and downward continuation corrections calculated from DEMs, this research work explores the sensitivity of geoid models to variations in DEMs, aiming to elucidate the impact of different DEMs on the accuracy and precision of geoid modelling. The study aims at a comprehensive sensitivity analysis framework to assess the influence of DEM resolution, terrain representation, and fitting methods on regional geoid modelling in India. The selected study area consists of three states of India (Haryana, Punjab, and Himachal Pradesh) bounding approximately 169,500 km² of the area (73.5≤λ≤77.5 and 29≤ϕ≤33 of longitude and latitude, respectively) of vast topography including Indo-Gangetic Plain, Shivalik Hills, lofty hills, deep valleys, and verdant forests. This study employs Least Squares Modification of Stokes formula with Additive Corrections (LSMSAC) method developed by the Royal Institute of Technology, Sweden and evaluates four DEMs, Cartosat, Merit, Palsar and SRTM. For surface correction (fitting), 24 GNSS points are used with 4,5 & 7 parameter fitting models which is validated with 15 other GNSS point based on elementary statistics. This investigation offers insights into selection of an optimal DEM by obtaining RMSE between developed geoid using various DEMs and 15 GNSS points. Based on the obtained results by considering above-mentioned DEMs with various fitting models, the Cartosat DEM outperformed other DEMs by obtaining lowest RMSE (0.078603m) with 7 parameter fitting model. Surprisingly, the lowest RMSE (0.064557m) is obtained by Cartosat DEM with 4 parameter model which could be because of Cartosat DEM being an India specific DEM. While comparing the efficacy of developed geoid between globally available DEM, Merit performed best with lowest RMSE (0.078657m). Out of 90 combinations of each DEM for various sets of Degree/order of Global Geopotential model (GGM), integration cap size; the best result is obtained by 180 degree of GGM and 0.8 integration cap size for each DEM. Presented study improved our understanding in assessing the sensitivity of geoid models to various DEMs. This research aids geodesists, geophysicists, and remote sensing specialists in making informed decisions while selecting a suitable DEM for geoid computations. The findings presented in this paper contribute to the ongoing efforts to enhance the precision and reliability of geoid modelling techniques, ultimately improving our understanding of Earth's gravity field.

How to cite: Kumar, A., maurya, V., and dwivedi, R.: Sensitivity Analysis of Digital Elevation Models in Geoid Modelling for Indian region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1087, https://doi.org/10.5194/egusphere-egu24-1087, 2024.

Numerical methods like the Finite Element Methods (FEM) or Finite Element Methods (FVM) are widely used in many engineering applications to solve boundary value problems that are hard to find rigorous analytical solutions. These numerical methods have been also applied in geodesy in many previous studies regardless of its huge computation demands. They have arisen due to the fact that the upper boundary condition was usually set up at the satellite orbit level, hundreds of kilometers above the Earth. The relatively large distances between the bottom boundary Earth' s surface, and the upper boundary even exacerbates the computation loads because of the required discretization in between. Considering that many areas such as the US have uniformly distributed airborne gravity data that are just a few kilometers above the topography, we propose to move the upper boundary from the satellite orbit level to the mean flight level of the airborne gravimetry. The significant reduction in altitudes, dramatically saves the large computation demands in previous FEM or FVM computations. This paper demonstrates this benefit by using FVM for both simulated data and real data in the target area. In the simulated case, the FVM numerical results show that about an order of magnitude precision improvement can be obtained when moving the upper boundary from 250km to 10km, the maximum altitude of GRAV-D. For the real data sets, 2-3 cm level of accurate quasi geoid model can be obtained depending on different schemes used to model the topographic mass. The paper also demonstrates how to find the upper layer in case no airborne data is available. Last but not the least, this study provides a 3D representation of the entire local gravity field instead of a single 2D surface, the (quasi) geoid.

How to cite: Li, X., Cunderlik, R., Lin, M., Macak, M., Zahorec, P., Papco, J., Minarechova, Z., Krcmaric, J., and Roman, D.: FVM: A Good Match to Airborne Gravimetry?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1346, https://doi.org/10.5194/egusphere-egu24-1346, 2024.

This groundbreaking study addresses the imperative to comprehend gravity shifts resulting from Groundwater Storage (GWS) variations in the Arabian Peninsula. Despite the critical importance of water resource sustainability and its relationship with gravity, limited research emphasizes the need for expanded exploration. The investigation explores the impact of GWS extraction on the gravity field, utilizing Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data in addition to validation using WaterGAP Global Hydrology Model (WGHM). Spanning April 2002 to June 2023, the study predicts GWS trends over the next decade using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model.  The comprehensive time-series Analysis reveals a huge GRACE-derived GWS trend about -4.90±0.32 mm/year during the period of study. This significantly influences the gravity anomaly GA values, demonstrating a corresponding fluctuation in GWS time series. The projected GWS indicates a depletion rate of 14.51 km³/year over the next decade. The correlation between GWS and GA is substantial at 0.80, while GA and rainfall correlation is negligible due to low precipitation rates. Employing multiple linear regression explains 80.61% of the variance in gravity anomaly due to GWS, precipitation, and evapotranspiration. The study investigates climate change factors—precipitation, temperature, and evapotranspiration—providing a holistic understanding of forces shaping GWS variations. Precipitation and evapotranspiration exhibit nearly equal values, limiting GWS replenishment opportunities. This research holds significance in studying extensive GWS withdrawal in the Arabian Peninsula, particularly concerning crust mass stability. Integrating GRACE and hydrological models’ datasets furnishes a comprehensive understanding, contributing valuable foresight into the future trajectory of GWS. The results illuminate intricate relationships between GWS, gravity anomalies, and climate factors, presenting a robust framework for sustainable water resource management. 

How to cite: Mohasseb, H. A., Shen, W., Abd-Elmotaal, H. A., and Jiao, J.: Assessing Groundwater Sustainability in the Arabian Peninsula and its Impact on Gravity Fields through GRACE Measurements , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2185, https://doi.org/10.5194/egusphere-egu24-2185, 2024.

Although machine learning has become increasingly important in geodesy related fields such as geophysics, seismology and remote sensing, its applications in geodesy, especially in physical geodesy, are still in its early stages. The main reason for this can be attributed to the black box nature of pure data-driven machine learning, which lacks physical interpretability and credibility, making it difficult for machine learning approaches to be used in physical geodesy that takes reliability and accuracy as its core criteria. Physics-Informed Neural Networks (PINNs) is a class of deep learning algorithms aims to seamlessly integrate data and physical prior knowledge including ordinary or partial differential equations, it can yield more physically interpretable machine learning models that provide robust and accurate predictions. We present the PINN approach for gravimetric geoid modeling from Earth gravity model, terrestrial and airborne gravity datasets. A convolutional neural network (CNN) deep learning architecture is used, gravity measurements and physical laws are integrated by embedding the Laplace’s equation of disturbing potential and the fundamental equation of gravity anomaly into the loss function of the neural network using automatic differentiation. The PINN based geoid computation approach is tested in the area of the Colorado 1-cm geoid experiment. Simulated gravity observations and GNSS leveling derived geoid heights based on EIGEN-6C4 are used to validate the theoretical correctness and validity of the proposed PINN approach, and its performance on precise geoid modeling in this challenging area is evaluated using the actual terrestrial and airborne gravity observations, GNSS leveling measured geoid heights and high resolution DEM provided by NGS/NOAA.

How to cite: Jiang, T., Tu, Z., and Dang, Y.: Physics-Informed Neural Networks for geoid modeling: preliminary results in Colorado, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4068, https://doi.org/10.5194/egusphere-egu24-4068, 2024.

The coverage of the gravity data plays an important role in the geoid determination process. Still some parts in the world have poor gravity data coverage, with sometimes, large data gaps, e.g., Africa. In this paper we study the effect of implementing Moho depths on the gravity interpolation at large data gaps. For this reason, and in order to qualify that effect, an artificial data gap has been made in the gravity data set of Austria (originally with perfect gravity data coverage). The outcome of the present study is essential for the IAG sub-commission on the gravity and geoid in Africa in order to determine the African geoid from the available data sets with the best possible precision. The gravity interpolation has been made at the original omitted data points at the data gap with and without the Moho information. The interpolated gravity has thus been compared to the original omitted data values for both cases to determine the effect of using Moho depths on gravity interpolation. The results are shown and comprehensively discussed.

How to cite: Abd-Elmotaal, H. and Kühtreiber, N.: Effect of Implementing Moho Depths on Gravity Interpolation at Large Data Gaps , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4477, https://doi.org/10.5194/egusphere-egu24-4477, 2024.

The combination of satellite positioning techniques (e.g., GPS) and high-resolution geoid or quasi-geoid models provides an alternative to the expensive and time-consuming spirit leveling for the determination of physical heights. The reliability of the physical heights thus undergoes the same accuracy limitations of the (quasi-) geoid models. However, in many regions, especially developing or newly industrializing countries, there is no reliable regional gravity model, due to the low availability or quality of surface gravity data. This study tackles such challenges in a case study in the northwestern part of South America and provides the first up-to-date high-resolution Colombian quasi-geoid model.

This region is a challenging study area with coastlines on both the Pacific and the Atlantic Ocean and rugged topography with high elevation reaching more than 5,000 m. Available terrestrial and airborne data were collected during the last eight decades, which frequently contain systematic errors and biases and the corresponding metadata is missing. We develop approaches to validate and improve the quality of old gravity datasets. They are then combined with a global gravity model (GGM) and topography models, which play an important role in mountainous areas, within the remove-compute-restore (RCR) procedure. In the offshore area, satellite altimetry-derived gravity data are additionally incorporated, which are obtained from the latest release of the DTU (Technical University of Denmark) gravity anomaly grid, DTU21GRA.

The computed quasi-geoid model is thoroughly validated with independent GPS/leveling data. It delivers an STD of 15.76 cm in comparison to the GPS/leveling data, which is 36% smaller than that obtained from the latest South American quasi-geoid model QGEIOD2021 (24.51 cm). Five recent high-resolution GGMs, namely EGM2008, EIGEN6C4, GECO, SGG-UGM-1, and XGM2019 are also validated using the same GPS/leveling data. They deliver STD values of 28.09 cm, 21.10 cm, 20.39 cm, 20.93 cm, and 17.86 cm, respectively, which are averagely 38% larger than that of our computation.

How to cite: Liu, Q., Schmidt, M., and Sánchez, L.: Methods for geoid determination in regions with challenging data quality and coverage, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5383, https://doi.org/10.5194/egusphere-egu24-5383, 2024.

We have analysed the digital elevation models (DEMs) published in recent years and merged them to create a new elevation grid together with complementary Earth’s relief model for bedrock, surface, bathymetry, ice surface, and ice thickness. Although each model is unique in its own, the merged grid is able to provide seamless elevation information based on a common reference surface globally with high spatial resolution as required for various geoscience applications. We present how the merging procedure was carried out, how the accuracy of the model was evaluated and for which application areas it is intended. Our aim is to disseminate the use of a homogeneous and a consistent elevation model that is particularly suitable for geodetic applications in all parts of the world, including global and regional geoid calculations. The DEMs and associated auxiliary files included in the merged product are: TanDEM-X 90m over all dry land and ice-covered regions, ETOPO2022, GEBCO-2022 and GEBCO-2023 over land and ocean, BedMachineGreenland-v5 over Greenland, BedMachineAntarctica-v3 over Antarctica, GLOBathy (the Global Lakes Bathymetry Dataset) over lakes globally. Masks for ocean, dry land, lakes, islands in the lakes, ponds on the islands, ice-covered land, ice-covered shelves, the area outside Greenland and the ice-covered lake Vostok (Antarctica) were taken into account in the merging. This complete model is anticipated to provide a standardized DEM for various applications in geodesy and geophysics.  Our future plans include high resolution topographic gravity field modelling using this consolidated 30 arc-second digital elevation model and laterally varying global density data.

How to cite: Förste, C., Abrykosov, O., and Ince, E. S.: A consolidated 30 Arc-second Global Digital Elevation Model for Geodesy and Geophysics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8340, https://doi.org/10.5194/egusphere-egu24-8340, 2024.

Many geodetic-related observations nowadays require a precise local geoid height that is as accurate as sub-centimeters. Here, we develop a method to combine global Earth Gravitational Model (EGM), Digital Terrain Model (DTM), as well as highly accurate local gravity and Global Navigation Satellite System (GNSS) observations to achieve this goal. We carried out several observation campaigns to obtain high space resolution and high accuracy gravity and GNSS data. Firstly, we analyze the accuracy of the commonly used methods such as those that use EGM only or use EGM and DTM combined. Then we test our method with a synthetic earth that was developed with EGM, DTM, and local high-resolution topography. Our results should that, the high degree EGM-only geoid has a mean error of tens of centimeters; the geoid from combined EGM and DTM can achieve accuracy of several centimeters; and if we want to have a local geoid with deviations less than sub-centimeter, high accurate observations with space resolution as high as 1"x1" are needed.

How to cite: Huang, J., Zheng, G., Yushan, A., and Qiu, B.: Observation requirements for precise determination of local (quasi)geoid, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14283, https://doi.org/10.5194/egusphere-egu24-14283, 2024.

In the frame of the GeoNetGNSS project, funded by the European Union and National Funds through the Region of Central Macedonia (RCM) in Northern Greece, regional gravimetric and hybrid geoid models have been determined with the main goal being to support a newly established network of Continuously Operating Reference Stations (CORS). The main aim was to assist everyday surveying purposes by delivering accurate orthometric heights based on GNSS/Levelling, i.e., determining orthometric heights without the need to carry out levelling. With that in mind, a regional gravimetric geoid was determined based on historical and newly acquired high-accuracy and density gravity data, employing the Remove-Compute-Restore (RCR) technique and both stochastic and spectral evaluations of Stokes’ integral. Consequently, a hybrid deterministic and stochastic approach was used to model the residuals of the gravimetric geoid solution relative to available GNSS/Levelling geoid heights. The latter refer to 533 geodetic benchmarks in the entire study area, where accurate static GNSS observations and orthometric heights from the Hellenic Military Geographic Service (HMGS) were available. Various parametric models ranging from simple north-south bias and tilt to 2^nd and 3^rd order degree polynomial models were evaluated in terms of the fit residual absolute and relative differences. After the deterministic fit, a collocation approach employing exponential and 2^nd order Gauss-Markov covariance functions was used to model the stochastic residuals. Finally, the hybrid deterministic and stochastic corrector surface, provided as grid corrections for the entire area under study, has been determined to accommodate user needs for orthometric height determination. From the results acquired, absolute differences of the order of 1-2 cm and relative ones at the 3-4 ppm have been achieved after validation against independent GNSS/Levelling observations.

How to cite: Natsiopoulos, D. A., Vergos, G., Mamagiannou, E. G., Tzanou, E. A., Triantafyllou, A. I., Tziavos, I. N., Ramnalis, D., and Polychronos, V.: Refinements of regional gravimetric and hybrid geoid models in support of the GeoNetGNSS CORS network in Northern Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17974, https://doi.org/10.5194/egusphere-egu24-17974, 2024.

Coastal zones exhibit unique altimetry signal characteristics, primarily influenced by the presence of land artifacts. The shape of the altimetry echo serves as a distinctive marker, representing the physical parameters of the surface it originates from. Open ocean reflections for SAR (Synthetic Aperture Radar) mode yield signals with a steep leading edge and a trailing edge modeled by a negative exponential function. In contrast, land areas in coastal zones typically produce specular and quasi-specular waveforms. The presence of specific waveform classes is further influenced by seasonality and changes in land use and patterns such as coastal erosion.

This study aims to classify altimetry waveforms in coastal zones at various global sites and subsequently retrack the identified waveform classes using an optimal retracking strategy. Site selection is based on the availability of in-situ tide gauge data. Waveform classification is achieved using a Long Short-Term Memory (LSTM) auto-encoder, capturing the temporal nature of waveforms and providing an 8-dimensional feature representation. In addition, the LSTM-autoencoder  provides de-noised waveforms, which are used for subsequent retracking processes.

Different waveform shapes necessitate specific retracking strategies. While an Ocean retracker suffices for SAR waveforms over open oceans, it is inadequate for retracking specular, quasi-specular, and multi-peak waveforms. Advanced retracking algorithms such as OCOG, Threshold, ALES, Beta-5, and Beta-9 are employed based on the waveform class.

To validate the proposed strategy, the performance of the altimetry product, sea level anomalies, and retracking outcomes are compared with established coastal altimetry products like XTRACK, in-situ tide gauge data, and popular retracking algorithms like OCOG, Ocean retracker, Threshold, Beta-5 and Beta-9. Sea level anomalies are derived from sensor geophysical data records (SGDR) of altimetry missions and compared with existing coastal altimetry products and in-situ tide gauge records. Evaluation metrics such as Pearson's correlation coefficient and root mean square error assess the agreement in seasonal and yearly trends, as well as the accuracy of measurements.

This comprehensive analysis aims to validate the effectiveness of the proposed coastal waveform post-processing strategy, showcasing its ability to quantify long-term sea level trends and explore regional variations.

How to cite: Kant, S. and Devaraju, B.: Altimetry Waveform Classification and Retracking Strategy for Improved Coastal Altimetry Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-770, https://doi.org/10.5194/egusphere-egu24-770, 2024.

It is known that natural hazards such as volcanic eruptions, earthquakes, and tsunamis can trigger acoustic and gravity waves (AGWs) that could reach the ionosphere and generate electron density disturbances known as Travelling Ionospheric Disturbances (TIDs). These disturbances can be investigated in terms of variations in the ionospheric total electron content (TEC) measurements, collected by continuously operating ground-based Global Navigation Satellite Systems (GNSS) receivers. The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm is a well-known real-time tool for estimating TEC variations. It is based on single-time differences of geometry-free combinations of GNSS carrier-phase measurements.

Artificial Intelligence (AI), particularly in machine learning, offers computational efficiency and data handling, leading to its exploration in ionospheric studies. In this context, the abundance of data allows the exploration of a VARION-based machine learning classification approach to detect TEC perturbation. For this purpose, we used the VARION-TEC variations from the 2015 Illapel earthquake and tsunami, leveraging the distinct ionospheric response triggered by the event.

We employed machine learning algorithms, specifically Random Forest (RF) and XGBoost (XGB), using the VARION-core observations (i.e., dsTEC/dt) as input features. We formulated a binary classification problem using supervised machine learning algorithms and manually labelled the time frames of TEC perturbations as the target variable. We considered two elevation cut-off time series, namely 15° and 25°, to which we applied the classifier. XGBoost with a 15° elevation cut-off dsTEC/dt time series reached the best performance, achieving an F1 score of 0.77, recall of 0.74, and precision of 0.80 on the test data. More in detail, regarding the testing samples, the model accurately classified 183 out of 247 (74.09%) samples of sTEC variations related to the earthquake and tsunami (True Positives, TP). Moreover, 2975 out of 3021 (98.49%) testing samples were correctly classified as containing no sTEC variations caused by an earthquake (True Negatives, TN). However, 64 out of 247 samples (25.91%) were erroneously classified as not containing sTEC variations related to the event (False Negatives, FN), while 46 out of 3021 (1.51%) were wrongly classified as containing sTEC variations related to the earthquake and tsunami (False Positives, FP).

This model showed a 75-second average deviation in predicting perturbation time frames for testing links, equivalent to 5 steps in the 15-second time series intervals. This highlights the algorithm's potential for early detection of ionospheric perturbations from earthquakes and tsunamis, aiding in early warning purposes.

Finally, the model efficiently detects TIDs within 2-3 minutes, showing an impressive computational efficiency, crucial for effective early warning systems. It relies only on the VARION-generated real-time TEC time series (dsTEC/dt), enabling its application in an operational real-time setting using real-time GNSS data.

In conclusion, this work demonstrates high-probability TEC signature detection by machine learning for earthquakes and tsunamis, which can be used to enhance tsunami early warning systems.

How to cite: Fuso, F., Ravanelli, M., Crocetti, L., and Soja, B.: Using Machine Learning for identifying TEC signatures related to earthquakes and tsunamis: the 2015 Illapel event case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1853, https://doi.org/10.5194/egusphere-egu24-1853, 2024.

    Evaluation of uncertainties of geodetic parameter estimates 
is the problem that is not yet solved in a satisfactory way. 
A direct evaluation of the uncertainties derived from a least 
square solution is labeled "formal" and is usually biased, 
sometimes up to an order of magnitude. Customary, the use of 
formal errors for scientific analysis is discouraged. We claim 
that the root of the problem is neglecting off-diagonal elements 
in the variance-covariance matrix of the noise in the data. 
A careful reconstruction of the full variance-covariance matrix, 
including the off-diagonal terms greatly improves realism of 
uncertainty estimates derived from least squares. We processed 
the dataset of VLBI group delays and built the a priori 
variance-covariance of the atmosphere-driven noise based on 
analysis of the output of NASA high-resolution numerical weather 
models. We found that the uncertainties of parameter estimates 
derived from this least square solution that uses such 
variance-covariance matrices become much closer to realistic 
errors. We consider approaches for for implementation of this 
method in routine data analysis of space geodesy data.

How to cite: Petrov, L. and Hanaba, N.: From formal errors towards realistic uncertainties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2619, https://doi.org/10.5194/egusphere-egu24-2619, 2024.

Accurate ionospheric models are essential for single-frequency high-precision Global Navigation Satellite Systems (GNSS) applications. Global ionospheric maps (GIMs), which depicts the global distribution of vertical total electron content (VTEC), are a widely used ionospheric product provided by the International GNSS Service (IGS). To meet the increasing need for real-time applications, the IGS real-time service (RTS) has been established and offers real-time (RT) GIMs that can be used for real or near-real time applications. However, the accuracy of present RT GIMs is still significantly lower compared with the final GIMs. IGS RT GIMs show an RMSE of 3.5-5.5 TECU compared to the IGS final GIMs. In this study, we focus on enhancing the accuracy of RT GIMs through machine learning (ML) approaches, specifically a classical Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN). The objective is to bridge the gap between the RT GIMs and the final IGS GIMs. This is achieved by using RT GIMs as input and final GIMs as target. The ML approach is applied to the IGS combined RT GIMs and Universitat Politècnica de Catalunya (UPC) RT GIMs. The performance of the improved RT GIMs is evaluated in comparison to the combined IGS final GIM.

We consider over 11'000 pairs of RT GIMs and final GIMs. Over a comprehensive test period spanning 3.5 months, the proposed approach shows promising results with an enhancement of more than 30% in accuracy of RT GIMs. Especially for regions with high VTEC values, which are most critical, the results show a significant improvement. The results demonstrate the model’s great potential in generating more accurate and refined real-time maps.

The integration of ML techniques proves to be a promising avenue for refining and augmenting the precision of real-time ionospheric maps, thereby addressing critical needs in the realm of space weather monitoring and single-frequency applications.

How to cite: Iten, M., Mao, S., and Soja, B.: Enhanced Real-time Global Ionospheric Maps using Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3117, https://doi.org/10.5194/egusphere-egu24-3117, 2024.

In PPP, the stochastic model of observation determines the availability and reliability of positioning accuracy, and the observations are usually weighted according to the angle of the GNSS observation, and the smaller the angle of the observation, the more the influence of atmospheric noise and multipath on the observation data increases, and the accuracy of the observations decreases. Based on this, we proposed multi-indicator comprehensive assessment based on grey correlation analysis for observation stochastic modeling of PPP. The position dilution of precision (PDOP), carrier-to-noise density ratio (C/N0) and pseudorange multipath indicators are selected to construct a multi-indicator matrix. Firstly, the indicators are normalized, and then the entropy weight of each assessment indicator is calculated to determine the indicator weight. Meanwhile, after selecting the optimal indicator set, the matrix is constructed to find the grey correlation coefficient and finally the grey correlation degree. According to the above method, the comprehensive assessment results of the quality of satellite observation data for each epoch can be obtained, and the PPP weight array can be established. One-week observations from 243 MGEX stations are selected to conduct GPS-only, Galileo-only and BDS-3-only kinematic PPP, the stochastic model using the highest-elevation and the proposed method is applied, respectively. The results show that, compared with the traditional method, the positioning accuracies and convergence time all can be improved using the proposed method. The positioning accuracies of GPS can be improved by about 4.23%, 8.66%, 5.04% and 5.46% in the east(E), north(N), up(U) and three-dimensional(3D) directions, respectively; 15.96%, 14.25%, 14.72% and 15.01% for Galileo; and 13.53%, 8.42%, 11.65% and 11.40% for BDS-3. The average improvements of convergence time in the east, north and up directions are 5.53%, 7.80% and 5.01% for GPS, BDS-3 and Galileo, respectively.

How to cite: Huang, G., Li, M., and Wang, L.: Multi-Indicator Comprehensive Assessment for Observation Stochastic Model of PPP, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4609, https://doi.org/10.5194/egusphere-egu24-4609, 2024.

The precise point positioning-real-time kinematic (PPP-RTK) method achieves fast convergence in global navigation satellite system (GNSS) positioning and navigation. Correcting slant ionospheric delays is crucial for this purpose. The conventional way of obtaining slant ionospheric corrections at the user end involves generating an ionospheric map using a first-order polynomial function or interpolating using methods such as IDW and Kriging. However, with these approaches is challenging to obtain precise and stable ionospheric corrections especially during ionospheric disturbances, potentially degrading the positioning solution even with augmentation. Fortunately, machine learning has the capability to capture complex and non-linear characteristics of diverse data, offering a potential solution to this issue.

In this study, we aim to improve the accuracy of slant ionospheric delay models using machine learning and evaluate them in PPP-RTK. Initially, we extract highly precise slant ionospheric delays from carrier-phase measurements after ambiguity resolution for two regional GNSS networks in Switzerland and the South of China. Then, we employ the Gaussian Process Regressor to interpolate epoch-specific and satellite-specific slant ionospheric delays, utilizing latitude and longitude as features. Two different approaches are tested: the direct interpolation from reference stations and the indirect interpolation from a gridded map. Our results indicate that the accuracy of interpolated ionospheric delays using machine learning is higher than with conventional methods, including IDW and Kriging. Finally, we evaluate PPP-RTK positioning results with ionospheric corrections from the different interpolation methods, revealing that the machine learning method exhibits superiority in both positioning accuracy and convergence time over conventional methods.

How to cite: Lyu, S., Xiang, Y., Yu, W., and Soja, B.: Machine learning-based regional slant ionospheric delay model and its application for PPP-RTK, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5743, https://doi.org/10.5194/egusphere-egu24-5743, 2024.

Automatic detection and characterization of spatial and temporal features of surface deformation signals associated with anthropogenic activities is a challenging task, with important implications for the evaluation of multi-hazard related to human activities (e.g. earthquakes, subsidence, sea-level rise and flooding), particularly in coastal areas. In this work, we use synthetic Global Navigation Satellite System (GNSS) displacement time-series and apply Deep Learning algorithms (i.e. Convolutional Neural Network (CNN) and Autoencoder) in extracting the time and space features of ground deformation due to natural and anthropogenic processes. We focus on improving three fundamental aspects such as the spatial coverage, the temporal coverage and the accuracy of measurement that come from GNSS technique. The study area is Northern Italy, and particularly the North Adriatic coasts, where gas and oil production sites as well as gas storage sites are present. If in production sites hydrocarbon is constantly extracted during the year, in storage sites the gas/oil is usually injected from April to October and extracted between November and March. Our goals are to understand the effect of hydrocarbon production and extraction/injection process on surface deformation as precisely measured by the dense network of continuous GNSS stations operating in the study area and the ability of CNN-Autoencoder to characterize ground displacements caused by anthropogenic processes. Aims of this work are to identify temporal and spatial patterns in ground deformation time series caused by oil and gas extraction/or gas storage (i.e. extraction and injection); and estimate reservoir parameters (i.e. volumes, depths and extensions). We realize the training dataset by setting up 202 GNSS stations, randomly locating gas/oil reservoirs, which are described by a simple Mogi model, characterized by different depths and temporal evolution of volume changes. The Mogi model, as an approximate spherical shape of a reservoir, displays the ratio of horizontal displacement to vertical displacement due to volume change (i.e. inflating or deflating) and pressure varying with time. The temporal evolution of the volumes of the Mogi sources is simulated by using different parameters associated with several functions namely seasonal, exponential, multi-linear and bell shape. Weighted Principal Component Analysis (WPCA) is used to deal with missing data, which is a common feature in GNSS time series, under an assumption that the weights of missing data are zero. Furthermore, since the CNN-Autoencoder works by analyzing images, the synthetic GNSS time series are interpolated by leveraging the Kriging Interpolation method, which is a Gaussian Process Regression, to obtain the ground displacement in 2D physical space. After calibrating the CNN-Autoencoder model with the synthetic GNSS time series, the model is applied to real data. The code is written in Python and runs on a High-performance computing (HPC) cluster with Graphic Process Unit (GPU) at National Institute of Geophysics and Volcanology (INGV) in Bologna, Italy. 

How to cite: Vu, D. T., Gualandi, A., Pintori, F., Serpelloni, E., and Pezzo, G.: Deep Learning spatio-temporal analysis of anthropogenic ground deformation recorded by GNSS time series in the North Adriatic coasts of Italy , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9203, https://doi.org/10.5194/egusphere-egu24-9203, 2024.

In recent years, geodesy based on spaceborne microwave remote sensing has gained significant advances. However, whether the observations from the Global Navigation Satellite System (GNSS) or Interferometric Synthetic Aperture Radar (InSAR), the results are inevitably influenced by atmospheric tropospheric delay. Although the tropospheric zenith total delay (ZTD) can be estimated through the gridded meteorological data products and empirical models provided by the ERA5 reanalysis product, its accuracy is still insufficient to meet the needs of modern geodesy. To overcome this challenge, we propose leveraging machine learning techniques to learn local spatio-temporal patterns of tropospheric delay for inferring total zenith delay (ZTD) and zenith wet delay (ZWD) at any location within the learning area.

Our findings indicate that artificial neural networks can establish a robust mapping between ZTD estimated by empirical models and GNSS-measured ZTD. Then employing the ensemble learning strategy and the time series dynamics model, the ZTD at any location within the sample area can be inferred. To evaluate our approach, we conducted tests during the active water vapor season in the Tübingen region of Baden-Württemberg, Germany, from June 25 to July 9, 2022. In comparative experiments with the root mean square error (RMSE) of Zenith Total Delay (ZTD) derived from ERA5, our proposed method yielded a significant reduction in RMSE, decreasing it from 16.4292mm to 7.2108mm. This reflects a remarkable accuracy improvement of 56.11%.

The proposed approach holds promise for enhancing the precision of GNSS positioning, InSAR earth observation, and generating more dependable water vapor products.

How to cite: Wang, D., Wang, L., and Kutterer, H.: Machine learning for atmospheric delay correction in geodesy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9543, https://doi.org/10.5194/egusphere-egu24-9543, 2024.

Borehole strainmeters are remarkably precise instruments.  They are often installed to record deformation produced by earthquakes, postseismic slip, and slow earthquakes.  Strainmeters can record such tectonic deformation on timescales of minutes to months with a precision of 0.1 to 1 nanostrain; they record sub-Angstrom changes in borehole width.  

However, the instruments’ high precision also extends to non-tectonic signals.  The borehole width often changes by more than 1 Angstrom when it rains, when atmospheric pressure increases, and when snow loads the ground.  Thus if we want to take full advantage of the instruments and investigate tectonic deformation with high precision, we need to understand and remove the deformation produced by non-tectonic signals like water loading.

So in this study, I present several neural network-based models of hydrologic deformation.  Neural networks are ideal for this modelling as they can accommodate the nonlinearity of the system; 1 cm of rain will cause different deformation if it falls on saturated, winter soil than if it falls on dry, summer soil.  Further, neural networks can take advantage of the abundance of local weather data, including at short timescales.  In my initial modelling, I attempt to reproduce and predict strain as a function of current and past precipitation, atmospheric pressure, wind speed, and temperature.  For simplicity and ease of use, all these parameters are taken from the ECMWF reanalysis models.

I design two neural networks to model the observed strain, using physical intuition to limit the number of free parameters and thus improve the training.  The first network is simple; it creates 10 linear combinations of past rainfall, with exclusively positive weights, and then combines those linear combinations to predict the strain.  The second network also creates 10 linear combinations of past rainfall with positive weights.  But it multiplies those linear combinations of rain by nonlinear functions that could represent the state of the Earth and aquifers.  These nonlinear functions include dependencies on past rainfall, atmospheric pressure, wind speed, and temperature.

These networks train quickly, within a few minutes, and they do a reasonable job of producing the first-order features of the strain.  Both models accommodate more than 50% of the hydrologic signal on timescales of days.  Such modelling may or may not be interesting to hydrologists, but for those interested in tectonic deformation, reproducing and removing 50% of the hydrologic signal means removing 50% of the noise.

It is likely that a better developed and regularised model could remove much more of the noise, and we are continuing to add constraints, initial weights, and training schemes to improve the hydrologic modelling.

How to cite: Hawthorne, J.: Neural network-based hydrology corrections for borehole strainmeters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10154, https://doi.org/10.5194/egusphere-egu24-10154, 2024.

Displacement time series from Global Navigation Satellite System (GNSS) at daily rates are used commonly to investigate and understand the processes controlling Earth's surface deformation originating from tectonic processes such as postseismic slip, slow slip events and viscoelastic relaxation, but also non-tectonic processes such as reflectometry, atmospheric sensing and remote sensing. For each individual research field, different parts of the total recorded GNSS displacement time series are of intrest. A major difficulty is the modeling and isolation of non-tectonic seasonal signals that are established to be related with non-tidal surface loading.

In the past, many methods were developed with some success based on Kalman filters, matrix factorization and various approaches using curve fitting to separate the tectonic and non-tectonic contributions. However, these methods still have some difficulties in  isolating the seasonal loading signals especially in the presence of interannual variations in the seasonal loading pattern and steps in the time series.

We present here a deep learning model trained on a globally distributed, continuous 8-10 years long dataset of ~8000 stations PPP-GNSS displacement time series from NGL to estimate the seasonal loading signals using a global non-tidal surface loading model developed at ESM-GFZ. We compare our model to other statistical methods for isolation of the seasonal with the established method of subtraction of the non-tidal surface loading signals (hydrological loading, and non-tidal atmospheric and oceanic loading) as our baseline. We also present the evaluation of our model and its capabilities in reducing the seasonal loading signal as well as parts of the high-frequency scattering in the original GNSS time series.

How to cite: Çökerim, K., Bedford, J., and Dobslaw, H.: A Globally Trained Deep Learning Model for Estimation of Seasonal Residual Signals in GNSS displacement time series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10290, https://doi.org/10.5194/egusphere-egu24-10290, 2024.

Geological hazards caused by both natural forces and human-induced disturbances, such as land subsidence, earthquakes, tectonic motion, mining activities, coastal erosion, volcanic activities, and permafrost alterations, cause great adverse effects to earth’s surface. The preservation of a comprehensive record detailing past, present, and future surface movements is imperative for effective disaster risk mitigation and property protection. Interferometric Synthetic Aperture Radar (InSAR) is widely recognized as a highly effective and extensively employed geodetic technique for comprehending the spatiotemporal evolution of historical ground surface deformation. However, it only reveals the past deformation evolution process and the deformation update is slowly considering the long revisit cycle of satellites. Deformation evolution in the future is also crucial for preventing and mitigating geological hazards. Unlike traditional mathematical-statistical models and physical models, machine learning methods provide a new perspective and possibility to efficiently and automatically mine the time series information over a large-scale area. In the context of InSAR time series prediction over large areas, the previous researches do not consider the spatiotemporal heterogeneity caused by various factors over a large-scale area and mainly focus on one typical deformation point.

Therefore, in this study, we present a framework designed to predict large-scale spatiotemporal InSAR time series by integrating independent component analysis (ICA) and a Long Short-Term Memory (LSTM) machine learning model. This framework is developed with a specific focus on addressing spatiotemporal heterogeneity within the dataset. The utilization of the ICA method is employed to identify and capture the displacement signals of interest within the InSAR data, enabling the characterization of independent time series signals associated with various natural or anthropogenic processes. Additionally, a K-means clustering approach is incorporated to partition the study area into spatiotemporal homogeneity subregions across a large-scale region, aiming to mitigate potential decreases in model accuracy caused by data heterogeneity. Subsequently, LSTM models are constructed for each cluster, and optimal parameters are determined. The proposed framework is rigorously tested using simulated datasets and validated against two real-world cases—land subsidence in the Willcox Basin and post-seismic deformation following the Sarpol-e Zahab earthquake. Comparative analysis demonstrates that the proposed model surpasses the original LSTM, resulting in a 34% and 17% improvement in average prediction accuracy, respectively. The spatial prediction results in 60 days over the two cases are mapped with high accuracy.

This study introduces an integrated framework that seamlessly integrates InSAR data processing with machine learning techniques such as LSTM to enhance our ability to predict deformation over large-scale geographical areas. The adaptability of the proposed model has made it an alternative to numerical or empirical models, especially when detailed on-site data is scarce or challenging to obtain. While our immediate applications have focused on scenarios on land subsidence and post-seismic deformation, the broader implications of our methodology are evident. We anticipate the proposed framework will be expanded to various application domains, including mining, infrastructure stability, and other situations involving sustained motions. The proposed framework will ultimately contribute to more informed decision-making and risk assessment in complex dynamic systems.

How to cite: Peng, M., Motagh, M., Lu, Z., Xia, Z., Guo, Z., Zhao, C., and Quan, Y.: Improving ground deformation prediction in satellite InSAR using ICA-assisted RNN model , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10467, https://doi.org/10.5194/egusphere-egu24-10467, 2024.

Accurate prediction of Earth orientation parameters (EOPs) is critical for astro-geodynamics, high-precision space navigation, and positioning, and deep space exploration. However, the current models' prediction accuracy for EOPs is significantly lower than that of geodetic technical solutions, which can adversely affect certain high-precision real-time users. In this study, we introduce a simultaneous prediction approach for Polar Motion (PM) and Celestial Pole Offsets (CPO) employing deep neural networks, aiming to deliver precise predictions for both parameters.
The methodology comprises three components, with the first being feature interaction and selection. The process of feature selection within the context of deep learning differs from traditional methods for machine learning, and may not be directly applicable to theme since they are designed to automatically learn relevant features. Consequently, we propose in this step a solution based on feature engineering to select the best set of variables that can keep the model as simple as possible but with enough precision and accuracy using recursive feature elimination and the SHAP value algorithm, aiming to investigate the influence of FCN (Free Core Nutation) with its amplitude and phase on the CPO forecasting. This investigation is crucial since FCN is the main source of variance of the CPO series. Considering the role represented by the effective Angular Momentum functions (EAM), and their direct influence on the Earth's rotation, it is logical to assess numerically the impact of EAM on the Polar motion and FCN excitations. SHAP value aids in comprehending how each feature contributes to final predictions, highlighting the significance of each feature relative to others,  and revealing the model's dependency on feature interactions.
During the second phase, we formulate two deep-learning methods for each parameter. The first Neural Network incorporates all features, while the second focuses on the subset of features identified in the initial step. This stage primarily involves exploring feature and hyperparameter tuning to enhance model performance. The SHAP value algorithm is also used in this stage for interpretation. 
In the final phase, we construct a multi-task deep learning model designed to simultaneously predict ( CPO ) and ( PM ).  This model is built using the optimal set of features and hyperparameters identified in the preceding steps. To validate the methodology, we employ the most recent version of the time series from the International Earth Rotation and Reference Systems Service (IERS), namely IERS 20 C04 and EAM provided by the German Research Center for Geosciences (GFZ). We focus on a forecasting horizon of 90 days, the practical forecasting horizon needed in space-geodetic applications.
In the end, we conclude that the developed model is proficient in simultaneously predicting ( CPO ) and ( PM ). The incorporation of ( EAM ), sheds light on its role in CPO excitations and Polar Motion predictions.

How to cite: Guessoum, S., Belda, S., Ferrándiz, J. M., Begga, A., Karbon, M., Schuh, H., Modiri, S., and Heinkelmann, R.:  Feature Selection and Deep Learning for Simultaneous Forecasting of Celestial Pole Offset (CPO) and Polar Motion (PM), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10575, https://doi.org/10.5194/egusphere-egu24-10575, 2024.

Global Navigation Satellite Systems (GNSS) are best known for their accurate positioning, navigation and timing capabilities. In total, over 20.000 permanent high-grade GNSS stations are available worldwide, the positions of which are monitored with millimeter accuracy. Thanks to the high accuracy and the fact that these stations are mounted on the ground, subtle movements due to hydrological changes and crustal deformation can be observed. Thus, the GNSS observations contain valuable geophysical information. Although many geodetic applications require these movements to be properly understood and potentially corrected, this is not trivial due to the complexity of the interactions within the Earth’s system. Therefore, there is a severe lack of available models explaining residual GNSS station movements beyond conventionally modeled effects. On the opposite, if these movements are properly understood, GNSS observations might contribute to the correct interpretation of emerging environmental changes.

This study exploits the wealth of satellite-derived Earth observation (EO) data to derive suitable models to explain GNSS station movements. We combine GNSS station coordinate time series and EO variables with the help of machine learning techniques to benefit from various types of information. While the target vector consists of concatenated GNSS station coordinate time series over Europe, EO variables such as precipitation, soil water, snow water equivalent, and land cover data are used as input features. Different machine learning models, including Random Forest, XGBoost, and Multilayer Perceptron, are investigated and compared. Additionally, a sensitivity analysis is performed to determine the individual impact of EO variables to quantify what drives GNSS movements, which in turn, might allow monitoring the corresponding Earth system processes based on GNSS observations.

How to cite: Crocetti, L., Schneider, R., and Soja, B.: Explaining GNSS station movements based on Earth observation data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10706, https://doi.org/10.5194/egusphere-egu24-10706, 2024.

Abstract:

Machine Learning (ML) is emerging as a powerful tool for data analysis. Anomaly detection based on classical approaches is sometimes limited in processing speed on big data, especially for massive datasets. Meanwhile, quantum algorithms have been shown to have the potential for optimization, scenario simulation, and artificial intelligence. Thus, this study combines quantum algorithms and ML to improve the binary classification performance of ML models for better sensitivity of surface deformation detection. We experimented with GNSS-InSAR combination data to identify significant deformation regions in Northern Germany. We classify the movement characteristics based on four main features: vertical movement velocities, root mean square errors, standard deviations, and outliers in the GNSS-InSAR time series. Our primary results reveal that the classification accuracy based on Quantum Machine Learning (QML) is outstanding compared to the pure ML technique. Specifically, on the same sample dataset, the classification performance of the neural network based on pure ML is only around 50 to 70%, while that of the QML technique can reach ~90%. The significant deformation regions are concentrated in the river basins of Elbe, Weser, Ems, and Rhine, where the average surface subsidence speed varies around -4.5 mm/yr. Also, we suggest dividing the surface movement features in Northern Germany into five classes to reduce the effect of the data quality variety and algorithm uncertainty. Our findings will advocate the development of quantum computing applications as well as promote the potential of the QML for deformation analyses. 

Keywords:

Quantum Machine Learning, Binary Classification, GNSS-InSAR Data, Deformation Detection.

How to cite: Le, N., Männel, B., Motagh, M., Brack, A., and Schuh, H.: Quantum Machine Learning for Deformation Detection: Application for InSAR Point Clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11029, https://doi.org/10.5194/egusphere-egu24-11029, 2024.

The reception of non-line-of-sight (NLOS) signals is a prevalent issue for Global Navigation Satellite System (GNSS) applications in urban environments. Such signals can significantly degrade the positioning and navigation accuracy for pedestrians and vehicles. While various methods, such as dual-polarization antennas and 3D building models, have been proposed to identify NLOS signals, they often require additional equipment or impose computational burdens, which limits their practicality. In this study, we introduce a machine learning (ML)-based classifier designed to detect NLOS signals based solely on quality indicators extracted from raw GNSS observations. We examined several input features, including carrier-to-noise density and elevation, and analyzed their relative importance. The effectiveness of our approach was validated using multi-GNSS data collected statically in the city of Hannover. To establish ground truth (i.e., a target) for training and testing the model, we used ray tracing in combination with a 3D building model of Hannover. The developed ML-based classifier achieved an accuracy of approximately 90% for NLOS signal classification. Furthermore, a vehicle-borne data set was used to test the utility of the ML-based signal classifier for kinematic positioning. The performance of the ML-aided positioning solution was compared against a solution without NLOS classification (raw solution) and with the ray-tracing-based classification results (reference solution). It was found that the ML-based solution demonstrated positioning precisions of 0.47 m, 0.55 m and 1.02 in the east, north and up components, respectively. This represents improvements of 64.6%, 33.4% and 36.6% over the raw solution. Additionally, we examined the performance of the ML-based classifier across various urban environments along the vehicle trajectory to gain deeper insights.

How to cite: Pan, Y., Icking, L., Ruwisch, F., Schön, S., and Soja, B.: Non-line-of-sight GNSS Signal Classification for Urban Navigation Using Machine Learning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11427, https://doi.org/10.5194/egusphere-egu24-11427, 2024.

One big challenge in the analysis and interpretation of geodetic data is the separation of the individual signal and noise components contained in the data. Specifically, the global, temporal gravity data obtained by the GRACE and GRACE Follow-On satellite missions contain spatial-temporal gravity signals caused by all kinds of mass variations in the Earth’s system. While only the sum of all signals can be measured, for geophysical interpretation, an extraction of individual signal contributions is necessary.

Therefore, our aim is to develop an algorithm solving the signal separation task in global, temporal gravity data. Since the individual signal components are characterized by specific patterns in space and time, the algorithm to be found needs to be able to extract patterns in the 3-dimensional latitude-longitude-time space.

We propose to exploit the pattern recognition abilities of deep neural networks for solving the signal separation task. Our method uses a multi-channel U-Net architecture which is able to translate the sum of various signals as single-channel input to the individual signal components as multi-channel output. The loss function is a weighted sum of the L2 losses of the individual signals.

We perform a supervised training using synthetic data derived from the updated Earth System Model of ESA. The latter consists of separate datasets for temporal gravity variations caused by mass redistribution processes in the atmosphere, the oceans, the continental hydrosphere, the cryosphere and the solid Earth.

In our study, we use different parts of this dataset to form training and test datasets. In this fully-synthetic framework, the ground truth of the individual signal components is also known in the testing stage, allowing a direct computation of the separation errors of the trained separation model.

In our contribution, we present results on optimizing our algorithm by tuning various hyperparameters of the neural network. Moreover, we demonstrate the impact of the number of superimposed signals and the definition of the loss function on the signal separation performance of our algorithm.

How to cite: Heller-Kaikov, B., Pail, R., and Werner, M.: Signal separation in global, temporal gravity data using a multi-channel U-Net, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12487, https://doi.org/10.5194/egusphere-egu24-12487, 2024.

High-precision ionospheric prediction is essential for real-time applications of the Global Navigation Satellite System (GNSS), especially for single-frequency receivers. Various machine learning (ML) algorithms have been utilized for ionospheric forecasting and shown great potential. However, previous studies have primarily relied on IGS global ionospheric maps (GIMs) as training data to develop models for global vertical total electron content (VTEC) forecasting. The forecasting accuracy is thereby limited by the input IGS GIMs due to their low spatio-temporal resolution.

Our previous work proposed a neural network-based (NN-based) global ionospheric model. GIMs generated with this approach showcased enhanced accuracy compared with conventional IGS GIMs as we can finely resolve VTEC irregularities. In this study, we benefit from these ML-based GIMs by employing the transfer learning principle to improve the quality of GIM forecasts. The ML-based model for 1-day ahead global VTEC forecasting is first trained based on a series of IGS GIMs from 2004 to 2022. Then, it is fine-tuned using the recent NN-based GIMs from 2020 to 2022. In this context, the model can gain good generalizability from the large dataset of IGS GIMs while having comparable accuracy with NN-based GIMs. Different machine learning approaches, including convolution long short-term memory (ConvLSTM) network and transformer, are implemented and compared. To validate their performance, we perform hindcast studies to compare the 1-day ahead forecasts of our model with satellite altimetry VTEC and conducted single-frequency precise point positioning tests based on the forecast maps.

How to cite: Mao, S., Gou, J., and Soja, B.: An Ionospheric Forecasting Model Based on Transfer Learning Using High-Resolution Global Ionospheric Maps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12556, https://doi.org/10.5194/egusphere-egu24-12556, 2024.

Spaceborne GNSS Reflectometry (GNSS-R) is a novel remote sensing technique providing accumulating data volume with global coverage and enhanced temporal resolution. The reflected pre-existing L-Band signal of opportunity transmitted by the Global Navigation Satellite System contains information about the reflection surface properties which can be quantified and converted into data products for further studies. To retrieve such information, Artificial intelligence (AI) models are implemented to estimate geophysical parameters based on the GNSS-R observations. With more and more complex deep learning models being proposed and more and more input features being considered, understanding the decision-making process of the models and the contributions of the input features becomes as important as enhancing the model output accuracy.

This study explores the potential of the Explainable AI (XAI) to decode complex deep learning models for ocean surface wind speed estimation trained by the Cyclone GNSS (CYGNSS) observations. The input feature importance is evaluated by applying the SHAP (SHapley Additive exPlanations) Gradient Explainer to the model on an unseen dataset. By analyzing the SHAP value of each input feature, we find that in addition to the two known parameters that are used in the operational wind speed retrieval algorithm, other scientific and technical ancillary parameters, such as the orientation of the satellite and the signal power information are also useful for the model.

We seek to offer a better understanding of the deep learning models for estimating ocean wind speed using GNSS-R data and explore the potential features for more accurate retrieval. In addition to building an efficient model with effective inputs, XAI also helps us to discover the important factors found by models which can enhance the physical understanding of the GNSS-R mechanism.

How to cite: Xiao, T., Asgarimehr, M., Arnold, C., Zhao, D., Mou, L., and Wickert, J.: Explainable AI for GNSS Reflectometry: Investigating Feature Importance for Ocean Wind Speed Estimation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12715, https://doi.org/10.5194/egusphere-egu24-12715, 2024.

Snow is a key variable of the global climate system and the hydrological cycle, as well as one of the most critical sources of freshwater. Therefore, measurements of snow-related parameters such as seasonal snow height (SSH) or snow-water-equivalent (SWE) are of great importance for science, economy and society. Traditionally, these parameters are either measured manually or with automated ground-based sensors, which are accurate, but expensive and suffer from low temporal and spatial resolution.

A new alternative for such systems is the use of GNSS observations, by application of the GNSS interferometric reflectometry (GNSS-IR) method. The technique enables users to infer information about soil moisture, snow depth, or vegetation water content. Signal-to-Noise Ratio (SNR) observations collected by GNSS receivers are sensitive to the interference between the direct signal and the reflected signal (often referred to as “multipath”). The interference pattern changes with the elevation angle of the satellite, the signal wavelength, and the height of the GNSS antenna above the reflecting surface. By comparing this reflector heights estimated for snow surfaces with those from bare soil conditions, snow height can be determined.

The estimation of reflector heights, and respectively SSH, is typically carried out using Lomb-Scargle Periodogram (LSP) spectrum analysis. This study investigates the potential of machine learning methods for this task, using similar input parameters as the standard GNSS-IR retrieval. Results from different supervised algorithms such as Random Forest (RF) or Gradient Boosting (GB) are shown for different GNSS sites and experimental setups. First investigations indicate that snow heights can be successfully obtained with machine learning, with results less noisy than with classical approaches.

How to cite: Aichinger-Rosenberger, M. and Soja, B.: Exploring the performance of machine learning models for the GNSS-IR retrieval of seasonal snow height, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14724, https://doi.org/10.5194/egusphere-egu24-14724, 2024.

Time series of interferometric SAR (InSAR) images offer the potential to detect and monitor surface displacements with high spatial and temporal resolution, even for small and slow deformation processes. Yet, due to the nature of InSAR, the interferometric signal can contain a multitude of contributions. Different displacement source mechanisms could superpose each other, signals that are residuals of atmospheric and topographic effects could not be completely removed during processing of the time series or non-coherent noise could exist. Therefore, the criteria for the selection of temporally stable pixels are often rather strict, leading to significant reduction of the spatial resolution density.

However, to understand the underlying processes of a deformation field, it is important to extract the displacement signals from the data at the best resolution possible and differentiate signals from different source mechanisms. Furthermore, being able to describe the displacement field as superposition of several simple mechanisms is a possible answer to the general question how the information content from tens of thousands of points each coming with a time series over hundreds of acquisitions can be extracted and comprehended.

We address these issues, by determining the dominant displacement signals of different sources in a subset of reliable pixels of InSAR time series datasets with data driven component analysis methods. Subsequently we use models of these signals to identify their displacement patterns in previously not regarded pixels. We utilize the statistical principal component analysis for removing uncorrelated signal contributions and compare different blind source separation methods, such as independent component analysis and independent vector analysis for differentiating between displacements of different origin.

We apply our method to a dataset of multiple orbits of Sentinel-1 InSAR time series from 2015 to 2022 above the gas storage cavern field Epe in NRW, Germany.  Epe displays a complex surface displacement field, consisting of trends caused by cavern convergence, cyclic gas pressure dependent contributions, as well as ground water dependent seasonal displacements. With our approach, we can successfully distinguish the signals of the different source mechanisms and obtain a dense spatial sampling of these signals. Our results show good agreement with geodetic measurements from GNSS and levelling and show a strong correlation to cavern filling levels and groundwater levels, suggesting causal relations.

How to cite: Seidel, A., Even, M., Westerhaus, M., and Kutterer, H.: Signal decomposition of multi-source displacement fields with component analysis methods, applied to InSAR time series of the Epe gas storage cavern field (Germany), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16740, https://doi.org/10.5194/egusphere-egu24-16740, 2024.

In geological characterization, the traditional methods that rely on the covariance matrix for continuous variable estimation often either neglect or oversimplify the challenge posed by subsurface non-stationarity. This study presents an innovative methodology using ancillary data such as geological insights and geophysical exploration to address this challenge directly, with the goal of accurately delineating the spatial distribution of subsurface petrophysical properties, especially, in large geological fields where non-stationarity is prevalent. This methodology is based on the geodesic distance on an embedded manifold and is complemented by the level-set curve as a key tool for relating the observed geological structures to intrinsic geological non-stationarity. During validation, parameters ρ and β were revealed to be the critical parameters that influenced the strength and dependence of the estimated spatial variables on secondary data, respectively. Comparative evaluations showed that our approach performed better than a traditional method (i.e., kriging), particularly, in accurately representing the complex and realistic subsurface structures. The proposed method offers improved accuracy, which is essential for high-stakes applications such as contaminant remediation and underground repository design. This study focused primarily on two-dimensional models. There is a need for three-dimensional advancements and evaluations across diverse geological structures. Overall, this research presents novel strategies for estimating non-stationary geologic media, setting the stage for improved exploration of subsurface characterization in the future.

How to cite: Park, E.: Manifold Embedding Based on Geodesic Distance for Non-stationary Subsurface Characterization Using Secondary Information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16940, https://doi.org/10.5194/egusphere-egu24-16940, 2024.

In the analysis of sub-annual climatological or geodetic time series such as tide gauges, precipitable water vapor, or GNSS vertical displacements time series but also temperatures or gases concentrations, seasonal cycles are often found to have a time-varying amplitude and phase.

These time series are usually modelled with a deterministic approach that includes trend, annual, and semi-annual periodic components having constant amplitude and phase-lag. This approach can potentially lead to inadequate interpretations, such as an overestimation of Global Navigation Satellite System (GNSS) station velocity, up to masking important geophysical phenomena that are related to the amplitude variability and are important for deriving trustworthy interpretation for climate change assessment.

We address that challenge by proposing a novel linear additive model called the fractional Sinusoidal Waveform process (fSWp), accounting for possible nonstationary cyclical long memory, a stochastic trend that can evolve over time and an additional serially correlated noise capturing the short-term variability. The model has a state space representation and makes use of the Kalman filter (KF). Suitable enhancements of the basic methodology enable handling data gaps, outliers, and offsets. We demonstrate our method using various climatological and geodetic time series to illustrate its potential to capture the time-varying stochastic seasonal signals.

How to cite: Kermarrec, G., Maddanu, F., Klos, A., and Proietti, T.: The fractional Sinusoidal wavefront Model (fSwp) for time series displaying persistent stationary cycles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-24, https://doi.org/10.5194/egusphere-egu24-24, 2024.

On some maps of the first military survey of the Habsburg Empire, the upper direction of the sections does not face the cartographic north, but makes an angle of about 15° with it. This may be due to the fact that the sections were subsequently rotated to the magnetic north of the time. Basically, neither their projection nor their projection origin is known yet.

In my research, I am dealing with maps of Inner Austria, the Principality of Transylvania and Galicia (nowadays Poland and Ukraine), and I am trying to determine their projection origin. For this purpose, it is assumed, based on the archival documentation of the survey, that these are Cassini projection maps. My hypothesis is that they are Graz, Cluj Napoca or Alba Julia and Lviv. I also consider the position of Vienna in each case, since it was the main centre of the survey.

The angle of rotation was taken in part from the gufm1 historical magnetic model back to 1590 for the assumed starting points and year of mapping. In addition, as a theoretical case, I calculated the rotation angle of the map sections using coordinate geometry. I then calculated the longitude of the projection starting point for each case using univariate minimization. Since the method is invariant to latitude, it can only be determined from archival data.

Based on these, the starting point for Inner Austria from the rotation of the map was Vienna, which is not excluded by the archival sources, and since the baseline through Graz also started from there, it is partly logical. The map rotation for Galicia and Transylvania also confirmed the starting point of the hypothesis.  Since both Alba Julia and Cluj Napoca lie at about the same longitude, the method cannot make a difference there; and the archival data did not provide enough evidence. In comparison, the magnetic declination rotations yielded differences of about 1°, which may be due to an error in the magnetic model.

On this basis, I have given the assumed projections of the three maps with projection starting points, and developed a method for determining the projection starting points of the other rotated grid maps. The results suggest that there is a very high probability that the section network was rotated in the magnetic north direction, and thus provide a way to refine the magnetic declination data at that time.

With this method I managed to give new indirekt magnetic declinations data from Central-East Europe, which can help to improve the historical magnetic field models. The main reason for this is that we don’t have any measurement from that region.

Furthermore the difference beetwen the angle of the section north and the declination data from gufm1 always 0.8-1°. Maybe there are systematical data error at that region.

Supported by the ÚNKP-23-6 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

How to cite: Koszta, B. and Timár, G.: A possible cartographical data source for historical magnetic field improvement: The direction of the section north of the Habsburg first military survey, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-582, https://doi.org/10.5194/egusphere-egu24-582, 2024.

The alteration of traditional hydrological patterns due to global warming is leading to a modification of the hydrological cycle. This situation draws a complex scenario for the sustainable management of water resources. However, this issue offers a challenge for the development of innovative approaches that allow an in-depth capturing the logical temporal-dependence structure of these modifications to advance sustainable management of water resources, mainly through the reliable predictive models. In this context, Bayesian Causality (BC), addressed through Causal Reasoning (CR) and supported by a Bayesian Networks (BNs), called Bayesian Causal Reasoning (BCR) is a novel hydrological research area that can help identify those temporal interactions efficiently.

This contribution aims to assesses the BCR ability to discover the logical and non-trivial temporal-dependence structure of the hydrological series, as well as its predictability. For this, a BN that conceptually synthesizes the time series is defined, and where the conditional probability is propagated over the time throughout the BN through an innovative Dependence Mitigation Graph. This is done by coupling among an autoregressive parametric approach and causal model. The analytical ability of the BCR highlighted the logical temporal structure, latent in the time series, which defines the general behavior of the runoff. This logical structure allowed to quantify, through a dependence matrix which summarizes the strength of the temporal dependencies, the two temporal fractions that compose the runoff: one due to time (Temporally Conditioned Runoff) and one not (Temporally Non-conditioned Runoff). Based on this temporal conditionality, a predictive model is implemented for each temporal fraction, and its reliability is assessed from a double probabilistic and metrological perspective.

This methodological framework is applied to two Spanish unregulated sub-basins; Voltoya river belongs to Duero River Basin, and Mijares river, in the Jucar River Basin. Both cases with a clearly opposite temporal behavior, Voltoya independent and Mijares dependent, and with increasingly more problems associated with droughts.

The findings of this study may have important implications over the knowledge of temporal behavior of water resources of river basin and their adaptation. In addition, TCR and TNCR predictive models would allow advances in the optimal dimensioning of storage infrastructures (reservoirs), with relevant substantial economic/environmental savings. Also, a more sustainable management of river basins through more reliable control reservoirs’ operation is expected to be achieved. Finally, these results open new possibilities for developing predictive hydrological models within a BCR framework.

How to cite: Zazo, S., Molina, J. L., Patino-Alonso, C., and Espejo, F.: Predictive ability assessment of Bayesian Causal Reasoning (BCR) on runoff temporal series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1988, https://doi.org/10.5194/egusphere-egu24-1988, 2024.

Climate-induced forest mortality poses an increasing threat worldwide, which calls for developing robust approaches to generate early warning signals of upcoming forest state change. This research explores the potential of satellite imagery, utilizing advanced spatio-temporal indicators and methodologies, to assess the state of forests preceding mortality events. Traditional approaches, such as techniques based on temporal analyses, are impacted by limitations related to window size selection and detrending methods, potentially leading to false alarms. To tackle these challenges, our study introduces two new approaches, namely the Spatial-Temporal Moran (STM) and Spatial-Temporal Geary (STG) approaches, both focusing on local spatial autocorrelation measures. These approaches can effectively address the shortcomings inherent in traditional methods. The research findings were assessed across three study sites within California national parks, and Kendall's tau was employed to quantify the significance of false and positive alarms. To facilitate the measurement of ecosystem state change, trend estimation, and identification of early warning signals, this study also provides "stew" R package. The implications of this research extend to various groups, such as ecologists, conservation practitioners, and policymakers, providing them with the means to address emerging environmental challenges in global forest ecosystems.

How to cite: Alibakhshi, S.: Spatial-Temporal Analysis of Forest Mortality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3857, https://doi.org/10.5194/egusphere-egu24-3857, 2024.

Iram Parvez¹, Massimiliano Cannata², Giorgio Boni¹, Rossella Bovolenta¹ ,Eva Riccomagno³ , Bianca Federici¹

¹ Department of Civil, Chemical and Environmental Engineering (DICCA), Università degli Studi di Genova, Via Montallegro 1, 16145 Genoa, Italy (iram.parvez@edu.unige.it,bianca.federici@unige.it, giorgio.boni@unige.it, rossella.bovolenta@unige.it).

² Institute of Earth Sciences (IST), Department for Environment Constructions and Design (DACD), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), CH-6952 Canobbio, Switzerland(massimiliano.cannata@supsi.ch).

³ Department of Mathematics, Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy(riccomag@dima.unige.it).

The deployment of hydrometeorological sensors significantly contributes to generating real-time big data. The quality and reliability of large datasets pose considerable challenges, as flawed analyses and decision-making processes can result. This research aims to address the issue of anomaly detection in real-time data by exploring machine learning models. Time-series data is collected from IstSOS - Sensor Observation Service, an open-source software that stores, collects and disseminates sensor data. The methodology consists of Gated Recurrent Units based on recurrent neural networks, along with corresponding prediction intervals, applied both to individual sensors and collectively across all temperature sensors within the Ticino region of Switzerland. Additionally, non-parametric methods like Bootstrap and Mean absolute deviation are employed instead of standard prediction intervals to tackle the non-normality of the data. The results indicate that Gated Recurrent Units based on recurrent neural networks, coupled with non-parametric forecast intervals, perform well in identifying erroneous data points. The application of the model on multivariate time series-sensor data establishes a pattern or baseline of normal behavior for the area (Ticino). When a new sensor is installed in the same region, the recognized pattern is used as a reference to identify outliers in the data gathered from the new sensor.

How to cite: Parvez, I.: Exploring Machine Learning Models to Detect Outliers in HydroMet Sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4280, https://doi.org/10.5194/egusphere-egu24-4280, 2024.

For its immunity to post-formation geological modifications, zircon is widely utilized as chronological time capsule and provides critical time series data potential to unravel key events in Earth’s geological history, such as supercontinent cycles. Fourier analysis, which assumes stationary periodicity, has been applied to zircon-derived time series data to find the cyclicity of supercontinents, and wavelet analysis, which assumes non-stationary periodicity, corroborates the results of Fourier Analysis in addition to detecting finer-scale signals. Nonetheless, both methods still prognostically assume periodicity in the zircon-derived time-domain data. To stay away from the periodicity assumption and extract more objective information from zircon data, we opt for a Bayesian approach and treat zircon preservation as a composite stochastic process where the number of preserved zircon grains per magmatic event obeys logarithmic series distribution and the number of magmatic events during a geological time interval obeys Poisson distribution. An analytical solution was found to allow us to efficiently invert for the number and distribution(s) of changepoints hidden in the globally compiled zircon data, as well as for the zircon preservation potential (encoded as a model parameter) between two neighboring changepoints. If the distributions of changepoints temporally overlap with those of known supercontinents, then our results serve as an independent, mathematically robust test of the cyclicity of supercontinents. Moreover, our statistical approach inherently provides a sensitivity parameter the tuning of which allows to probe changepoints at various temporal resolution. The constructed Bayesian framework is thus of significant potential to detect other types of trend swings in Earth’s history, such as shift of geodynamic regimes, moving beyond cyclicity detection which limits the application of conventional Fourier/Wavelet analysis.

How to cite: Qian, H., Tian, M., and Zhang, N.: Unveiling Geological Patterns: Bayesian Exploration of Zircon-Derived Time Series Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5268, https://doi.org/10.5194/egusphere-egu24-5268, 2024.

Semi-enclosed freshwater and brackish ecosystems, characterised by restricted water outflow and prolonged residence times, often accumulate nutrients, influencing their productivity and ecological dynamics. These ecosystems exhibit significant variations in bio-physical-chemical attributes, ecological importance, and susceptibility to human impacts. Untangling the complexities of their interactions remains challenging, necessitating a deeper understanding of effective management strategies adapted to their vulnerabilities. This research focuses on the bio-physical aspects, investigating the differential effects of spring and summer light on phytoplankton communities in semi-enclosed freshwater and brackish aquatic ecosystems.

Through extensive field sampling and comprehensive environmental parameter analysis, we explore how phytoplankton respond to varying light conditions in these distinct environments. Sampling campaigns were conducted at Müggelsee, a freshwater lake on Berlin's eastern edge, and Barther Bodden, a coastal lagoon northeast of Rostock on the German Baltic Sea coast, during the springs and summers of 2022 and 2023, respectively. Our analysis integrates environmental factors such as surface light intensity, diffuse attenuation coefficients, nutrient availability, water column dynamics, meteorological data, Chlorophyll-a concentration, and phytoplankton communities. Sampling encompassed multiple depths at continuous intervals lasting three days.

Preliminary findings underscore significant differences in seasonal light availability, with summer exhibiting extended periods of substantial light penetration. These variations seem to impact phytoplankton abundance and diversity uniquely in each ecosystem. While ongoing analyses are underway, early indications suggest distinct phytoplankton responses in terms of species composition and community structure, influenced by the changing light levels. In 2022 the clear water phase during spring indicated that bloom events have occurred under ice cover much earlier than spring, while in the summer there were weak and short-lived blooms of cyanobacteria. The relationship between nutrient availability and phytoplankton dynamics, however, remains uncertain according to our data.

This ongoing study contributes to understanding the role of light as a primary driver shaping phytoplankton community structures and dynamics in these environments.  Our research findings offer insights for refining predictive models, aiding in ecosystem-specific eutrophication management strategies, and supporting monitoring efforts of Harmful Algal Blooms.

How to cite: Kaharuddin, A. and Kaligatla, R.: Comparative Study of Spring and Summer Light Effects on Phytoplankton Communities in Semi-Enclosed Fresh- and Brackish Aquatic Ecosystems., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5733, https://doi.org/10.5194/egusphere-egu24-5733, 2024.

Forecasting the aurora and its location accurately is important to mitigate any potential harm to vital infrastructure like communications and electricity grid networks. Current auroral prediction models rely on our understanding of the interaction between the magnetosphere and the solar wind or geomagnetic indices. Both approaches do well in predicting but have limitations concerning forecasting (geomagnetic indices-based model) or because of the underlying assumptions driving the model (due to a simplification of the complex interaction). By applying machine learning algorithms to this problem, gaps in our understanding can be identified, investigated, and closed. Finding the important time scales for driving empirical models provides the necessary basis for our long-term goal of predicting the aurora using machine learning.

Periodicities of the Earth’s magnetic field have been extensively studied on a global scale or in regional case studies. Using a suite of different time series analysis techniques including frequency analysis and investigation of long-scale changes of the median/ mean, we examine the dominant periodicities of ground magnetic field measurements at selected locations. A selected number of stations from the SuperMAG network (Gjerloev, 2012), which is a global network of magnetometer stations across the world, are the focus of this investigation.

The periodicities retrieved from the different magnetic field components are compared to each other as well as to other locations. In the context of auroral predictions, an analysis of the dominating periodicities in the auroral boundary data derived from the IMAGE satellite (Chisham et al., 2022) provides a counterpart to the magnetic field periodicities.

Ultimately, we can constrain the length of time history sensible for forecasting.

How to cite: Gilmore, K. R., Bentley, S. N., and Smith, A. W.: Magnetospheric time history:  How much do we need for forecasting?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6065, https://doi.org/10.5194/egusphere-egu24-6065, 2024.

Dynamical systems can display a range of dynamical regimes (e.g. attraction to, fixed points, limit cycles, intermittency, chaotic behaviour) depending on the values of parameters in the system. In this work we demonstrate how non-parametric entropy estimation codes (in particular NPEET) based on the Kraskov method can be applied to find regime transitions in a 3D chaotic model (the Lorenz 1963 system) when varying the values of the parameters. These infromation-theory-based methods are simpler and cheaper to apply than more traditional metrics from dynamical systems (e.g. computation of Lyapunov exponents). The non-parametric nature of the method allows for handling long time series without a prohibitive computational burden. 

How to cite: Amezcua, J. and Chakraborty, N.: Using information-theory metrics to detect regime changes in dynamical systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6151, https://doi.org/10.5194/egusphere-egu24-6151, 2024.

Evaluation of scaling properties and fractal formalisms is one of the potential approaches for modelling complex series. Understanding the complexity and fractal characterization of drought index time series is essential for better preparedness against drought disasters. This study presents a novel visibility graph-based evaluation of fractal characterization of droughts of three meteorological subdivisions of India. In this method, the horizontal visibility graph (HVG) and Upside-down visibility graph (UDVG) are used for evaluating the network properties for different standardized precipitation index (SPI) series of 3, 6 and 12 month time scales representing short, medium and long term droughts. The relative magnitude of fractal estimates is controlled by the drought characteristics of wet-dry transitions. The estimates of degree distribution clearly deciphered the self-similar properties of droughts of all the subdivisions. For an insightful depiction of drought dynamics, the fractal exponents and spectrum are evaluated by the concurrent application of Sand Box Method (SBM) and Chhabra and Jenson Method (CJM). The analysis was performed for overall series along with the pre- and post-1976-77 Global climate shift scenarios. The complexity is more evident in short term drought series and UDVG formulations implied higher fractal exponents for different moment orders irrespective of drought type and locations considered in this study. Useful insights on the relationship between complex network and fractality are evolved from the study, which may help in improved drought forecasting. The visibility graph based fractality estimation evaluation is efficient in capturing drought and it has vast potential in the drought predictions in a changing environment.

Keywords:  Drought, Fractal, SPI, Visibility Graph

How to cite: Rajesh, S. M., Bahuleyan, M., Nair GR, A., and Sankaran, A.: Fractal complexity evaluation of meteorological droughts over three Indian subdivisions using visibility Graphs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9367, https://doi.org/10.5194/egusphere-egu24-9367, 2024.

The Wavelet-Induced Mode Extraction procedure (WIME) [2] was developed drawing inspiration from Empirical Mode Decomposition. The concept involves decomposing the signal into modes, each presenting a characteristic frequency, using continuous wavelet transform. This method has yielded intriguing results in climatology [3,4]. However, the initial algorithm did not account for the potential existence of slight frequency fluctuations within a mode, which could impact the reconstruction of the original signal [4]. The new version (https://atoms.scilab.org/toolboxes/toolbox_WIME/0.1.0) now allows for the evolution of a mode in the space-frequency half-plane, thus considering the frequency evolution of a mode [2]. A natural application of this tool is in the analysis of Milankovitch cycles, where subtle changes have been observed throughout history. The method also refines the study of solar activity, highlighting the role of the "Solar Flip-Flop." Additionally, the examination of temperature time series confirms the existence of cycles around 2.5 years. It is now possible to attempt to correlate solar activity with this observed temperature cycle, as seen in speleothem records [1].

[1] Allan, M., Deliège, A., Verheyden, S., Nicolay S. and Fagel, N. Evidence for solar influence in a Holocene speleothem record, Quaternary Science Reviews, 2018.
[2] Deliège, A. and Nicolay, S., Extracting oscillating components from nonstationary time series: A wavelet-induced method, Physical Review. E, 2017.
[3] Nicolay, S., Mabille, G., Fettweis, X. and Erpicum, M., A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method, Nonlinear Processes in Geophysics, 2010.
[4] Nicolay, S., Mabille, G., Fettweis, X. and Erpicum, M., 30 and 43 months period cycles found in air temperature time series using the Morlet wavelet, Climate Dynamics, 2009.

How to cite: Faulx, E., Fettweis, X., Mabille, G., and Nicolay, S.: Wavelet-Induced Mode Extraction procedure: Application to climatic data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9537, https://doi.org/10.5194/egusphere-egu24-9537, 2024.

A wide range of geoprocesses manifest as observable events in a variety of contexts, including shifts in palaeoclimate regimes, evolutionary milestones, tectonic activities, and more. Many prominent research questions, such as synchronisation analysis or power spectrum estimation of discrete data, pose considerable challenges to linear tools. We present recent advances using a specific similarity measure for discrete data and the method of recurrence plots for different applications in the field of highly discrete event data. We illustrate their potential for palaeoclimate studies, particularly in detecting synchronisation between signals of discrete extreme events and continuous signals, estimating power spectra of spiky signals, and analysing data with irregular sampling.

How to cite: Marwan, N. and Braun, T.: New concepts on quantifying event data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10258, https://doi.org/10.5194/egusphere-egu24-10258, 2024.

Nowadays photovoltaic panels are becoming more affordable, efficient, and popular due to their low carbon footprint. PV panels can be installed in many places providing green energy to the local grid reducing energy cost and transmission losses. Since the PV production is highly dependent on the weather conditions, it is extremely important to estimate expected output in advance in order to maintain energy balance in the grid and provide enough time to schedule load distribution. The PV production output can be calculated by various statistical and machine learning prediction methods. In general, the more data available, the more precise predictions can be produced. This poses a problem for recently installed PV panels for which not enough data has been collected or the collected data are incomplete. 

A possible solution to the problem can be the application of an approach called Transfer Learning which has the inherent ability to effectively deal with missing or insufficient amounts of data. Basically, Transfer Learning is a machine learning approach which offers the capability of transferring knowledge acquired from the source domain (in our case a PV panel with a large amount of historical data) to different target domains (PV panels with very little collected historical data) to resolve related problems (provide reliable PV production predictions). 

In our study, we investigate the application, benefits and drawbacks of Transfer Learning for one day ahead PV production prediction. The model used in the study is based on complex neural network architecture, feature engineering and data selection. Moreover, we focus on the exploration of multiple approaches of adjusting weights in the target model retraining process which affect the minimum amount of training data required, final prediction accuracy and model’s overall robustness. Our models use historical meteorological forecasts from Deutscher Wetterdienst (DWD) and photovoltaic measurements from the project PVOutput which collects data from installed solar systems across the globe. Evaluation is performed on more than 100 installed PV panels in Central Europe.

How to cite: Lóderer, M., Sandanus, M., Pavlík, P., and Rozinajová, V.: Application of Transfer Learning techniques in one day ahead PV production prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10415, https://doi.org/10.5194/egusphere-egu24-10415, 2024.

In each magnetic observatory, the magnetic field is registered in local Cartesian coordinate systems associated with the geographic coordinates of the locations of these observatories. To observe extraterrestrial magnetic field sources, such as the interplanetary magnetic field or magnetic clouds, a method of joint processing of data from magnetic observatories of the international Intermagnet network was implemented. In this method, the constant component is removed from the observation results of individual observatories, their measurement data is converted into the ecliptic coordinate system, and the results obtained from all observatories are averaged after the coordinate transformation.

The first data on joint processing of measurement results from the international network of Intermagnet magnetic observatories in the period before the onset of magnetic storms of various types, during these storms and after their end are presented. There is a significant improvement in the signal-to-noise ratio after combining the measurement results from all observatories, which makes it possible to isolate weaker external magnetic fields. A change in the shape of magnetic field variations is shown, which can provide new knowledge about the mechanism of development of magnetic storms.

How to cite: Zhumabayev, B., Vassilyev, I., Mendakulov, Z., Fedulina, I., and Kapytin, V.: Results of joint processing of magnetic observatory data of international Intermagnet network in a unified coordinate system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11897, https://doi.org/10.5194/egusphere-egu24-11897, 2024.

We introduce the CLEAN algorithm to identify narrowband Ultra Low Frequency (ULF) Pc5 plasma waves in Earth’s magnetosphere. The CLEAN method was first used for constructing 2D images in astronomical radio interferometry but has since been applied to a huge range of areas including adaptation for time series analysis. The algorithm performs a nonlinear deconvolution in the frequency domain (equivalent to a least-squares in the time domain) allowing for identification of multiple individual wave spectral peaks within the same power spectral density. The CLEAN method also produces real amplitudes instead of model fits to the peaks and retains phase information. We applied the method to GOES magnetometer data spanning 30 years to study the distribution of narrowband Pc5 ULF waves at geosynchronous orbit. We found close to 30,0000 wave events in each of the vector magnetic field components in field-aligned coordinates. We discuss wave occurrence and amplitudes distributed in local time and frequency. The distribution of the waves under different solar wind conditions are also presented. With some precautions, which are applicable to other event identification methods, the CLEAN technique can be utilized to detect wave events and its harmonics in the magnetosphere and beyond. We also discuss limitations of the method mainly the detection of unrealistic peaks due to aliasing and Gibbs phenomena.

How to cite: Inceoglu, F. and Loto'aniu, P.: Using the CLEAN Algorithm to Determine the Distribution of Ultra Low Frequency Waves at Geostationary Orbit, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12928, https://doi.org/10.5194/egusphere-egu24-12928, 2024.

Understanding volcanic activity through time series data analysis is crucial for uncovering the fundamental physical mechanisms governing this natural phenomenon.

In this study, we show the application of multifractal and fractal methodologies, along with statistical analysis, to investigate time series associated with volcanic activity. We aim to make use of these approaches to identify significant variations within the physical processes related to changes in volcanic activity. These methodologies offer the potential to identify pertinent changes preceding a high-energy explosion or a significant volcanic eruption.

In particular, we apply it to analyze two study cases. First, the evolution of the multifractal structure of volcanic emissions of low, moderate, and high energy explosions applied to Volcán de Colima (México years 2013-2015). The results contribute to obtaining quite evident signs of the immediacy of possible dangerous emissions of high energy, close to 8.0x10^8 J. Additionally, the evolution of the adapted Gutenberg-Richter seismic law to volcanic energy emissions contributes to confirm the results obtained using multifractal analysis. Secondly, we also studied the time series of the Gutenberg-Richter b-parameter of seismic activities associated with volcanic emissions in Iceland, Hawaii, and the Canary Islands, through the concept of Disparity (degree of irregularity), the fractal Hurst exponent, H, and several multifractal parameters. The results obtained should facilitate a better knowledge of the relationships between the activity of volcanic emissions and the corresponding related seismic activities.  

How to cite: Monterrubio-Velasco, M., Lana, X., Arámbula-Mendoza, R., and Zúñiga, R.: Applying Multifractal Theory and Statistical Techniques for High Energy Volcanic Explosion Detection and Seismic Activity Monitoring in Volcanic Time Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12938, https://doi.org/10.5194/egusphere-egu24-12938, 2024.

Spatially interpolated meteorological data products are widely used in the geosciences as well as disciplines like epidemiology, economics, and others. Recent work has examined methods for quantifying uncertainty in gridded estimates of near-surface air temperature that produce distributions rather than simply point estimates at each location. However, meteorological variables are correlated not only in space but in time, and sampling without accounting for temporal autocorrelation produces unrealistic time series and potentially underestimates cumulative errors. This work first examines how uncertainty in air temperature estimates varies in time, both seasonally and at shorter timescales. It then uses data-driven, spectral, and statistical methods to better characterize uncertainty in time series of estimated air temperature values. Methods for sampling that reproduce spatial and temporal autocorrelation are presented and evaluated. The results of this work are particularly relevant to domains like agricultural and ecology. Physical processes including evapotranspiration and primary production are sensitive to variables like near-surface air temperature, and errors in these important meteorological inputs accumulate in model outputs over time.

How to cite: Doherty, C. and Wang, W.: Characterizing Uncertainty in Spatially Interpolated Time Series of Near-Surface Air Temperature, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13593, https://doi.org/10.5194/egusphere-egu24-13593, 2024.

Vegetation phenology is the recurring of plant growth, including the cessation and resumption of growth, and plays a significant role in shaping terrestrial water, nutrient, and carbon cycles. Changes in temperature and precipitation have already induced phenological changes around the globe, and these trends are likely to continue or even accelerate. While warming has advanced spring arrival in many places, the effects on autumn phenology are less clear-cut, with evidence for earlier, delayed, or even unchanged end of the growing season (EOS). Meteorological droughts are intensifying in duration and frequency because of climate change. Droughts intricately impact changes in vegetation, contingent upon whether the ecosystem is limited by water or energy. These droughts have the potential to influence EOS changes. Despite this, the influence of drought on EOS remains largely unexplored. This study examined moisture’s role in controlling EOS by understanding the relationship between precipitation anomalies, vegetation’s sensitivity to precipitation (SPPT), and EOS. We also assess regional variations in responses to the impact of SPPT on EOS.

The study utilized multiple vegetation and water satellite products to examine the patterns of SPPT in drought and its impact on EOS across aridity gradients and vegetation types. By collectively evaluating diverse SPPTs from various satellite datasets, this work offers a comprehensive understanding and critical basis for assessing the impact of drought on EOS. We focused on the Northern Hemisphere from 2000 to 2020, employing robust statistical methods. This work found that, in many places, there was a stronger relationship between EOS and drought in areas with higher SPPT. Additionally, a non-linear negative relationship was identified between EOS and SPPT in drier regions, contracting with a non-linear positive relationship observed in wetter regions. These findings were consistent across a range of satellite-derived vegetation products. Our findings provide valuable insights into the effects of SPPT on EOS during drought, enhancing our understanding of vegetation responses to drought and its consequences on EOS and aiding in identifying drought-vulnerable areas.

How to cite: Choi, E. and Gray, J.: Understanding the role of vegetation responses to drought in regulating autumn senescence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13879, https://doi.org/10.5194/egusphere-egu24-13879, 2024.

We present a machine-learning-based approach for predicting the geomagnetic main field changes, known as secular variation (SV), in a 5-year range for use for the 14^th generation of International Geomagnetic Reference Field (IGRF-14). The training and test datasets of the machine learning (ML) models are geomagnetic field snapshots derived from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data (MCM Model; Ropp et al., 2020). The geomagnetic field data are not used as-is in the original time series but were differenced twice before training. Because SV is strongly influenced by the geodynamo process occurring in the Earth's outer core, challenges still persist despite efforts to model and forecast the realistic nonlinear behaviors (such as the geomagnetic jerks) of the geodynamo through data assimilation. We compare three physics-uninformed ML models, namely, the Autoregressive (AR) model, Vector Autoregressive (VAR) model, and Recurrent Neural Network (RNN) model, to represent the short-term temporal evolution of the geomagnetic main field on the Earth’s surface. The quality of 5-year predictions is tested by the hindcast results for the learning window from 2004.50 to 2014.25. These tests show that the forecast performance of our ML model is comparable with that of candidate models of IGRF-13 in terms of data misfits after the release epoch (Year 2014.75). It is found that all three ML models give 5-year prediction errors of less than 100nT, among which the RNN model shows a slightly better accuracy. They also suggest that Overfitting to the training data used is an undesirable machine learning behavior that occurs when the RNN model gives accurate reproduction of training data but not for forecasting targets.

How to cite: Sato, S. and Toh, H.: A machine-learning-based approach for predicting the geomagnetic secular variation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16981, https://doi.org/10.5194/egusphere-egu24-16981, 2024.

The quality of the seismic signal recorded at permanent and temporary stations is sometimes degraded, either abruptly or over time. The most likely cause is a high level of humidity, leading to corrosion of the connectors but environmental changes can also alter recording conditions in various frequency ranges and not necessarily for all three components in the same way. Assuming that the continuous seismic signal can be described by a normal distribution, we present a new approach to quantify the seismogram quality and to point out any time sample that deviates from this Gaussian assumption. To this end the notion of background Gaussian signal (BGS) to statistically describe a set of samples that follows a normal distribution. The discrete function obtained by sorting the samples in ascending order of amplitudes is compared to a modified probit function to retrieve the elements composing the BGS, and its statistical properties, mostly the Gaussian standard deviation, which can then differ from the classical standard deviation. Hence the ratio of both standard deviations directly quantifies the dominant gaussianity of the continuous signal and any variation reflects a statistical modification of the signal quality. We present examples showing daily variations in this ratio for stations known to have been affected by humidity, resulting in signal degradation. The theory developed can be used to detect subtle variations in the Gaussianity of the signal, but also to point out any samples that don't match the Gaussianity assumption, which can then be used for other seismological purposes, such as coda determination.

How to cite: Beucler, É., Bonnin, M., and Cuvier, A.: Introducing a new statistical theory to quantify the Gaussianity of the continuous seismic signal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17344, https://doi.org/10.5194/egusphere-egu24-17344, 2024.

Climate events may induce abnormal ocean wave activities, that can be detected by seismic array on nearby coastlines. We collected long-term continuous array seismic data in the Groningen area and the coastal areas of the North Sea, conducted a comprehensive analysis to extract valuable climate information hidden within the ambient noise. Through long-term spectral analysis, we identified the frequency band ranging from approximately 0.2Hz, which appears to be associated with swell waves within the region, exhibiting a strong correlation with the significant wave height (SWH). Additionally, the wind waves with a frequency of approximately 0.4 Hz and gravity waves with periods exceeding 100 seconds were detected from the seismic ambient noise. We performed a correlation analysis between the ambient noise and various climatic indexes across different frequency bands. The results revealed a significant correlation between the North Atlantic Oscillation (NAO) Index and the ambient noise around 0.17Hz.

Subsequently, we extracted the annual variation curves of SWH frequency from ambient noise at each station around the North Sea and assembled them into a sparse spatial grid time series (SGTS). An empirical orthogonal function (EOF) analysis was conducted, and the Principal Component (PC) time series derived from the EOF analysis were subjected to a correlation analysis with the WAVEWATCH III (WW3) model simulation data, thereby confirming the wave patterns. Moreover, we conducted the spatial distribution study of SGTS. The spatial features revealed that the southern regions of the North Sea exhibit higher wind-wave energy components influenced by the Icelandic Low pressure system and topography, which explains the correlation between ambient noise in the region and the NAO index. Furthermore, spatial features disclosed a correlation between the first EOF mode of the North Sea ocean waves and the third mode of sea surface temperature anomalies. This research shows the potential of utilizing existing off-shore seismic monitoring systems to study global climate variation and physical oceanography.

How to cite: Zhong, Y., Gu, C., Fehler, M., Prieto, G., Wu, P., Yuan, Z., Chen, Z., and Kang, B.: Unveiling Climate-Induced Ocean Wave Activities Using Seismic Array Data in the North Sea Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17566, https://doi.org/10.5194/egusphere-egu24-17566, 2024.

Several machine learning algorithms and analytical techniques do not allow gaps or non-values in input data. Unfortunately, earth observation (EO) datasets, such as satellite images, are gravely affected by cloud contamination and sensor artifacts that create gaps in the time series of collected images. This limits the usage of several powerful techniques for modeling and analysis. To overcome these limitations, several works in literature propose different imputation methods to reconstruct the gappy time series of images, providing complete time-space datasets and enabling their usage as input for many techniques.

However, among the time-series reconstruction methods available in literature, only a few of them are publicly available (open source code), applicable without any external source of data, and suitable for application to petabyte (PB) sized dataset like the full Landsat archive. The few methods that match all these characteristics are usually quite trivial (e.g. linear interpolation) and, as a consequence, they often show poor performance in reconstructing the images. 

For this reason, we propose a new methodology for time series reconstruction designed to match all these requirements. Like some other methods in literature, the new method, named seasonally weighted average generalization (SWAG), works purely on the time dimension, reconstructing the images working on each time series of each pixel separately. In particular, the method uses a weighted average of the samples available in the original time series to reconstruct the missing values. Enforcing the annual seasonality of each band as a prior, for the reconstruction of each missing sample in the time series a higher weight is given to images that are collected exactly on integer multiples of a year. To avoid propagation of land cover changes in future or past images, higher weights are given to more recent images. Finally, to have a method that respects causality, only images from the past of each sample in the time series are used.

To have computational performance suitable for PB sized datasets the method has been implemented in C++ using a sequence of fast convolution methods and Hadamard products and divisions. The method has been applied to a bimonthly aggregated version of the global GLAD Landsat ARD-2 collection from 1997 to 2022, producing a 400 terabyte output dataset. The produced dataset will be used to generate maps for several biophysical parameters, such as Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), normalized difference water index (NDWI) and bare soil fraction (BSF). The code is available as open source, and the result is fully reproducible.

References:

Potapov, Hansen, Kommareddy, Kommareddy, Turubanova, Pickens, ... & Ying  (2020). Landsat analysis ready data for global land cover and land cover change mapping. Remote Sensing, 12(3), 426.

Julien, & Sobrino (2019). Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data. International Journal of Applied Earth Observation and Geoinformation, 76, 93-111.

Radeloff, Roy, Wulder, Anderson, Cook, Crawford, ... & Zhu (2024). Need and vision for global medium-resolution Landsat and Sentinel-2 data products. Remote Sensing of Environment, 300, 113918.

How to cite: Consoli, D., Parente, L., and Witjes, M.: A new methodology for time-series reconstruction of global scale historical Earth observation data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18061, https://doi.org/10.5194/egusphere-egu24-18061, 2024.

Climate variability gives rise to many different kinds of extreme impact events, including heat waves, crop failures, or wildfires. The frequency and magnitude of such events are changing under global warming. However, it is less known to what extent such events occur with some regularity, and whether this regularity is also changing as a result of climate change. Here, we present a novel method to systematically study the time-autocorrelation of these extreme impact events, that is, whether they occur with a certain regularity. In studies of climate change impacts, different types of events are often studied in isolation, but in reality they interact. We use ensembles of global biophysical impact simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) driven with climate models to assess current conditions and projections. The time series analysis is based on a discrete Fourier transformation that accounts for the stochastic fluctuations from the climate model. Our results show that some climate impacts, such as crop failure, indeed exhibit a dominant frequency of recurrence; and also, that these regularity patterns change over time due to anthropogenic climate forcing.

How to cite: Zantout, K., Frieler, K., and Schewe, J. and the ISIMIP team: The regularity of climate-related extreme events under global warming, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18197, https://doi.org/10.5194/egusphere-egu24-18197, 2024.

The availability of comprehensive aerial photography is limited to the mid-20th century, posing a challenge for quantitatively analyzing long-term surface changes in proglacial areas. This creates a gap of approximately 100 years, spanning the end of the Little Ice Age (LIA). Employing digital monoplotting and historical terrestrial images, our study reveals quantitative surface changes in a LIA lateral moraine section dating back to the second half of the 19th century, encompassing a total study period of 130 years (1890 to 2020). With the long-term analysis at the steep lateral moraines of Gepatschferner (Kauner Valley, Tyrol, Austria) we aimed to identify changes in vegetation development in context with morphodynamic processes and the changing climate.

In 1953, there was an expansion in the area covered by vegetation, notably encompassing scree communities, alpine grassland, and dwarf shrubs. However, the destabilization of the system after 1980, triggered by rising temperatures and the resulting thawing of permafrost, led to a decline in vegetation cover by 2020. Notably, our observations indicated that, in addition to morphodynamic processes, the overarching trends in temperature and precipitation exerted a substantial influence on vegetation development. Furthermore, areas with robust vegetation cover, once stabilised, were reactivated and subjected to erosion, possibly attributed to rising temperatures post-1980.

This study demonstrates the capability of historical terrestrial images to enhance the reconstruction of vegetation development in context with morphodynamics in high alpine environments within the context of climate change. However, it is important to note that long-term mapping of vegetation development through digital monoplotting has limitations, contingent on the accessibility and quality of historical terrestrial images, as well as the challenges posed by shadows in high alpine regions. Despite these limitations, this long-term approach offers fundamental data on vegetation development for future modelling efforts.

How to cite: Ramskogler, K., Altmann, M., Mikolka-Flöry, S., and Tasser, E.: Long-term vegetation development in context of morphodynamic processes since mid-19th century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18210, https://doi.org/10.5194/egusphere-egu24-18210, 2024.

Discrimination of  geomagnetic quasi-periodic signals by using SSA Transform

• Palangio¹, L. Santarelli ¹

¹Istituto Nazionale di Geofisica e Vulcanologia L’Aquila

³Istituto Nazionale di Geofisica e Vulcanologia Roma

Correspondence to:  lucia.santarelli@ingv.it

Abstract

In this paper we present an application of  the SSA Transform to the detection and reconstruction of  very weak geomagnetic signals hidden in noise. In the SSA Transform  multiple subspaces are used for representing and reconstructing   signals and noise.  This analysis allows us to reconstruct, in the time domain, the different harmonic components contained in the original signal by using  ortogonal functions. The objective is to identificate the dominant  subspaces that can be attributed to the  signals and the subspaces that can be attributed to the noise,  assuming that all these  subspaces are orthogonal to each other, which implies that the  signals and noise  are independent of one another. The subspace of the signals is mapped simultaneously on several spaces with a lower dimension, favoring the dimensions that best discriminate the patterns. Each subspace of the signal space is used to encode different subsets of functions having common characteristics, such as  the same periodicities. The subspaces  identification was performed by using singular value decomposition (SVD) techniques,  known as  SVD-based identification methods  classified in a subspace-oriented scheme.The  quasi-periodic variations of geomagnetic field  has been investigated in the range of scale which span from 22 years to 8.9 days such as the  Sun’s polarity reversal cycle (22 years), sun-spot cycle (11 years), equinoctial effect (6 months), synodic rotation of the Sun (27 days) and its harmonics. The strength of these signals vary from fractions of a nT to tens of nT. Phase and frequency variability of these cycles has been evaluated from the range of variations in the geomagnetic field recorded at middle latitude place (covering roughly 4.5 sunspot cycles). Magnetic data recorded at L'Aquila Geomagnetic observatory (geographic coordinates: 42° 23’ N, 13° 19’E, geomagnetic coordinates: 36.3° N,87°.2 E, L-shell=1.6) are used from 1960 to 2009.

How to cite: Paolo Giovanni, P. and Lucia, S.: Discrimination of  geomagnetic quasi-periodic signals by using SSA Transform, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19601, https://doi.org/10.5194/egusphere-egu24-19601, 2024.

Over the past decades, especially since 2014, large quantities of Earth Observation (EO) data became available in high spatial and temporal resolution, thanks to ever-developing constellations (e.g.: Sentinel, Landsat) and open data policy. However, in the case of optical images, affected by cloud coverage and the spatially changing overlap of relative satellite orbits, creating temporally generalized and dense time series by using only measured data is challenging, especially when studying larger areas.

Several papers investigate the question of spatio-temporal gap filling and show different interpolation methods to calculate missing values corresponding to the measurements. In the past years more products and technologies have been constructed and published in this field, for example Copernicus HR-VPP Seasonal Trajectories (ST) product.  These generalized data structures are essential to the comparative analysis of different time periods or areas and improve the reliability of data analyzing methods such as Fourier transform or correlation. Temporally harmonized input data is also necessary in order to improve the results of Machine Learning classification algorithms such as Random Forest or Convolutional Neural Networks (CNN). These are among the most efficient methods to separate land cover categories like arable lands, forests, grasslands and built-up areas, or crop types within the arable category.

This study analyzes the efficiency of different interpolation methods on Sentinel-2 multispectral time series in the context of land cover classification with Machine Learning. We compare several types of interpolation e.g. linear, cubic and cubic-spline and also examine and optimize more advanced methods like Inverse Distance Weighted (IDW) and Radial Basis Function (RBF). We quantify the accuracy of each method by calculating mean square error between measured and interpolated data points. The role of interpolation of the input dataset in Deep Learning (CNN) is investigated by comparing Overall, Kappa and categorical accuracies of land cover maps created from only measured and interpolated time series. First results show that interpolation has a relevant positive effect on accuracy statistics. This method is also essential in taking a step towards constructing robust pretrained Deep Learning models, transferable between different time intervals and agro-ecological regions.

The research has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2021 funding scheme.

Keywords: time series analysis, Machine Learning, interpolation, Sentinel

How to cite: Simon, M., Richter-Cserey, M., Pacskó, V., and Kristóf, D.: Temporal Interpolation of Sentinel-2 Multispectral Time Series in Context of Land Cover Classification with Machine Learning Algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22262, https://doi.org/10.5194/egusphere-egu24-22262, 2024.

The variability of the atmospheric drag force on satellites is influenced by several factors, which include neutral density and gas composition, solar activity, and the orientation and shape of the satellite. During periods of geomagnetic activity, changes in these properties contribute to significant variations in the drag force, impacting the satellite trajectory. Therefore, the atmospheric drag force is considered as one of the largest sources of error in the orbit estimation for Low Earth Orbit (LEO) satellites. The methods presently used for satellite orbit determination define a constant term to represent the drag force, which can result in significant errors in long-term propagation and prediction of satellite positions. This work aims to improve SGP4 orbit propagation method by updating the drag term at regular intervals. The estimation of the drag term implements atmospheric drag analysis, which involves calculating the satellite drag coefficients using different Gas-Surface Interaction (GSI) models and neutral density data from Global Ionosphere Thermosphere Model (GITM) to estimate the mean motion derivative. The results demonstrate improved orbit propagation and estimation of orbital decay for the Gravity Recovery and Climate Experiment (GRACE) and Communication/Navigation Outage Forecasting System (C/NOFS) satellites during selected periods containing quiet and storm times.

How to cite: Dey, S., Anderson, P., and Bukowski, A.: Improving Orbit Propagation of LEO Satellites Using Atmospheric Drag Analysis   , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-827, https://doi.org/10.5194/egusphere-egu24-827, 2024.

Combined precise satellite orbits and clocks stand as core contributions from the International GNSS Service (IGS), integrating the individual inputs of various Analysis Centers (AC). The availability and quality of multi-GNSS products developed by ACs within the IGS multi-GNSS Pilot Project propel IGS towards replacing combined GPS and GLONASS products with homogeneous combined products encompassing all GPS, GLONASS, Galileo, BeiDou, and QZSS systems. A primary challenge faced by the IGS lies in refining the combination algorithm for multi-GNSS orbits and clocks to provide users with the utmost quality products.

This study delves into concepts aimed at enhancing the orbit combination algorithm, with a specific focus on adjusting the weighting scheme and detecting outlier observations. The core of the combination methodology adheres to the concept proposed by GFZ, employing a least-squares framework wherein weights used for combining AC orbits are determined through least-squares variance component estimation (VCE).

Four distinct weighting strategies are introduced and compared in this study. These strategies involve utilizing either the constellation, satellite type, satellite type on the same orbital plane, or each satellite individually to form datasets used in determining weights for each AC. Furthermore, a novel approach is developed to correct the weights for individual ACs based on the results of Satellite Laser Ranging orbit validation. This serves as an additional factor in the combination, mitigating the impact of systematic AC-dependent orbit mismodeling issues. All proposed strategies underwent testing using multi-GNSS orbit solutions over a 10-month period in 2023.

Firstly, the combination results show an agreement between the different AC’s input orbits around 15, 20, 30, 50, and 100 mm for GPS, GLONASS, Galileo, BeiDou, and QZSS, respectively. Regarding the AC weighting strategy, the constellation-specific weighting approach provides the most robust solution and allows for handling differences between AC-specific issues in the orbit modeling of individual constellations. The satellite-specific weighting approach offers better resilience against the adverse effects caused by the inhomogeneous quality of satellite blocks/types/generations within a constellation, especially for BeiDou. However, the satellite-specific weighting encounters problems related to the appearance of invalid negative variances/weights for individual satellites as the output of VCE, mainly for BDS-3 and QZSS. The negative variance component can be an important indication of defects in our variance component model. Grouping satellites of similar characteristics in a satellite-type-specific weighting approach increases redundancy and reduces the issue but not entirely. Ultimately, we demonstrate potential solutions to address this issue. This involves simplifying the iterated VCE or resorting to the legacy inverse mean square differences between the mean orbit and the AC’s orbits as weights, particularly in cases where the classic VCE proves ineffective.

How to cite: Zajdel, R., Mansur, G., Brack, A., Sakic, P., and Männel, B.: Optimizing Multi-GNSS Orbit Combination: A Comprehensive Study on Weighting Strategies and Outlier Detection ;, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-876, https://doi.org/10.5194/egusphere-egu24-876, 2024.

Orbital elements define the shape, size, and orientation of a satellite’s orbit, as well as the position of the orbiting satellite along the ellipse at a specific time. Currently, precise orbit determination relies solely on satellite observations. However, future plans involve equipping Genesis and Galileo satellites with Very Long Baseline Interferometry (VLBI) transmitters. This would enable to observe satellites with VLBI radio telescopes and allow to determine orbital elements from VLBI observations to satellites.

This study investigates the potential of VLBI observations to satellites to contribute to the determination of orbital elements. In a first step, schedules, including satellite and quasar observations, are created using VieSched++.  The Vienna VLBI and Satellite Software (VieVS) is used to simulate and analyze these schedules.  To introduce the orbital elements in the Least Squares Adjustment, the partial derivatives of the position vector with respect to the orbital elements are needed. There are different approaches available for computing these partials, including numerical, analytical, or using partials obtained from the ORBGEN module in the Bernese GNSS software.  Next, the partials of the position vector are utilized to determine the partial derivative of the time delay tau with respect to the orbital elements.

This enables the estimation of orbital elements from simulated VLBI observations to satellites. The estimated parameters' quality is evaluated based on the mean formal errors and repeatabilities. Furthermore, the correlations between all orbital elements are examined.

How to cite: Wolf, H., Böhm, J., and Hugentobler, U.: Potential of VLBI observations to satellites to estimate orbital elements , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2806, https://doi.org/10.5194/egusphere-egu24-2806, 2024.

GNSS processing for POD suffers from systematic errors when the location of the instantaneous phase center is not known
with a few millimeter accuracy. The traditional approach to model the GNSS phase center origin and variations is to
determine antenna phase maps iteratively from the residuals. A more direct approach consists in modeling the antenna
phase map through an expansion in well-chosen Zernike polynomials, in order to reduce potential correlations with dynamic
modeling errors. This strategy has been applied to derive the GNSS antenna phase maps of several altimetry missions such
as Sentinel-3A, Jason-3, Sentinel-6 MF and SWOT. The derived phase corrections were tested on CNES POE-G GNSS-only
reduced-dynamic orbit solutions to assess their impact and validate their benefits owing to independent SLR observations. 

How to cite: Baños Garcia, A.: In-flight GNSS phase map calibration modelling with Zernike polynomials, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3521, https://doi.org/10.5194/egusphere-egu24-3521, 2024.

Combined satellite-only global gravity field models represent a combined solution of gravity field observations from multiple geodetic measurement principles and satellite missions. The advantage of such a combination is that it compensates for the weaknesses of individual observing techniques. However, when combining solutions estimated by different institutions, inconsistencies may arise due to the different algorithms and models used in the actually available software, leading to a deterioration in performance. To mitigate such performance degradation, it is advantageous to perform all evaluations with a uniform software package. Since our in-house Gravity Recovery Object Oriented Programming System (GROOPS) software tool has become a widely accepted tool in the scientific community, we have now also incorporated the Satellite Laser Ranging (SLR) functionality to ensure the continued development of the software. On this basis, the opportunity arises to uniformly determine all the contributions to the combined gravity field using a single software package. This contribution is intended as a preliminary study for the next Gravity Observation Combination (GOCO) model. In this regard, we present low-degree solutions based on SLR observations using GROOPS and compare them with findings from other research groups (e.g., Cheng et al. 2013, Krauss et al. 2019) as well as solutions based on the satellite mission GRACE and GRACE-FO.

How to cite: Suesser- Rechberger, B., Mayer-Guerr, T., Krauss, S., Dumitraschkewitz, P., Oehlinger, F., and Tieber-Hubmann, C.: Contribution of SLR to satellite-only global gravity field model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5366, https://doi.org/10.5194/egusphere-egu24-5366, 2024.

Since 2023, three different solutions for the latest (2020) realization of the International Terrestrial Reference System (ITRS) are publicly available, namely the ITRF2020, the JTRF2020 and the DTRF2020. All solutions are based on the same input data but were derived using different combination approaches and data correction strategies. Since the ITRS realizations are used as a priori reference frames for precise orbit determination (POD) of Earth orbiting satellites, it is important to investigate the impact of the different frames on the POD results.

In this study, we briefly introduce the different features of the ITRS realizations and elaborate how the data correction models (e.g., periodic variations vs. non-tidal loading corrections) of the different xTRF2020 solutions can be optimally applied for the POD of selected satellites tracked by Satellite Laser Ranging (SLR) stations. We conclude the study with results obtained for various orbital parameters, such as the scaling factor of the non-gravitational (non-conservative) accelerations as well as the estimated empirical accelerations. Finally, we summarize the optimal settings of each ITRS realization for the satellite PODs discussed in this paper (i.e., LAGEOS-1, LARES-2, Jason-1/2/3).

How to cite: Bloßfeld, M., Zeitlhöfler, J., Rudenko, S., and Seitz, M.: Application of ITRS 2020 realizations for the SLR-based POD of selected geodetic and Earth-observing satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5385, https://doi.org/10.5194/egusphere-egu24-5385, 2024.

Laser ranging to spherical satellites is a major source for the determination of geophysical and geometric parameters like the scale of the reference system and the lowest degree (1-2) gravity field coefficients, i.e. geocenter motion, dynamic oblateness and the orientation of the rotation axis of the Earth. The International Laser Ranging Service (ILRS) is collecting Satellite Laser Ranging (SLR) observations of a global station network, providing the range data as normal points to its analysis centers, which perform precise orbit determination (POD) and network solutions based on 7 day orbital arcs. Currently, efforts are undertaken to extend the classical ILRS processing of the LAGEOS and ETALON satellites by LARES 2, as well as lower-flying satellites, i.e. LARES, Stella and Starlette, necessitating an adaption of the SLR-POD model and parametrization due to the increased sensitivity to orbit perturbations at low orbit altitudes.

We present the status of the SLR-POD at AIUB, where in support of the ILRS analysis center at BKG the incorporation of the low-flying SLR satellites into the 7 day POD and network solution of LAGEOS/ETALON/LARES-2 is beeing tested, making use of long-arc stacking techniques of daily arcs to continuous 7 day arcs. All orbit and geophysical parametes are estimated in one common estimation process to avoid implicit regularization by apriori information. Special attention is paid to the co-estimation of the low-degree gravity field coefficients and the correlations with the empirical dynamic parameters deployed in the classical LAGEOS/ETALON POD model.

How to cite: Meyer, U., Geisser, L., König, D., Dach, R., Thaller, D., and Jäggi, A.: Orbit parameterization aspects in global solutions of spherical Laser Ranging Satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5427, https://doi.org/10.5194/egusphere-egu24-5427, 2024.

Satellite Laser Ranging (SLR) observations are provided by a global station network of the International Laser Ranging Service (ILRS). Since almost all SLR stations are unique in terms of utilized equipment, e.g., laser system, photo-detector or timing devices, their measurement performances may slightly differ. Furthermore, the tracked satellites have various properties, e.g., material composition (number and type of retro-reflectors or mantel material), diameter, area-to-mass ratio, altitude or inclination, which have an impact on the measurement precision and the requirements on the background force modeling. A reliable estimation of geodetic parameters solely based on SLR can only be performed by combining SLR observations to several spherical satellites provided by the entire ILRS network. To take the quality of each individual SLR measurement into account, several stochastic models, e.g., static or time-variable station- and/or satellitespecific weights, are introduced and their impact on the parameter estimation is studied.

How to cite: Geisser, L., Meyer, U., Arnold, D., and Jäggi, A.: Stochastic Modeling of SLR Observations and its Impact on the Parameter Estimation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5649, https://doi.org/10.5194/egusphere-egu24-5649, 2024.

The GENESIS mission plays a pivotal role in the FutureNAV program envisioned by the European Space Agency. This groundbreaking venture aims to co-locate four space geodetic techniques on a single platform in space. One of the primary mission objectives is accurately determining geodetic parameters, including geocenter motion, low-degree gravity field, Earth rotation parameters, and the positions of ground stations in tracking networks. Although preliminary satellite inclination and altitude were provided, there is untapped potential for studying ways to enhance the efficiency of incorporating GENESIS into geodetic products.

This research focuses on the preliminary optimization of GENESIS orbital parameters – semi-major axis, inclination, and eccentricity- to assess the benefits arising from diverse observation geometries on geodetic products. The analysis is based on simulations of different variants of GENESIS orbital parameters tracked by a network of 20 satellite laser ranging (SLR) stations. We provide GENESIS-only solutions as well as the combined solution with a selected subset of geodetic satellites that are used today or in the future for the realization of the terrestrial reference frames: LAGEOS-1, LAGEOS-2, LARES-1, and LARES-2. GENESIS will be equipped with two GNSS receivers contributing to high-accuracy orbit determination, serving as a priori parameters for SLR-based solutions. Therefore, we check the GENESIS sensitivity to global geodetic parameters, assuming that the precise orbits are not derived from SLR but can be well-defined from other techniques.

We study the impact of different GENESIS orbits on the gravity potential parameters, especially the zonal terms, Earth rotation parameters, and geocenter coordinates. Our findings underscore that, through meticulous processing, GENESIS has the potential to significantly contribute to achieving the goals of the Global Geodetic Observing System (GGOS), particularly in terms of refining the Z component of the geocenter coordinates.

How to cite: Kur, T. and Sośnica, K.: Sensitivity of different variants of GENESIS orbit to global geodetic parameters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6359, https://doi.org/10.5194/egusphere-egu24-6359, 2024.

All BeiDou global navigation satellite system (BDS-3) satellites are equipped with Ka-band inter-satellite link (ISL) payloads to achieve the inter-satellite ranging and communication. With the rapid development of low earth orbit (LEO) satellites, the LEO onboard BDS-3 observations also become available. The LEO onboard and ISL data can be an effective supplement for precise orbit determination (POD) of the BDS-3 satellite. In this research, we processed the integrated POD of BDS-3 and LEO satellites with the real ground station, LEO onboard GNSS, and ISL observations. To analyze the contribution of different data to BDS-3 POD accuracy, four POD schemes are present: ground stations only, ground stations + 1 LEO, ground stations + ISL, and ground stations + 1 LEO + ISL. The ground tracking data are from about 40 globally distributed ground stations and 10 stations in Asia-Pacific region, respectively. The LEO onboard GNSS observations are from a dual-constellation GNSS receiver of the LUTAN-01A satellite, which is a Chinese LEO synthetic-aperture-radar (SAR) satellite for geological observation. The Ka-band observations from more than 400 ISL established between BDS-3 satellites are also used in the integrated POD. The obtained orbits are evaluated by orbit overlaps comparison, the comparison with IGS analysis center precise orbits, and SLR (satellite laser ranging) validation. Compared to the POD using the observation of only 10 ground stations, the addition of 1 LEO onboard GNSS or ISL observations can improve the orbit accuracy of BDS-3 by more than 60%. Furthermore, adding both LEO onboard GNSS and ISL observations simultaneously can lead to further improvements in BDS-3 orbit accuracy.

How to cite: Chen, H., Geng, T., Xie, X., Li, Q., Su, X., and Zhao, Q.: Enhanced orbit determination for BDS-3 satellites with LEO onboard GNSS and inter-satellite link data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7776, https://doi.org/10.5194/egusphere-egu24-7776, 2024.

For radar altimetry missions such as Sentinel-6, a precise orbit is mandatory for deriving reliable sea surface heights. In addition to accurate tracking measurements, precise orbit determination relies on high-fidelity non-gravitational force models. At 1300km the solar radiation pressure (SRP), which varies mainly with the satellite’s orientation towards the Sun, is the dominating non-gravitational force. Additionally, the acceleration due to the Earth radiation pressure (ERP) acts on the satellite and decreases with increasing satellite altitude. As the radiation of Sun and Earth is partly absorbed at the satellite’s surface, the satellite experiences an additional force due to the thermal reradiation of heat (thermal reradiation pressure, TRP). Aerodynamic forces are in constrast negligible at around 1300km altitude.

In previous investigations, we extended the conventional radiation pressure force models with a focus on a GRACE-like satellite. Our SRP and ERP model extensions can be easily applied to other satellites with available geometry and thermo-optical material properties. However, transferring the heat-conductive TRP model to other satellites is more challenging, as assumptions on the materials’ thermal diffusivity and thickness as well as inner heat sources need to be made to adequately model the satellite’s surface temperature.

In this study, we attempt to develop a TRP force model for Sentinel-6. We depart from GRACE settings and refine our model stepwise. Boundary conditions are updated and the material properties are replaced with available information or assumptions. Then, a stepwise tuning is necessary to match the modelled surface temperatures with thermistor measurements. We choose one beta cycle during the year 2023 for our experiments. Preliminary investigations reveal that the tuning of the satellite’s thermal properties varies strongly with the beta angle.

How to cite: Vielberg, K., Kusche, J., and Peter, H.: Tuning thermal reradiation pressure accelerations for Sentinel-6, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7841, https://doi.org/10.5194/egusphere-egu24-7841, 2024.

High-quality GPS observations from low Earth orbiting (LEO) satellites can be used to derive thermosphere densities along the satellite orbit. Such GPS-derived densities are currently computed operationally for all three Swarm satellites, in the framework of the Swarm Data, Innovation, and Science Cluster. Considering the increasing number of LEO satellites equipped with GPS receivers, this so-called GPS-based accelerometry approach offers great potential for improving thermosphere models and for studying the influence of solar and geomagnetic activity on the thermosphere.

To better quantify the accuracy that can be obtained with this approach, we assess the performance using the GRACE mission as a test case. For this mission high quality accelerometer data are available, which we can use to validate our GPS-based results. In addition, the GRACE mission has experienced a large variation in density signals, which allows us to assess the performance under a large range of conditions.

We present our GPS-based accelerometry processing strategy, which is based on a Kalman filter approach. The radiation pressure accelerations are accurately modelled and empirical accelerations capture the remaining aerodynamic signal. The empirical accelerations are modeled as Gauss-Markov processes defined by a steady-state variance, process noise and correlation time, which require careful tuning. This applies in particular to the setting of the process noise in the along-track direction, due to the large variations in the encountered aerodynamic signal. Best performance is obtained when the process noise setting is adapted to these variations. Using these adaptive filter settings, results are shown for periods with low, moderate, and high density. In a next step, we will implement the improved filter settings into our regular Swarm GPS-derived density processing chain.

How to cite: van den IJssel, J., Siemes, C., and Visser, P.: Assessment of GPS-based accelerometry performance with adaptive filter settings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8395, https://doi.org/10.5194/egusphere-egu24-8395, 2024.

The Copernicus Precise Orbit Determination (CPOD) Service delivers, as part of the Ground Segment of the Copernicus Sentinel-1, -2, -3, and -6 missions, orbital products and auxiliary data files for their use in the corresponding Payload Data Ground Segment (PDGS) processing chains at ESA and EUMETSAT, and to external users through the newly available Copernicus Data Space Ecosystem (https://dataspace.copernicus.eu/).

The CPOD Service is based on FocusPOD, a suite of tools for POD and geodesy powered by GMV MAORI, a new GMV in-house Flight Dynamics & Geodesy library, written from scratch in modern C++ and python. The CPOD Service makes use of GNSS measurements to generate the orbital products, and of Satellite Laser Ranging (SLR) measurements for validation purposes. Recently, DORIS-processing capabilities have been added to FocusPOD to generate orbit solutions based on this geodetic technique, which is available on-board Sentinel-3 and -6 missions.

The purpose of this work is to present the DORIS processing capabilities recently developed, including DORIS RINEX data parsing, observation preprocessing steps, and precise orbit determination results. The highlight will be the achievable accuracy based on DORIS and an analysis of the obtained residuals depending on the chosen parametrisation, including validation against GNSS-based solutions and SLR.

How to cite: Fernández, M., Fernández, C., Peter, H., Féménias, P., and Nogueira Loddo, C.: Copernicus POD Service – POD RESULTS BASED ON DORIS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8672, https://doi.org/10.5194/egusphere-egu24-8672, 2024.

The Copernicus Precise Orbit Determination (CPOD) Service delivers, as part of the Ground Segment of the Copernicus Sentinel-1, -2, -3, and -6 missions, orbital products and auxiliary data files for their use in the corresponding Payload Data Ground Segment (PDGS) processing chains at ESA and EUMETSAT, and to external users through the newly available Copernicus Data Space Ecosystem (https://dataspace.copernicus.eu/).

For the last few years, a progressive degradation of the accuracy of some of the CPOD orbital products has been observed, caused by the increasing solar activity. Solar activity follows an 11-year cycle, and since the minimum in 2020, overall solar activity has been higher and geomagnetic storms have been happening more often. This has two impacts on POD: a more “turbulent” atmosphere which brings higher frequency density changes, making the modelling of the satellite´s drag more challenging, and an increase of ionospheric scintillation, particularly over the polar regions, that causes an increase of the GNSS carrier phase noise.

This study aims at characterising better the impact of the solar activity on the different POD products, with a focus on the degradation of orbit determination and predictions required by the Sentinel-1 mission. Overall tendencies since the solar minimum in 2020 are analysed, including a detailed look on strong geomagnetic storms. Mitigation strategies to overcome this situation are evaluated, namely adapting the POD processing dynamic parametrisation, and updating the solar activity proxies (particularly the geomagnetic indexes) more frequently.

How to cite: Lara Espinosa, S., Fernández, C., Fernández, M., Peter, H., and Féménias, P.: Copernicus POD Service – Impact of High Solar activity on POD, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8756, https://doi.org/10.5194/egusphere-egu24-8756, 2024.

Among the core products of the International GNSS Service (IGS) are precise satellite orbits and clocks, which are generated by the Analysis Center Coordinator (ACC) as a combination of the solutions provided by different Analysis Centers (AC). A strategic goal of the IGS is to facilitate multi-GNSS solutions, implying that the currently operational system-wise GPS and GLONASS combinations should be replaced by a consistent set of multi-GNSS products, eventually containing at least GPS, GLONASS, Galileo, BeiDou, and QZSS.

Over the past years, the Satellite Precise Orbit and Clock Combination (SPOCC) software tool has been developed at GFZ. It provides a fully consistent multi-GNSS orbit and clock combination that covers all available and possible future constellations and is based on a well-defined unified least-squares framework. The resulting combined orbit and clock products are a weighted average of the individual AC solutions with weights determined through least-squares variance component estimation (VCE). A main objective is to support multi-GNSS precise point positioning (PPP) users.

We will introduce the combination workflow, which essentially consists of alignments harmonizing the AC products followed by the VCE and the weighted averaging, and is complemented by quality checks such as outlier detection. For the orbit combination, the alignment consists of Helmert transformations applied to the AC orbits, which is iterated with the VCE-based weighted averaging until convergence. The clock alignments consist of a radial correction from the orbit differences between the AC solutions, a removal of the impact of different reference clocks in the AC solutions, as well as an adjustment of all non-GPS satellite clocks for different inter-system bias (ISB) references at the ACs. The combination can be configured for different weighting schemes, including AC specific weights, AC+constellation specific weights, up to satellite type or even satellite specific weights, and the Helmert transformations can be based on different sets of satellite orbits.

The SPOCC software has been extensively tested with the operational IGS products, the IGS Multi-GNSS Experiment (MGEX) products, and the IGS repro3 products. Performance evaluations by means of a comparison of the combination with the input products and the official IGS combination, through a satellite laser ranging (SLR) validation, and with PPP results will be used to show that the software achieves reliable results that are suitable for the users’ high precision GNSS applications.

SPOCC is implemented in Python and will be provided as open source software.

How to cite: Brack, A., Mansur, G., Sakic, P., Zajdel, R., and Männel, B.: SPOCC - a GFZ Software Tool for a Multi-GNSS Orbit and Clock Combination, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10249, https://doi.org/10.5194/egusphere-egu24-10249, 2024.

Progress in precise satellite orbit determination (POD) and navigation depends on the future ranging and time transfer capabilities. This leads to the need for high-precision links as well as high-precision clocks. However, not only a higher accuracy is required, but also the use of combination of complementary observation techniques to reduce systematic errors within an observation network. In a simulation study, we show our first concept of a GEodesy and Time Reference In Space (GETRIS). The initial GETRIS concept is based on the idea to have the reference in space build on geosynchronous orbit (GSO) satellites, but using Medium Earth Orbit (MEO) satellites is also possible. The goal is to achieve orbit accuracies of the GETRIS satellites at the same level as for ground stations – a few millimeters. Using high-precision optical links, the GETRIS shall establish connections to satellites in the near Earth environment and in deep space. When creating our GETRIS concept, we focus on three key pillars of a simulation study: Instrumentation, geometry and modelling. In terms of instrumentation, we performed scenarios using the synergy between L-band observations as well as high-precision dual one-way Optical Inter-Satellite Links (OISL) and ground-space based dual one-way links, called Optical Two-Way Links (OTWL), in different combinations. The geometry changes depending on the used observations network and the satellite constellation. Thereby, we analyze scenarios using a MEO-only and MEO+GSO constellations. To finally achieve mm-level orbit accuracies, an advanced modelling of non-gravitational forces is essential. A GETRIS can not only help with the connection of near Earth and deep space satellites, but also support the Global Geodetic Observing System (GGOS) and satellite missions such as GENESIS, which aim to realize a precise terrestrial reference frame with 1 mm accuracy and a stability of 0.1 mm/year.

How to cite: Marz, S., Schlicht, A., and Hugentobler, U.: Towards a GEodesy and Time Reference In Space (GETRIS): A simulation study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10344, https://doi.org/10.5194/egusphere-egu24-10344, 2024.

The ESA GENESIS mission, which obtained green light at ESA's Council Meeting at Ministerial Level in November 2022 and which is expected to be launched in 2027, aims to significantly enhance the accuracy and stability of the Terrestrial Reference Frame (TRF). This shall be achieved by equipping one satellite at approximately 6000 km altitude with well-calibrated instruments for all four space-geodetic techniques contributing to TRF realizations, i.e., Global Navigation Satellite Systems (GNSS), Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), and by exploiting the such realized very precise space collocations.

The GENESIS satellite is foreseen to carry a zenith- and nadir-pointing GNSS antenna to track (at least) GPS and Galileo signals. Because of its very high altitude, GENESIS will cover much larger nadir angles as seen from the GNSS transmitting antennas, compared to receivers at ground or in low Earth orbit. This will partly result in GNSS observations with lower signal-to-noise ratios. Furthermore, to date, only little reliable information is available on the GNSS transmit antenna gain and carrier phase patterns at very large nadir angles. These problems lead to questions with respect to the best possible exploitation of GNSS data by GENESIS, e.g., whether phase pattern calibrations will need to be performed by means of tracked GNSS data (which would weaken the GNSS contribution to GENESIS).

The aim of this study is to assess the impact of GNSS transmit antenna phase pattern errors on the GNSS-based POD of GENESIS, as well as global GNSS network solutions for GNSS orbits and clock corrections, Earth rotation and geocenter parameters and station coordinates based on GNSS observations from GENESIS and terrestrial stations. To accomplish this, we employ simulated GNSS pseudo-range and carrier phase data for GENESIS and ground stations, which have been generated based on detailed link-budget computations and a comprehensive set of transmit antenna gain patterns. The data are used for closed-loop simulation investigations, which allow to compare the reconstructed orbit and geodetic parameter solutions to the simulation truth and offer a quantification of the impact of transmit antenna phase pattern uncertainties on the estimated parameters.

How to cite: Arnold, D., Miller, A., Kobel, C., Montenbruck, O., Steigenberger, P., and Jäggi, A.: GNSS-based orbit and geodetic parameter estimation by means of simulated GENESIS data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12683, https://doi.org/10.5194/egusphere-egu24-12683, 2024.

How to cite: Salas, M., Fernández, J., and van den IJssel, J.: Precise Relative Navigation in Medium Earth Orbits with Global Navigation Satellite Systems and Intersatellite Links for Black Hole Imaging, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13048, https://doi.org/10.5194/egusphere-egu24-13048, 2024.

Improving the concepts of Global Navigation Satellite Systems (GNSS) has become a vital task for subsequent GNSS applications like the determination of the Terrestrial Reference Frame (TRF). A promising goal is the reduction of uncertainties in non-conservative force modeling such as Solar Radiation Pressure (SRP) modelling in Precise Orbit Determination (POD) and the effect on the estimated orbits and geocenter coordinates. In previous simulation studies, accelerometers on next-generation GNSS satellites have proven to be a promising opportunity. In this way, the periodic signals in the estimated geocenter coordinates induced by SRP mismodeling can be eliminated regardless of the angle of the Sun to the satellite and its orbital plane. At the same time, the satellite clock can be effectively decoupled from the satellite position estimates. In this study, we focus on the impact of highly accurate clocks and the synchronization of clocks between satellites, which would make it possible to estimate common clock parameters for the synchronized satellites. In doing so, we start with Galileo-type POD using prior simulated observations with the assumption of perfectly known clocks. Then, we simulate various scenarios assuming different clock models and compare the results with the perfect case scenario. This procedure will explore the potential of various ground reference and satellite clock accuracies. Additionally, we use inter-satellite links to synchronize the satellite clocks over one and over multiple orbital planes. Finally, we strive to assess the potential of improved clock modeling on the TRF, focusing on the estimation of geocenter coordinates.

How to cite: Schreiner, P., Glaser, S., König, R., Neumayer, K. H., Raut, S., and Schuh, H.: On the potential of highly accurate clocks and inter-satellite clock synchronization for GNSS satellite precise orbit and geocenter determination, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16139, https://doi.org/10.5194/egusphere-egu24-16139, 2024.

Routine and occasional/emergency orbital maneuvers are essential for many satellites to maintain their optimal trajectory and to achieve a wide range of operational objectives in a continuous way. However, incorrectly modelled highly dynamic changes of the orbit during maneuvers can significantly reduce the accuracy of precise orbit determination (POD) to an extent that is unacceptable for scientific requirements.

The aim of this study is to investigate strategies for maneuver handling of Low Earth Orbiting (LEO) satellites based on observations from on-board GNSS receivers complemented by a priori knowledge of thrust intensity and maneuver epochs that are provided by telemetry measurements. Assuming that the initial information is subject to instrumental biases, corrections for the maneuver accelerations are estimated together with nominal deterministic and pseudo-stochastic orbit parameters such as instantaneous velocity changes and piecewise constant accelerations.

Several estimation strategies are tested using recent developments in the Bernese GNSS software, which is continuously maintained and further developed at the Astronomical Institute of the University of Bern (AIUB). In particular, the test cases cover single long/short maneuvers as well as two consecutive maneuvers within one orbital arc. Depending on the length of the maneuver and the number of available observations, different polynomial functions (up to degree 2) are used to model the thrust acceleration. Finally, the quality of the solution is evaluated internally by comparing it to the days without maneuvers and by checking the consistency between the reduced dynamic and kinematic orbit. External validation is also performed with respect to the official independent products.

How to cite: Kalarus, M., Arnold, D., Padovan, S., Dach, R., and Jäggi, A.: Precise orbit determination for the maneuvering satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16732, https://doi.org/10.5194/egusphere-egu24-16732, 2024.

The Galileo for Science Project (G4S_2.0) is funded by the Italian Space Agency and has several goals in the field of Fundamental Physics to be achieved by exploiting the satellites of the Galileo-FOC Constellation. In this regard, a key point is to obtain a suitable satellite orbit solution by performing an accurate Precise Orbit Determination (POD). To this purpose modeling in a reliable way the complex effects of the Non-Conservative Forces, i.e. of Non-Gravitational Perturbations (NGPs), is essential. The activities undertaken in the construction of a Box-Wing model and of a Finite Element Model of the satellite will be presented with the preliminary results obtained by including these models into the POD of the Galileo satellites. In particular, using the orbital element residuals obtained from a POD we can test our new models and the improvements in POD quality.

How to cite: Lefevre, C., Visco, M., Lucchesi, D., Sapio, F., Peron, R., Cinelli, M., Di Marco, A., Fiorenza, E., Loffredo, P., Lucente, M., Magnafico, C., Santoli, F., and Vespe, F.: The Galileo for Science project: Non-Conservative Forces modeling for the Galileo FOC satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16819, https://doi.org/10.5194/egusphere-egu24-16819, 2024.

Since the launch of Seasat (1978), the first satellite to study ocean topography, our knowledge of the rise of 
mean sea level has evolved. Since then, 18 additional satellites were launched, with more and more satellite 
missions (up to 10 satellites are now simultaneously flying) dedicated to the measurement of the global and 
regional sea-surface height, carrying on board state of the art precision orbit determination tracking techniques 
and instruments. 

Jason-3 (2016) and Sentinel-6 MF (2020) are part and parcel of these ocean topography missions. The two 
reference satellites were operated in tandem (with Sentinel-6 MF flying 30 seconds behind its predecessor) 
between mid-December 2020 to April 2022 for calibration purposes. The main difference between these two 
satellites has to do with their respective platform design. Indeed, Sentinel-6 MF solar panels are fixed on the 
satellite and has an almost fixed attitude, unlike Jason-3 which has some yaw steering periods. 

In this study, we focus on the solar radiation pressure modeling errors of both Sentinel-6 MF and Jason-3 
during their tandem phase (4.5 beta cycles). The idea is to analyze the estimated empirical accelerations of 
these two satellites as a function of their beta angle. The Solar Radiation Pressure (SRP) depends only on two 
parameters: the orbital angle with respect to the sub-solar point and the beta angle. We will then propose 
updates of the SRP models. The effect of the terrestrial radiative perturbations will also be assessed.

How to cite: Saquet, E., Cherrier, M., Couhert, A., and Mercier, F.: Assessment of Jason-3 and Sentinel-6 MF radiation pressure model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16831, https://doi.org/10.5194/egusphere-egu24-16831, 2024.

Within the IGS, it was agreed that Precise Point Positioning (PPP) based on satellite orbit and clock corrections of the IGS analysis centers allow a direct access to the IGS realization of the International Terrestrial Reference Frame (ITRF). This convention is considered convenient for all PPP users and should not be changed in future.

On the other hand, the groups determining the GNSS satellite orbits do prefer an origin of the frame that is related to the Earth instantaneous center of mass since this is the reference of the gravitational orbit force model. With this background, some discussions take place to change the convention related to the origin of the terrestrial frame because it is convenient for the orbit determination. This convention is, however, in contradiction to the expected needs of the PPP users that do prefer a stable coordinate origin in time.

We will introduce a strategy to serve both needs by applying the center of mass corrections for the orbit determination only.

How to cite: Dach, R., Arnold, D., Brockmann, E., Kalarus, M., Prange, L., Schaer, S., Stebler, P., and Jäggi, A.: Review the IGS Strategy for Precise Point Positioning Applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16959, https://doi.org/10.5194/egusphere-egu24-16959, 2024.

The Galileo for Science Project (G4S_2.0) is funded by the Italian Space Agency and has
several goals in the field of Fundamental Physics to be achieved by exploiting the satellites
of the Galileo-FOC Constellation and the accuracy of their onboard atomic clocks. In
particular, the clock-bias, estimated in the data reduction of the tracking observations
allows to place constraints on the possible presence of Dark Matter in our galaxy in the
form of Domain Walls (DW) eventually produced in the very early Universe by ultralight
scalar field(s). The impact of the DW on an atomic clock would provide a delta-like
transient shift on the pseudo-derivative of the clock-bias. Such signal depends both on the
nature of the clock and the characteristics of the ultralight scalar comprising the DW.
Ongoing work on the clock-bias will be introduced as regards to data pre-processing and
simulations on false alarm and detection efficiency.

How to cite: Di Marco, A., Sapio, F., Visco, M., Lucchesi, D., Cinelli, M., Fiorenza, E., Lefevre, C., Loffredo, P., Lucente, M., Magnafico, C., Peron, R., Santoli, F., and Vespe, F.: The Galileo for Science project: Constraints on Dark Matter with the Galileo-FOC Constellation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18289, https://doi.org/10.5194/egusphere-egu24-18289, 2024.

SWOT (Surface Water and Ocean Topography) is a recently launched international satellite mission developed in close collaboration between CNES and NASA space agencies aimed to reach a new level of accuracy in observing global water systems of the Earth. In order to meet the high accuracy requirements of the mission most precise determined orbits are crucial. Therefore we generate orbits using dynamic Precise Orbit Determination (POD) with multiple techniques, i.e. DORIS, GPS and SLR with the aim of generating a highly precise orbit with minimal residual error. For this purpose, single technique orbits are generated based on DORIS and GPS only, using SLR for validation only. For quality analysis we evaluate the estimated parameters of the dynamic POD and perform orbit comparisons to external orbit solutions. Subsequently, to reduce technique-specific effects of the dynamic modeling, we generate a combined orbit solution based on DORIS and GPS, and estimate the reference points of the observation techniques on the satellite to examine the calibration by the manufacturer. Based on the generated orbit solutions altimetry evaluation such as cross-over point analysis is made and discussed.

How to cite: Reinhold, A., Schreiner, P., and Schöne, T.: On multi technique precise orbit determination for SWOT with respect to altimetry applications., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18467, https://doi.org/10.5194/egusphere-egu24-18467, 2024.

Tidal variability originating from the orbital dynamics of the Sun and the Moon can be observed in virtually all subsystems of the Earth. The evoked tidal phenomena in the atmosphere, the solid Earth, and the world oceans cause a large-scale redistribution of masses, primarily on daily and sub-daily time scales. The implied tidal variability impacts geodetic measurements. For example, the induced mass transport induces temporal changes in the Earth's gravity field which impact the orbits of artificial satellites. However, observations of a single satellite are generally insufficient to precisely estimate tidal signatures, resulting in a decreased accuracy of the Precise Orbit Determination (POD) of near-Earth satellites. Therefore, a priori prediction of tidal signals, especially ocean tidal signatures, by tidal atlases is necessary to exploit the full potential of geodetic data sets.

The most accurate ocean tide atlases are produced by incorporating satellite altimetry observations into the modeling process. However, limitations arising from the ambient signal-to-noise level have hindered their ability to accurately estimate small signals associated with minor tidal constituents. For those minor constituents, data-unconstrained ocean tide models can yield valuable constraints. For processing satellite altimetry data, initial experiments have been undertaken to integrate empirical and numerical models, aiming to deliver comprehensive tidal corrections (Hart-Davis et al., 2021, doi: 10.3390/rs13163310). It has been proposed that experimentation is necessary across all geodetic applications to determine the preferred model for specific tidal constituents and the optimal approach for merging models. This also includes the possibility of including minor ocean tides only implicitly, by deriving their admittance function from suitable neighboring tidal constituents.

How to cite: Klemann, V., Sulzbach, R., Kehm, A., Blossfeld, M., Hart-Davis, M., Dobslaw, H., and Mayer-Guerr, T.: Treatment of Modern Global Ocean and Atmospheric Tide Atlases in Precise Orbit Determination, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19842, https://doi.org/10.5194/egusphere-egu24-19842, 2024.

The density of the upper atmosphere can be determined by orbit and accelerometer data from low Earth orbit satellites. Especially the accelerometers of geodetic satellites, measuring the non-gravitational accelerations acting on them are a very viable observation.

The density estimation is mainly based on three separate disciplines, which are: (1) Precise radiative non-gravitational force modelling, (3) Modelling of the interaction between the rarefied atmospheric gases and the satellite, i.e. modelling of drag coefficients, and (3) the calibration of the accelerometer data by dynamic Precise Orbit Determination (POD). This contribution focuses mainly on the last point. Nevertheless, we also validate the modelled radiative accelerations and use them as reference for the calibration results.

The accelerometers of all geodetic satellites are affected by a drifting bias and scale factors unequal to one. Therefore a calibration of the data is indispensable. Usually time dependent bias and scale factors are estimated. For standard POD or Gravity Field Recovery (GFR) these parameters are estimated together with empiric and other model parameters. In both cases, the estimated accelerometer calibration parameters are not of major interest, but improve the orbit fit or gravitational field coefficients. The used parametrizations and weighting strategies of the observation data, do not give realistic or physical accelerometer calibration results because parameters are not sensitive and effects are absorbed or smeared into other parameters and models. This is unsatisfying, especially for the anticipated use for the density determination.

In this contribution we use dynamic POD and investigate different parametrization strategies tailored for a physical accelerometer calibration. We investigated the effect of constraining the accelerometer calibration parameters in that way, that a continuous calibration over all arcs is achieved, where normally each arc is treated locally separated from all other arcs. The scale factor is concurrently estimated, but over a longer batch of arcs. We varied the length between one day and years. Furthermore, different calibration equations, different observation data and combinations (kinematic positions, science orbit data, inter-satellite ranging), weighting strategies, initial parameters and pre-processing of the accelerometer data is investigated. The validation of the results is not easy, because usual metrics like post-fit residuals do not reflect the quality of the accelerometer calibration. We introduce a validation approach using the modelled non-gravitational forces and also show the influence of the different calibration options on the resulting density or drag acceleration.

All results and data for the whole GRACE and GRACE-FO missions are available on our data server (link in presentation/ uploaded material). 

How to cite: Wöske, F., Rievers, B., and Huckfeldt, M.: Tailored accelerometer calibration by POD for thermospheric density computation with GRACE and GRACE-FO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20733, https://doi.org/10.5194/egusphere-egu24-20733, 2024.

The International Terrestrial Reference System is realised in the form of multi-year reference frames such as the International Terrestrial Reference Frame (ITRF) or in the form of epoch reference frames relying on short observation time spans up to several weeks. The realisation is based on the combination of space-geodetic techniques, namely the global navigation satellite systems (GNSS), satellite laser ranging (SLR), very long baseline interferometry (VLBI) and Doppler orbitography and radiopositioning integrated by satellite (DORIS). In some ITRF and epoch reference frame solutions, SLR and VLBI are responsible for realising the datum parameters origin (only by SLR) and the scale, while the orientation of the network with respect to the Earth’s body is maintained by a mathematical constraint. The integration of the techniques is achieved by introduction of local ties (LTs) at co-located sites, i.e., by ground-based measurements of difference vectors between the technique-specific reference points. High accuracy of current LTs between techniques and the establishment of new co-location sites are necessary to provide (and further improve) a reliable realisation of the geodetic datum. Co-location sites with the SLR technique are of particular significance as this is the only technique that enables the realisation of a terrestrial reference frame origin with a high level of accuracy. As previous studies demonstrate, the performance of the observational networks has a significant impact on the accuracy and stability of the corresponding datum realisation, especially for epoch reference frames.

This study aims to examine how improving the performance of the existing network of co-located SLR stations could affect the quality of determined datum parameters. The considered simulation scenarios study the performance of SLR stations co-located with the VLBI technique and improve the performance of those that do not meet the standards set by the International Laser Ranging Service (ILRS). Moreover, it is examined how significant the improvement of the datum parameters is in the case of extending the SLR network with stations located nearby existing VLBI telescopes (due to a ‘better’ datum transfer via a higher number of local ties).

How to cite: Najder, J., Kehm, A., Bloßfeld, M., Sośnica, K., and Glomsda, M.: Improved geodetic datum realization based on simulation studies for co-located SLR-VLBI stations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-897, https://doi.org/10.5194/egusphere-egu24-897, 2024.

The growing demand for Earth science applications poses challenges in improving geodetic reference frames. Systematic errors currently restrict the accuracy of these frames because the classical geometric ties between multiple geodetic techniques fall short of sufficiency. Our objective is to identify and analyze the impact of variable GNSS receiver hardware delays (incl. antenna-hardware delays) on carrier-phase time transfer with an accuracy of picoseconds/millimeters. We propose using a ground-based GNSS pseudolite system synchronized to an optical timing system (clock tie) developed at the Geodetic Observatory Wettzell to calibrate the variable hardware delays and facilitate a closure in time between multiple geodetic techniques.

This study analyzes the requirements for developing a GNSS pseudolite and its transmission chain. We reformulate the classic iono-free Precise Point Positioning (PPP) mathematical theory to incorporate pseudolite data, separating the known receiver clock error from unknown transceiver hardware delays. The analysis suggests a preference for highly directive and mechanically stable Right Hand Circularly Polarized (RHCP) log periodic or helix transmission antennae. Calibration for Phase Center Offset (PCO), Phase Center Variations (PCVs) and careful installation to minimize multipath are crucial. This results in a carrier-phase observation model with three unknowns: transceiver hardware delays (our focus), frequency-dependent ambiguity terms, and low tropospheric delay influence.

Utilizing a USRP-based transmission procedure, we successfully tracked an E1B Galileo signal replica with an in-house developed GNSS software-defined receiver (SDR). The transmission was implemented using two approaches: over-the-air and loopback. The over-the-air transmission was carefully planned using a link budget calculation to ensure that it did not exceed the allowed free-air transmission constraints. Empirical validation ensured a carrier-to-noise ratio (C/N0) below 30dB/Hz near critical public areas. In the loopback approach, the transmitted GNSS signal was fed into the local SDR within the pseudolite, sharing the same Analog-Digital-Converter (ADC)/ Digital-Analog-Converter (DAC)ADC/DAC, clock and local oscillators. In a future stage, this signal is supposed to be compared to a reference signal derived from the optical timing system. 

In our analysis, we also assessed the stability of the USRP frequency synthesizer, known as Phase Lock Loop (PLL), in the context of high-precision applications, such as real-time kinematic (RTK) positioning. We found that tuning the synthesizer in integer-n mode is crucial in maintaining a stable carrier frequency and achieving a 100% real-time kinematic positioning fixing rate. 

How to cite: Lăpădat, A. M., Kodet, J., and Pany, T.: Towards calibration of GNSS receiver hardware delays for improving geodetic reference systems through clock ties. A requirements analysis for developing a GNSS pseudolite transmission chain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1600, https://doi.org/10.5194/egusphere-egu24-1600, 2024.

Mission Genesis of the European Space Agency (ESA) has been approved for launch in 2027. Genesis will be the first satellite in orbit to have a dedicated Very Long Baseline Interferometry (VLBI) transmitter on board, next to Global Navigation Satellite System (GNSS) and Doppler Orbitography and Radiopositioning Integrated on Satellite (DORIS) receivers as well as a Satellite Laser Ranging (SLR) reflector; consequently, Genesis will realize a space tie combining all geometric space geodetic techniques. If perfectly calibrated, the space tie will enhance and improve local ties measured on the ground. The following scenario is possible: If the orbit of Genesis is determined from the satellite techniques alone, the station coordinates of the VLBI radio telescopes in the "satellite frame" can be derived by VLBI observations to Genesis, thereby assessing the tie with the "VLBI frame", realized with decades of VLBI observations to quasars.

We present our plans to devise observing strategies for VLBI to reach accuracies as defined in the Genesis white paper. We start with our findings for VLBI transmitters on Galileo satellites, before we show the simulation results for the VLBI transmitter on Genesis. We illustrate the advantages of the Genesis satellite at 6000 km altitude compared to Galileo satellites in terms of sky coverage and accuracy of station coordinates, but also in terms of orbit estimation. Furthermore, we provide an outlook on geodetic parameters, which could not be determined with VLBI so far but will be possible with Genesis.

How to cite: Böhm, J., Wolf, H., and Kern, L.: Benefits for the terrestrial reference frame with VLBI observations to Genesis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3821, https://doi.org/10.5194/egusphere-egu24-3821, 2024.

The Satellite Laser Ranging (SLR) technique is used to independently validate the microwave-based satellite orbit products. In the so-called SLR validation, the orbit quality is assessed based on the analysis of the SLR residuals, which are the discrepancies between the direct SLR range measurements and the station-satellite vector calculated based on the SLR station positions and the evaluated orbits in Earth-fixed reference frame. Therefore the results of SLR validation are strongly related to the SLR station coordinates. In 2022, the new realization of the International Terrestrial Reference Frame – ITRF2020 – has been released, which considers a few innovations, mainly, an extended model of post-seismic deformations, and the seasonal station coordinate variations in form of annual and semi-annual terms.  In this study, we investigate the impact of recent advancements in ITRF into the SLR-based orbit validation of LEO and GNSS satellites.      

We perform the SLR validation of LEO orbit (Swarm-ABC, Sentinel-3A/B, Jason-3) products provided by European Space Agency (ESA) Copernicus Service and Technical University of Graz for one year. Also, we validate Galileo and BeiDou-3 orbit products delivered by ESA and Center for Orbit Determination in Europe in 2023. 

We incorporate the latest ITRF2020 realization into the SLR validation processing, contrasting the outcomes with solutions that involve the previous ITRF2014 release to illustrate the impact of TRF aging on validation results. Additionally, we examine the influence of including seasonal station motions on SLR validation outcomes. Furthermore, a comparison is made between SLR validation results when utilizing the most recent alternative TRF realizations, namely DTRF2020 and JTRF2020. We discuss the dependency of residuals on different measurement conditions, such as elevation angle and azimuth angle, and their time variability.

How to cite: Strugarek, D. and Zajdel, R.: Impact of terrestrial reference frame on the SLR validation results of GNSS and LEO orbits, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4333, https://doi.org/10.5194/egusphere-egu24-4333, 2024.

The latest realizations of the ITRS, specifically the ITRF2020, the JTRF2020 and the DTRF2020, have been computed using input data series provided by the IAG technique services IVS, ILRS, IGS and IDS. They cover the entire observation period of the individual techniques until the end of 2020. Since 1996, recalculations of the ITRF have been performed approximately every 3 to 6 years. The main reason for recalculation is to ensure a high accuracy of the ITRF for current applications. In particular, seismic events that occure after an ITRF release as well as the general increase of the ITRF extrapolation error with time are key factors that cause the increase of the ITRF uncertainty.

To enhance the frequency of ITRS realizations and consequently improve the accuracy of the ITRF, the ITRS Product Center plans to calculate annual updates of the ITRF2020 starting in 2024. The IAG technique services will provide three additional years of analyzed observations (2021-2023) collected after the end of the ITRF2020 observation period in February 2024. As an ITRS Combination Center, at DGFI-TUM, we will analyze the data series w.r.t. discontinuities, post-seismic deformations and their consistency with the input data series provided for the ITRS 2020 realizations. Model changes performed in between by the individual technique services, e.g. new PCO (phase center offsets) for GNSS satellites, updated mean long-term range biases for SLR satellites or gravitational deformation models for some more VLBI antennas, are expected to have an impact on the relevant ITRF parameters (station coordinates, EOP and datum parameters). Its order of magnitude and the effect of possible inconsistencies on the DTRF solution need to be investigated. We will present the first results of our analyses and draw preliminary conclusions regarding the accuracy of a possible DTRF2020 extension.

How to cite: Seitz, M., Bloßfeld, M., Glomsda, M., Angermann, D., Rudenko, S., and Zeitlhöfler, J.: First results of the analysis of the input data series provided by the IAG technique services for the extension of xTRF2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5732, https://doi.org/10.5194/egusphere-egu24-5732, 2024.

LAser RElativity Satellite 2 (LARES-2) successfully joined the constellation of geodetic satellites tracked by Satellite Laser Ranging (SLR) stations on July 13, 2022. LARES-2 has a spherical shape and a very favorable area-to-mass ratio that minimizes the non-gravitational orbit perturbations. Due to very small retroreflectors, the spread of center-of-mass corrections for different detectors installed at SLR sites is much smaller than for LAGEOS satellites. LARES-2 orbits at a similar height as LAGEOS-1, however, with a complementary inclination angle of 70° forming a butterfly configuration together with LAGEOS-1.

Although the primary objective of LARES-2 is verification of the Lense-Thirring effect emerging from general relativity, the satellite also has a substantial impact on the geodetic parameters derived from SLR observations. We process 18 months of LARES-2 data and compare the LAGEOS-1/2 solutions with the combined LAGEOS-1/2+LARES-2 solutions. We show the impact of LARES-2 on the (1) SLR station coordinates, (2) pole coordinates, (3) length-of-day excess, (4) low-degree gravity field parameters focusing on C₂₀ and C₃₀ coefficients, (5) scale of the reference frame, (6) geocenter motion. We show that LARES-2 can especially improve the Z component of the geocenter coordinates and de-correlate C₂₀ from the length-of-day parameter. The secular drifts of the ascending nodes for LARES-1 and LAGEOS-1 caused by C₂₀ are the same in terms of absolute values but with opposite signs. This allows us to successfully separate the measurements of length-of-day excess (or the UT rate) from the C₂₀-induced changes. We also analyze the empirical accelerations acting on LARES-2 which result from unmodeled non-gravitational orbit perturbations, such as thermal effects, and compare them to those observed for LAGEOS satellites. The observation geometry of LARES-2 is especially beneficial for stations located at high and medium latitudes, which allows it to improve the estimation of station coordinates provided by LAGEOS-1/2. Therefore, LARES-2 substantially contributes not only to general relativity and fundamental physics but also to space geodesy improving the future realizations of the international terrestrial reference frames.

How to cite: Sośnica, K., Gałdyn, F., Najder, J., Zajdel, R., and Strugarek, D.: Contribution of LARES-2 to the realization of reference frames, deriving Earth rotation and gravity field parameters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6189, https://doi.org/10.5194/egusphere-egu24-6189, 2024.

Global Navigation Satellite Systems (GNSS) are based on measuring the time that elapses between the signal’s transmission at the satellite and its reception on the ground. Therefore, clock information is required on both sides. While the GNSS satellites are equipped with atomic clocks, ground stations usually use the time information from the internal oscillator of their GNSS receiver, which has a much lower time-keeping performance compared to the satellite clocks. Nevertheless, some continuously operated tracking stations obtain their time information from an external atomic clock, as it is the case with many stations of the International GNSS Service (IGS).

To compensate for synchronization errors, current GNSS analysis models generally introduce clock biases for satellites and receivers into the observation equations. The often-made assumption of a pure white noise behavior for the estimated clocks may lead to high correlations with other geodetic parameters, such as the radial orbit error for the satellite clock, or the station height coordinate and tropospheric delay parameters for the station clock. A general solution to this problem is to reduce the amount of unknown clock parameters by modeling them in the adjustment process. In order to be modeled adequately, the corresponding clock must have a high degree of stability, which is particularly crucial for the ground stations.

In this contribution, we investigate the clock stability of globally distributed IGS tracking stations. Those IGS stations, that are steered by an external Hydrogen-Maser (H-Maser) clock, are considered in a global network analysis over a period of several weeks. The generated clock products are used to compare the frequency stabilities within the station network, as well as with the mean behavior of GPS and Galileo satellite blocks. After some further research on stations with significantly higher deviations, the final result of this contribution will be a set of reliable ground stations, that will serve as a basis for future clock modeling approaches at GFZ.

How to cite: Widczisk, J. S., Männel, B., and Wickert, J.: Investigations into GNSS clock biases in a global network of IGS H-Maser stations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6472, https://doi.org/10.5194/egusphere-egu24-6472, 2024.

In recent years, new determinations of the ITRF based on full-blown reanalyses of frame inputs from the four space-geodetic techniques have been produced at intervals of 3-6 years. Between frame determinations, ITRF users must rely on predictions of station positions of the reference stations included in the frame whose accuracy rapidly degrades over time, thus causing errors in the products derived from such predictions.    
JTRF2020 is the most recent TRF solution computed at JPL by assimilating the frame input data submitted by  IGS, IVS, ILRS, and IDS for ITRF2020. Determined with a square-root information filter and Dyer-McReynolds smoother algorithm, JPL frame products lend themselves to being updated rather easily as long as frame inputs from the four technique centers consistent with the frame-defining data set are readily available. 
In this presentation, we will discuss and test SREF (Square-root Reference frame Estimation Filter) updating capabilities in relation to JTRF2020. We will upload state estimate and its covariance computed at the last step of JTRF2020, and update them by assimilating at daily intervals the extended frame inputs made available by IGS (Repro3 extension), IVS (BKG operational combined series with loading effects restored using loading information from the NASA GSFC solution), ILRS (v170 and v171), and IDS (wd20) from 2021 through the end of 2022.   
Discussions will focus on the peculiarities of the extended frame inputs in relation to the data submitted for the ITRF2020 computation, and in particular on the data pre-processing and transformations we’ve applied to the extended frame inputs in order to ensure consistency with JTRF2020. We’ll also assess the quality of the JTRF2020 updates in terms of frame-defining parameters.     

How to cite: Abbondanza, C., Chin, T. M., Gross, R. S., and Heflin, M. B.: Updating JTRF2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6698, https://doi.org/10.5194/egusphere-egu24-6698, 2024.

Having a Very Long Baseline Interferometry (VLBI) transmitter (VT) onboard Galileo satellite allows us to determine the misorientation between GNSS and VLBI frames. To exploit the maximum performance, we study the operational strategies for VLBI ground segment. We simulate VLBI observations of a VT onboard a Galileo satellite to evaluate the rotation transformation between the VLBI and GNSS frames. The contribution of a VT as space tie is assessed by the evaluation of the formal precision of the orientation parameters between the VLBI and GNSS frames using different ground stations/baselines, aiming to find the optimal observation geometry for the best precision on the rotation transformation.

How to cite: Sert, H., Hugentobler, U., Karatekin, O., and Dehant, V.: Effect of network geometry on determination of VLBI-GNSS frame orientation using a VLBI transmitter onboard Galileo satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8143, https://doi.org/10.5194/egusphere-egu24-8143, 2024.

The multipath phenomenon is one of the factors affecting the accuracy of GNSS positioning. It results from reflections of the satellite signal from objects in the vicinity of the GNSS antenna. There are groups of techniques that allow minimizing the impact of this error on positioning results. These include: antenna placement, the use of the appropriate type of antenna, the use of a professional receiver as well as proper post-processing of observations. However, it is impossible to completely eliminate the influence of multipath on the measurement results. In the case of carrier phase differential positioning, this error has two main effects. First of all, the multipath increases the initial search space for correct ambiguities. Consequently, the accuracy of the vector solution between the reference station and the rover receiver is affected. The authors of this article examined how the characteristics of the multipath error changed at the stations of the Polish network of ASG-EUPOS reference stations in 2010-2021. Two computational strategies were adopted to determine the multipath: Code Minus Carrier linear combination (CMC) and pseudorange multipath observable (MP). Based on the research, it was found how the multipath values changed depending on the change of the receiver and the terrain around the reference stations. It was determined which stations had high multipath values in 2010 and what changes occurred over the 11 years. Based on the carried out analyses, it was also recommended to perform periodic tests that would allow it to detect incorrect configuration or incorrect operation of receivers.

How to cite: Tomaszewski, D., Pelc-Mieczkowska, R., and Rapiński, J.: Changes in the multipath value at ASG-EUPOS GNSS reference network stations in 2010-2021, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8188, https://doi.org/10.5194/egusphere-egu24-8188, 2024.

Highly accurate Terrestrial Reference Frames (TRF) – based on the combination of the four space-geodetic techniques Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI), Global Navigation Satellite Systems (GNSS) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) – are the fundamental backbone for a broad range of applications like land surveying, the geodetic monitoring of geophysical processes within the Earth system or navigation on and near the Earth’s surface. Recent efforts at the Geodetic Observatory Wettzell (GOW), Germany, aim at a transition from the purely geometric link between space-geodetic techniques via local ties as the current standard to an innovative quasi-error-free combination based on a common clock (CC) and a common target (CT).

Once the CC/CT-based infrastructure at GOW is fully developed and operational, it will be possible to uncover systematics between the space-geodetic techniques as well as individual instruments. However, to guarantee the long-term accuracy and stability of the TRF, it is indispensable to know and, if possible, to eliminate the systematics over the entire observation period of the techniques. A prerequisite for this is to compile an inventory of the existing discrepancies between the techniques and their possible causes.

The DFG research unit ‘Clock Metrology: Time as a New Variable in Geodesy’ features a joint project by DGFI-TUM and Uni Bonn with focus on developing a new CC-/CT-based approach to combine the space-geodetic techniques. As a basis, we develop an approach to analyse and cross-compare station position time series from different instruments/techniques observed over several decades. Based on the example of GOW co-locating all four space-geodetic techniques, we investigate absolute station position time series consistently aligned to the datum of the DTRF2020, DGFI-TUM’s most-recent realisation of the International Terrestrial Reference System (ITRS), as well as differential time series eliminating datum-realisation-related variations in the time series of one technique. Finally, we prepare a pool of metadata (log files, data time series from meteorological sensors and weather models, estimated clock and tropospheric parameters, etc.) and include these data in the analysis to identify causes of systematics. 

From the analyses, discontinuities, time-variable drifts and the spectra of intra- and inter-technique position difference time series between individual instruments at GOW can be identified and interpreted. The result of the work is an inventory which lists both, known and previously unmodelled systematics, and, as far as possible, their causes, thus providing the basis for the consistent combination of techniques in a common space-time.

How to cite: Kehm, A., Seitz, M., and Glaser, S.: Analysis of long time series of space-geodetic techniques at co-location sites to identify technique- and instrument-specific systematics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8612, https://doi.org/10.5194/egusphere-egu24-8612, 2024.

Geodesy provides the highest precision and accuracy International Terrestrial Reference Frame, International Celestial Reference Frame and Earth Orientation Parameters. However, in our processing chain, we take mathematical shortcuts, drop higher order polynomials, assume linearity where it is no longer valid, omit correlations and colored noise, and use outdated models. If intra- or inter-technique combinations are done, they happen at different stages, and different methods are employed. The datums applied to the reference frames are inherited over decades, accumulating all uncertainties of their predecessors. Dependencies between the reference frames and the EOP are largely ignored. Finally we inflate our errors by a predefined factor, to somehow account for all of that.
This is just a short list of the inconsistencies within our main products. Even for a specialist it will be almost impossible to list them all, for a mere end-user its an insurmountable task. In this work we will investigate these central products of geodesy, focusing mainly on the errors, their derivation, and significance from a user perspective. We look exemplarily at various official IVS and IAG products, and their reported errors. We investigate how transparent their nature and derivation is for the final user, if the parameters in question follow our physical understanding of the matter, and what insight we might gain from them.

How to cite: Karbon, M., Belda, S., Acuze, E., Moreira, M., Escapa, A., and Ferrándiz, J. M.: A critical look at the reported errors of geodetic products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8737, https://doi.org/10.5194/egusphere-egu24-8737, 2024.

The techniques of space geodesy, comprising the four techniques, Global Navigation Satellite Systems (GNSS), Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR) and Doppler Orbitography and Ranging Integrated by Satellite (DORIS) are currently reaching a measurement resolution in the range of 1 part per billion for the terrestrial reference frame. However, a small set of discrepancies remain evident within each of the techniques as well as in the combination of different techniques. Systematic measurement errors are causing this and problems in the local ties between the reference points of the various measurement systems and biases in the atmospheric refraction correction have long been suspected as the main contributors. 

However, it turns out that errors in the internal delay compensation of the measurement systems are also a significant contributor. They are extremely hard to detect, since they are small and come with different characteristics. It is understood that the experienced delay variation is related to a complex pattern of ambient temperature variation inside of the electronic devices. These changes relate to the micro-climate of the electronic signal path and can both be slow and highly variable. With the advent of high bandwidth mode-locked lasers and active delay compensation in the optical domain, it is now possible to utilize coherent time as an independent probe for instrumental signal delays. 

The research unit FOR5456 of the German National Science Foundation (DFG) has been formed in 2022 in order to apply and investigate active delay compensation to the techniques of space geodesy. This talk introduces the application of coherent time in space geodesy and its potential to act as a novel tie in fundamental stations.

How to cite: Schreiber, K. U.: Clock Ties: A novel approach for the reduction of systematic errors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9298, https://doi.org/10.5194/egusphere-egu24-9298, 2024.

How to cite: Kierulf, H. P.: Glacial induced variations in the uplift – a challenge for the reference frame , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9527, https://doi.org/10.5194/egusphere-egu24-9527, 2024.

The ITRF2020 marked considerable innovations compared with previous versions of the ITRF, by modeling nonlinear station motions (seasonal signals and Post-Seismic Deformation –PSD– for sites subject to major earthquakes). It also confirmed the stability of the CM-based frame origin, as sensed by SLR, at the level of or better than 5 mm and 0.5 mm/yr. For the first time in the ITRF history, the scale agreement between SLR and VLBI solutions submitted to ITRF2020 is at the level of 0.15 ppb (1 mm at the equator) at epoch 2015.0, with no drift. Motivated by these results, and for a number of reasons that will be exposed in this paper, the ITRS Center decided to regularly (yearly) update the ITRF2020, with a first update expected to be released around mid-2024. Depending on the input data availability that will be submitted by the four technique services, we expect some preliminary results that will be discussed in this presentation. Emphasis will be given to answer some critical questions, such as: how does the VLBI scale drift evolve after 2021.0? Do the SLR and VLBI scales remain in agreement? Does the SLR origin drift or not from the ITRF2020 origin after 2021.0?

How to cite: Altamimi, Z., Rebischung, P., Collilieux, X., Métivier, L., and Chanard, K.: ITRF2020 Updates : motivation and expectation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10560, https://doi.org/10.5194/egusphere-egu24-10560, 2024.

The observations of the four space-geodetic techniques (GNSS, SLR, VLBI, DORIS) are used for the computation of the International Terrestrial Reference Frame (ITRF) and for the realisation of its geodetic datum as well as (except VLBI) for the precise orbit determination of Earth-observing satellites. The ITRF itself is fundamental for a broad variety of scientific and societal applications such as Earth monitoring and positioning, relevant for satellite companies, logistics, and finally new techniques like autonomous driving. However, all four techniques still suffer from unknown systematic measurement or modelling errors which makes the estimation of bias parameters inevitable.

The Geodetic Observatory Wettzell (GOW), Germany, with its unique and ideal test environment comprising multiple GNSS antennas, three VLBI antennas, two SLR telescopes, a DORIS beacon, and numerous other sensor systems provides the opportunity to systematically identify, quantify, understand, and compensate system-specific measurement errors. The installation of a so-called common target (CT) in 2017 and the realisation of a common clock (CC) enables a profound analysis of space-geodetic measurements. The CT is connected to the clocks of the space-geodetic techniques via delay-compensated fibre links which allows so-called ‘closure in time’ experiments.

Within the recently established DFG research unit ‘Clock Metrology: Time as a New Variable in Geodesy’, one project at DGFI-TUM focuses on the investigation of time-related and technique-specific errors using ‘closure in time’ experiments at the GOW. Therefore, completed experiments involving the CT are analysed and enhanced experiments will be planned and conducted. With the aid of the CC, the source of present measurement discrepancies will be investigated and resolved. We present the current state of our work with the main focus on the analysis of VLBI and SLR observations of the two SLR and three VLBI systems at GOW.

How to cite: Zeitlhöfler, J., Bloßfeld, M., Neidhardt, A., and Eckl, J.: Identification of system-specific observation errors for SLR and VLBI telescopes at GOW, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10691, https://doi.org/10.5194/egusphere-egu24-10691, 2024.

Recent advances in SLR data analysis are described in ‘The ILRS contribution to ITRF2020’, Pavlis and Luceri (2022). Improved vertical resolution can now complement the more easily resolved horizontal motion.
We examine the emerging results from ITRF2020 and prioritize the most accurate geodetic products.
Height variations at SLR stations in tectonically stable regions of Australia and North America exhibit steady and consistent height rates. 
Robust long-term vertical motion models also enable precise monitoring of behavior at higher frequencies: annual, tidal, and diurnal. Tracking operation procedures have been implemented to reduce and monitor ranging measurement accuracy. Comparisons of the vertical signals in SLR, VLBI, DORIS and GNSS systems allow robust accuracy monitoring at co-located stations.
Data handling techniques are outlined to enhance the isolation of the geodetic signals and enable their application to Earth and Ocean Model development 

How to cite: Dunn, P., Husson, V., and Szwec, C.: Accurate Long Term Height Determination at SLR Stations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11287, https://doi.org/10.5194/egusphere-egu24-11287, 2024.

The future Atomic Clock Ensemble in Space (ACES) mission of the European Space Agency (ESA) will address the development of a time transfer concepts for tomorrow's technologies. The ACES configuration includes a new generation of high-precision atomic clocks, a microwave link terminal (MWL) on the ground and on the satellite, and an optical detector and reflector also on the satellite. Due to the fact, that the official launch date is in 2025, there is a lack of real observation data. For that, a full-scale simulation software has been implemented. The simulator produces MWL code and phase observations in downlink and uplink, as well as one- and two-way laser observations. To analyse the efficiency of a time transfer concept before launch, we used the simulator to generate a data set of 100 passes during July 2021.
Investigations based on this data set showed that the colocation of the high-precision geodetic observation techniques of the ACES mission could better separate the individual error contributions of a measurement. Due to the colocation of optical and microwave-based geodetic observation techniques, also error parameters like orbit and troposphere correction can be estimated together. Estimation of a common troposphere for all observation techniques, improves the accuracy of the determination of the offset between ground and ACES clocks. Our further investigations focus on the common troposphere estimation of multi-color optical observations, together with microwave-based observations and the effects of different weighting methods. An extension to a network of ground stations will demonstrate the advantages of the ACES mission for synchronizing multiple ground clocks. The colocation of different high-precision geodetic observation techniques and estimation of common parameters will benefit timing and ranging applications and fundamental physics studies.

How to cite: Vollmair, P., Schlicht, A., and Hugentobler, U.: Simulating the Colocation of High-Precision Microwave and Optical Techniques for Tropospheric Parameter Determination in Context of the ACES Mission., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11608, https://doi.org/10.5194/egusphere-egu24-11608, 2024.

Canada and the US are collaboratively implementing a new dynamic, geometric reference frame for North America (NA) known as the North American Terrestrial Reference Frame of 2022 (NATRF2022). It will be a plate-fixed reference frame based on ITRF2020/IGS20 that is kept aligned to the NA tectonic plate using an Euler pole rotation. We have estimated Euler pole parameters (EPP) for NA based on the spherical model of Earth using different sets of stations and compared our results to other sources, including those recently obtained by Kreemer (2023) under contract to the U.S. National Geodetic Survey. The velocity field used for our analyses are those from Kreemer (2023) that were obtained for 4274 stations using GipsyX precise point positioning in the IGb14 reference frame and corrected for non-tidal and atmospheric loading as well as hydrological loading obtained from GRACE. A challenge for our analyses is the impact of ongoing glacial isostatic adjustment (GIA) on the horizontal velocities which can bias the EPP estimation. To mitigate such biases, we have used the horizontal component of the ICE-6G model to remove the GIA effect from the velocity field. Following Kreemer (2023),  we have determined a small set of homogeneously distributed stations that closely reproduce Kreemer’s EPP estimates. We also considered that users of the new reference frame would prefer that the intra-plate motions be minimized across the entire continent for conventional use and have therefore computed the best fitting EPP that minimizes the overall intra-plate motions across the entire continent.  In addition, we chose only good stations that met certain statistical criteria for the residuals and had stable monumentation, preferably anchored to bedrock. Finally, we compare the EPP estimates for all these sets of stations with and without GIA removed using statistical tests and descriptive statistics. The differences in resulting intra-plate velocities across the continent are also discussed for each test.

How to cite: Goudarzi, M. A. and Craymer, M.: Estimating Euler pole parameters for NATRF2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13398, https://doi.org/10.5194/egusphere-egu24-13398, 2024.

We describe the development and assessment of a new terrestrial reference frame (TRF) based on combining GPS, SLR and VLBI at the observation level over the period 2010–2022. Included in the solution, in addition to station coordinates and precise orbit solutions for all participating satellites, are Earth orientation parameters (EOP) and low-degree zonal coefficients (J2 and J3) of the geopotential. The overall solution concept grew from earlier efforts to realize a TRF using GPS data alone, capitalizing on GPS receivers on the ground and in low-Earth orbit (LEO). Here we add observations from both the SLR and VLBI techniques, which provide the foundation for traditional realizations of the TRF.

In linking the GPS and SLR techniques, our approach dispenses with traditional ground survey ties, relying exclusively on space ties from the GRACE and Jason LEO missions. In addition to SLR from these satellites, we include observations from the dedicated LAGEOS satellites, which prove particularly important for recovering low-degree gravity. A major evolution of our approach is the addition of VLBI at the observation level. Lacking a robust tie in Earth orbit for VLBI observations, we apply as constraints the published ground survey ties to nearby GPS stations, enforcing inclusion of the corresponding tracking data in the solutions. The VLBI effort is in the exploratory phase, and further tuning of the strategy is needed to better exploit collocations with both GPS and SLR. About 40% of the participating solution arcs (spanning 2010–2022) now include VLBI and support accurate recovery of UT1 as part of the EOP solution.

Though the resulting TRF solution is based on only 12.6 years of data, it is competitive with ITRF2020 in terms of fundamental frame parameters (origin and scale) and their temporal evolution, both linear and seasonal. The relative rates of origin (3D) and scale (at Earth's surface) are 0.2 mm yr^-1 and 0.1 mm yr^-1 respectively. Absolute scale (at epoch 2015.0) and 3D origin both differ by 2 mm. One advantage of our technique is that precise orbit solutions for both GRACE and Jason missions, defined in the realized TRF, are byproducts of the overall solution. We use the Jason orbit solutions to characterize the impact of contemporary TRF errors on sea level variations (both global and regional) and discuss the implications of these results.

How to cite: Haines, B., Bertiger, W., Desai, S., Elmer, M., Heflin, M., Kuang, D., Lanyi, G., Miller, M., Naudet, C., Peidou, A., Ries, P., Tolstov, A., Wu, X., and Zivkov, N.: A New Realization of the Terrestrial Reference Frame: Combining GPS, SLR and VLBI at the Observation Level from 2010–2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13524, https://doi.org/10.5194/egusphere-egu24-13524, 2024.

Multi-station (or time series) stacking is a fundamental task in the realization of a terrestrial reference frame (TRF), which is, however, rank-deficient in nature due to the arbitrarily selected target frame. In practice, such a model is approximated as a linear form and the classical free network theory is applied. It is known that the one-step adjustment only works in cases where the nonlinearity (measured by curvature) is moderate, and the initial point is very good; for TRF, it requires the deformable networks with a small enough time span for the network shape to be nearly unaltered. However, these assumptions can be nullified for the cases such as large time span and the integration of some local survey results. To address these limitations, we propose to solve the geodetically meaningful and numerically exact least-squares (LS) solution for the multi-station stacking model. The contributions are summarized as follows:

• The original nonlinear LS objective for the multi-station stacking model is investigated and its characteristics are analyzed;
• The nonlinear Baarda’s S-transformation is formulated for such a problem, which transforms different LS solutions that share the same network configuration;
• Two ways to obtain the geodetically meaningful solution are proposed, i.e., the minimally-constrained solution and the nearest-solution, where the latter originates from the inner-constraint solution in the linear case.
• The iterative schemes to obtain the two types of solutions are derived, which are shown to be essentially the truncated Gauss-Newton method. In addition, some techniques such as finite differences are employed to enhance numerical stability.

The developed theory is verified by the real examples.

How to cite: Hu, Y., Fang, X., and Zeng, W.: Nonlinear least-squares solution for the multi-station stacking problem in realizing a terrestrial reference frame, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14503, https://doi.org/10.5194/egusphere-egu24-14503, 2024.

In anticipation of the first update of the 2020 realization of the International Terrestrial Reference Frame (ITRF2020), the International DORIS Service (IDS) Combination Center is participating in the estimation of DORIS stations positions/velocities as well as Earth Orientation Parameters (EOPs), using DORIS data. These computations are based on the latest weekly multi-satellite series from all four IDS Analysis Centers and two IDS Associated Analysis Centers, from January 2021 to December 2023.

The primary objectives of this study are to analyze the DORIS contribution to this first update of the ITRF2020 in terms of: (1) geocenter and scale solutions, (2) station positions and week-to-week repeatability, and (3) Earth Orientation Parameters (EOPs).

Comparisons with the IDS 19 series time extension (contributing to ITRF2020) will highlight the benefits of the new models, including the latest DORIS missions (e.g. HY-2C, HY-2D, Sentinel-6A MF), and the addition of two new IDS contributors. Additionally, this study will assess the impact of new strategies designed to mitigate perturbations caused by the South Atlantic Anomaly (SAA) on certain DORIS missions.

How to cite: Moreaux, G., Lemoine, F., Capdeville, H., Štěpánek, P., Otten, M., Nahmani, S., Pollet, A., and Schreiner, P.: IDS contribution to the first update of the ITRF2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14591, https://doi.org/10.5194/egusphere-egu24-14591, 2024.

The new ITRF20 includes a geocenter motion (GCM) model and seasonal station corrections, both with annual and semi-annual periods. The GCM model is recommended to be applied when processing space-geodetic observation data and the seasonal station corrections should be applied if no non-tidal modelling is applied for the station coordinates. In the case of the Copernicus Precise Orbit Determination (CPOD) Service this applies to the processing of GNSS data of the Sentinel satellites for precise orbit determination (POD) and to the processing of Satellite Laser Ranging (SLR) tracking data for validation of the estimated Sentinel orbits.

The CPOD Service delivers, as part of the Ground Segment of the Copernicus Sentinel-1, -2, -3, and -6 missions, orbital products and auxiliary data files for their use in the corresponding Payload Data Ground Segment (PDGS) processing chains at ESA and EUMETSAT, and to external users through the newly available Copernicus Data Space Ecosystem (https://dataspace.copernicus.eu/). It generates routinely several types of orbital products for Sentinel-1, -2, -3 and -6: predictions, near-real time (< 10 min), short-time critical (< 1.5 days) and non-time critical (< 25 days).

The POD quality control within the service is based on comparing the CPOD orbit products to orbit solutions provided by members of the accompanying Copernicus POD Quality Working Group (QWG). A combined orbit generated by CPOD from all available orbit solutions serves as reference for the comparisons. The new ITRF20 modelling opens the door to different Centre-of-Network (CoN) or Centre-of-Mass (CoM) frame realisations, so in order to avoid inconsistencies between the solutions when doing the combination and comparisons, special care must be taken.

This study aims to analyse the impact of the new ITRF20 GCM model and the seasonal station corrections on the CPOD Service products. Sentinel orbit comparisons and the corresponding processing metrics are analysed when applying the GCM model or not, with a focus on the geocenter motion modelling used by the different CPOD QWG centres.

In the SLR validation, station range biases are routinely estimated as part of the residuals analysis. Preliminary results reveal that the estimates of these range biases show smaller seasonal variations when applying the seasonal station corrections. Detailed analyses will be shown and discussed.

How to cite: Muñoz de la Torre, M. A., Fernández, C., Fernández, M., Peter, H., Féménias, P., and Nogueira Lodd, C.: Copernicus POD Service – Impact of ITRF2020 Modelling Changes on Orbit and Validation Results, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16188, https://doi.org/10.5194/egusphere-egu24-16188, 2024.

How to cite: Matonti, F., Miller, A., and Wnuk, J.: ITRF2020 usage and maintenance in a global dense network for commercial applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16881, https://doi.org/10.5194/egusphere-egu24-16881, 2024.

With the establishment of the International Terrestrial Reference Frame 2020 (ITRF2020), investigations revealed an unexpected positive VLBI scale drift after about 2014.0. Given the crucial role of Very Long Baseline Interferometry (VLBI) in determining the ITRF scale, this peculiar behavior raises concerns. Within the VLBI community, several studies have been conducted to decipher the cause behind this pattern. A recent study by the Onsala Space Observatory (OSO) explored the introduction of additional discontinuities in the station positions of NYALES20 and/or in the positions of MATERA, WETTZELL, and ONSALA60 due to repairs or replacements. They found that the introduction significantly mitigated the scale drift with respect to ITRF2020.

Utilizing our newest state-of-the-art combination software, VieCompy, developed at the Vienna Center for VLBI, we independently assess the impact of these additional breaks on session-wise estimated scale through a combination of VLBI sessions at the normal equation level.

How to cite: Kern, L., Krasna, H., Böhm, J., and Nothnagel, A.: Verifying the impact of additional breaks in station coordinates on VLBI scale drift, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16905, https://doi.org/10.5194/egusphere-egu24-16905, 2024.

We present a project of the research unit (RU) 'TIME' (Clock Metrology: A Novel Approach to TIME in Geodesy) which aims to determine gravity potential or height differences between remote sites by comparing optical clocks. A strontium optical lattice clock at the Physikalisch-Technische Bundesanstalt (PTB) in Braunschweig will be connected with the German Research Centre for Geosciences (GFZ) in Potsdam through a delay-compensated optical fiber. Then, optical time transfer is carried out between the geodetic observatories in Potsdam and Wettzell (where a second optical clock will be operated) via the Atomic Clock Ensemble in Space (ACES) using the Satellite Laser Ranging (SLR) telescopes. The key innovation is using time transfer, not frequency and optical free-space links over an extended period to determine physical height differences. Challenges include clock/link variations, atmospheric effects, visibility constraints and data gaps, etc. We investigate the major error sources and apply corrections like tidal effects. This approach showcases accurately transferring physical heights via time transfer and demonstrates the RU’s time concept for integrating geometric and physical heights in future height systems, especially for Global Geodetic Observing System (GGOS) core stations like the Geodetic Observatory Wettzell (GOW).

In this presentation, we introduce the principles, special properties and challenges of this specific measurement scenario. We also provide preliminary numbers for the expected accuracies of the various components and the resulting height difference based on simulations.

We acknowledge the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 490990195 – FOR 5456.

How to cite: Lachmann, K. E. and Müller, J.: Determination of physical heights via time transfer , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17616, https://doi.org/10.5194/egusphere-egu24-17616, 2024.

Achieving a high-precision geodetic spatial reference depends on a thorough understanding of the equipment-specific sources of error of phase centre corrections (PCCs) of Global Navigation Satellite System (GNSS) receiver antennas. GNSS station operators and network analysers are constantly challenged regarding consistent PCCs, such as in the latest IGS Repo3 project. The challenges are, on the one hand, that not for all antennas in the network multi-GNSS calibrations are available. On the other hand, not all antennas are individually calibrated, so that type mean combinations with individual PCCs have to be used.

Even small differences between PCCs can significantly affect position accuracy, troposphere modelling, and GNSS time and frequency transfer. Such deviations manifest differently depending on used hardware, software, and data processing approach. A generalised and easily accessible benchmark for assessing the quality of PCCs remains difficult to find. There is a lack of easy-to-apply and common quality assessments of PCCs when comparing individual calibrations versus a type mean and results from the various calibration facilities and calibration methods among each other.

In response to this challenge, a global initiative involving nine calibration organisations has launched a comprehensive ring calibration campaign. By sharing six constructionally different antenna samples for calibration and presenting the subsequent results, this collaborative effort aims to enhance (1) the consistency of calibration methods and facilities, (2) develop a validation strategy, and (3) provide insights into the stability of receiver antenna calibrations.

This contribution provides an overview of the current status of this campaign, initiated one and a half years ago, outlines the calibration and evaluation concept for carrier phase patterns. First initial results from consulting contributors are presented and the roadmap towards a standardised, robust quality assessment framework for PCCs will be covered.

How to cite: Kersten, T., Bilich, A., Sutyagin, I., and Schön, S.: A Global Collaboration to Enhance GNSS Receiver Antenna Calibration: The IGS Antenna Ring Campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17665, https://doi.org/10.5194/egusphere-egu24-17665, 2024.

Determining time series of bedrock motion from global navigation satellite systems (GNSS) in Antarctica is one method to investigate geodynamical processes (e. g. glacial isostatic adjustment (GIA)). To achieve coordinate time series with the highest possible precision, a stable realization of the International Terrestrial Reference Frame (ITRF) is a crucial precondition. Regional networks of GNSS stations have the advantage of exhibiting small common mode errors and allowing to infer accurate velocity estimates. However, to achieve high consistency, it is necessary to base the analysis on a global network of International GNSS Service (IGS) stations, constraining them to their ITRF positions. Various approaches can be found in the literature on how the global solution is constrained to the ITRF and how the regional solution is transformed to the global solution. These approaches have different effects on the resulting time series and inferred parameters, e. g. absolute coordinates, linear trends, and noise properties. In this study, approximately 30 Antarctic GNSS stations are processed together with a global GNSS network of overall 200 IGS stations. We investigate different approaches to realize the ITRF and discuss the inferred results. To be consistent w. r. t. the general processing, we use a consistent set of GNSS observation data, GNSS products (e. g. orbit corrections), and apply the Bernese GNSS Software v5.4. In this way, the residuals between the different coordinate time series can be assumed to be due to the differences in reference frame realization. In our time series analysis, we put particular emphasis on linear trends because these are most important for GIA studies.

How to cite: Heidrich-Meisner, K., Buchta, E., and Scheinert, M.: GNSS for geodynamics in Antarctica: Sensitivity to reference frame realizations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19608, https://doi.org/10.5194/egusphere-egu24-19608, 2024.

The contribution of the International Laser Ranging Service (ILRS) to the most recent realization of the International Terrestrial Reference System (ITRS) was the result of an analysis strategy with two major modifications compared to the operational products: a modeling for stations long-term systematic errors (biases reported in the ILRS Data Handling File) and an updated model of the target signature error (satellite Centre of Mass model). Both refined models were used as input information for the ILRS contribution to the ITRF2020 (International Terrestrial Reference Frame 2020).

Thereafter, the ILRS Analysis Standing Committee (ASC) focused its effort on implementing the new reference frame in its operational products, define a strategy to improve the ongoing monitoring of the systematic errors, compute the ILRS contribution to the planned ITRF2020 update, and to include LARES-2 among the considered satellites for the operational products.

The ILRS ASC implemented the ITRF2020/SLRF2020 into all its official operational products (TRF, Earth Orientation Parameters, predicted and combined satellite orbits) and its impact was evaluated. The operational products benefit from the continuous monitoring of the station systematic errors and the frequent updates of the Data Handling File whenever a significant change in the station systematic error is observed. In the future, a change-point detection algorithm, jointly estimating the times and the number of discontinuities, will be implemented to detect potential new discontinuities in the range bias series.

The inclusion of LARES-2 among the satellites whose data are operationally analyzed will furtherly increase the robustness of the estimated parameters. Finally, the ILRS ASC activities include the benchmarking of a new analysis center (CNES) which will formally begin its own contribution in 2024.

How to cite: Sarrocco, D., Luceri, C., Basoni, A., Bloßfeld, M., Evans, K., Kuzmicz-Cieslak, M., Lemoine, F., and Bianco, G.: ILRS analysis activities after the adoption of ITRF2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19991, https://doi.org/10.5194/egusphere-egu24-19991, 2024.

Through a parametric fit to daily vertical displacement time series from European Permanent GNSS stations, we conducted a statistical sensitivity analysis focusing on Vertical Land Motion (VLM) – specifically, station velocity (linear trend). We compared two independent corrections to raw observed displacements: non-tidal atmospheric, oceanic, and hydrological loading displacements, as well as a correction for common mode errors (CME). Our methodology involved selecting the most realistic stochastic models based on information criteria, analyzing GNSS-observed displacements and identifying discrepancies with loading model predictions. We also employed restricted maximum likelihood estimation (RMLE) to mitigate low-frequency noise biases, enhancing the reliability of velocity uncertainty estimates.

Our results demonstrate that 1) an autoregressive, power-law, and white noise model combination is preferred for uncorrected GNSS VLM data, 2) when compared to the corrected cases, this model choice yields lower improvement rates in trend sensitivity than previously reported, and 3) RMLE reveals that for many stations, noise is optimally modeled by a combination of random-walk, flicker-noise, and white noise. We report median trend sensitivity and detection rates of about 0.5 mm/year (with best results for the CME-corrected case), approaching the GGOS goal of a 0.1 mm/year precision, crucial for sea level studies and other applications.

How to cite: Hohensinn, R. and Bock, Y.: Gauging the Sensitivity of GNSS for Resolving Vertical Land Motion Over Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20115, https://doi.org/10.5194/egusphere-egu24-20115, 2024.

Based on approximately 15,000 global station coordinates and velocity values, the plate model PB2002_CASM was constructed using actual measured velocity field analysis and supervised clustering analysis based on global PB2002 plate boundary divisions. For plates with more stations, mathematical interpolation algorithms were applied to calculate grid velocity values, including inverse distance weighting, Euler vector method, finite element interpolation method, least squares configuration method, kriging interpolation method, and linear interpolation based on triangulation. For plates with sparse or missing stations, such as in the ocean, singular spectrum analysis was used to extract trend components and obtain station motion speeds, and the PB2002_CASM plate motion model was used to calculate grid velocity values. There are a total of 61,560 1°×1° grids within the global longitude range of -179° to 180° and latitude range of -85° to 85°, which were mathematically interpolated to form 20,071 grid points. The PB2002_CASM plate motion model was used to calculate the velocity of 40,624 1°×1° grid points. The accuracy of the calculated velocity was validated, with a deviation within 1mm, achieving 82% and 89% accuracy in the E and N directions, respectively.

How to cite: Xu, Y.: Global digital plate model PB2002_CASM and 1°×1° grid velocity field construction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20392, https://doi.org/10.5194/egusphere-egu24-20392, 2024.

Since Gauss (1828), the theoretical shape of the Earth has been defined as a distinct potential surface of the gravity field that best fits the sea level at rest. The gravity field is described as a sum of spherical function series whose coefficients are calculated from satellite and in situ gravity measurements. Sea levels have been measured in harbours since the 18th century using mareographs, and are now often supplemented by data from nearby GNSS stations.

In our work, we developed a program to determine the potential value for a given height from mareograph data. This allows us to use the data available from the stations to determine what this value was in an earlier period as well. Furthermore, if more than one dataset is available for a particular sea segment, the average value for that sea over a certain period can be given.

We tested the method in the Japanese Sea, where we had data from 9 stations. These were averaged every 5 years and the resulting data were used to calculate the potential values, as well as the average value for the sea segment for the latest period 2016-2020. In the future, we plan to use this method to perform such calculations for more sites, which would also provide information on the effects of tectonics and global sea level change.

Gauss, C. F. (1828) Bestimmung des Breitenunterschiedes zwischen den Sternwarten von Göttingen und Altona. Vandenhoeck und Ruprecht, Göttingen, 84 p.

Supported by the ÚNKP-23-1 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

How to cite: Cziraki, K. and Timár, G.: Estimation of the potential value of given sea segments for certain time periods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-584, https://doi.org/10.5194/egusphere-egu24-584, 2024.

The services of the International Association of Geodesy (IAG) provide very important and valuable geodetic data, information, and data products that are increasingly relevant for Earth system research, including monitoring of global change phenomena and a wide range of diverse applications such as satellite navigation, surveying, mapping, engineering, geospatial information systems, and so on.

Currently, it is difficult for many people to obtain an overview of all available geodetic products and data. The Global Geodetic Observing System (GGOS) of the IAG aims to fill this gap by developing the GGOS-Portal (ggos.org/portal), which will serve as a unique search and access point (one-stop shop) for geodetic data and products. Data and products will be described by rich metadata and remain physically located at their originating data centers of each contributing IAG service and other data providers. With this future platform, GGOS will contribute to increase the visibility of geodetic data for scientific research and to make other disciplines and the society aware of geodesy and its beneficial products.

Several software packages are available for the realization of such a metadata platform. Two of them are particularly suitable for this purpose: GeoNetwork and CKAN. Based on the community survey conducted in spring 2023, a requirements profile for the GGOS portal was drawn up as part of a feasibility study and compared with the tested functionalities of the two software packages. A particular focus was on testing the metadata harvesting function.

How to cite: Sehnal, M. and Steiner, L.: GGOS Portal - The future Metadata Platform for Geodetic Data - Feasibility Study and Perspectives, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1314, https://doi.org/10.5194/egusphere-egu24-1314, 2024.

The accuracy and robustness of the International Terrestrial Reference Frame lies in the combination of the four space-geodetic techniques. Network imbalances, partly caused by geography and land coverage strongly favour the northern hemisphere in the number of observations and, to a less clear extent, in accuracies. This is evident in large tie discrepancies, for example in Katherine and Yarragadee in Australia, where results from the various techniques differ to a significant degree.

In this contribution we investigate the hypothesis that those discrepancies stem from deficiencies in the global combination of the various techniques, rather than from the individual observations. Specifically, we show that a regionally processed VLBI dataset is superior in its local station coordinate repeatabilities than when processed as part of the global network.

The Australian AuScope VLBI network has been operating for over 10 years, delivering higher cadence and partly more precise observations than the regular global IVS observations. It is our aim here to investigate whether a local comparison between VLBI and GNSS data in Australia can shed light into the mystery of significant tie discrepancies that are currently present in the ITRF for this region.

How to cite: McCallum, L.: High-cadence VLBI in Australia for an improved TRF in the southern hemisphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2949, https://doi.org/10.5194/egusphere-egu24-2949, 2024.

One of the major deficiencies in the realization of the International Terrestrial Reference Frame (ITRF) stems from the combination of the four space-geodetic techniques, that are contributing: Very Long Baseline Interferometry (VLBI), Global Navigation Satellite Systems (GNSS), Satellite Laser Ranging (SLR), and Doppler Orbitography and Radiopositioning Integrated by Satellites (DORIS). The GENESIS satellite mission, a component of the European Space Agency’s (ESA) FutureNAV program, will address this problem. Scheduled for launch in 2027, the GENESIS satellite embodies a dynamic space-geodetic observatory, equipped with instruments encompassing all four space-geodetic techniques. The mission’s objective is to facilitate the in-orbit combination and co-location of these techniques in space, known as space ties. It is not yet known how GENESIS will be implemented in geodetic VLBI operations. In this talk, we will present a dedicated simulation study of VLBI observations to the GENESIS satellite. We look at the realistic observability given current and foreseeable antenna networks and session cadence. We schedule and simulate VLBI observations to the GENESIS satellite to investigate the accuracy with which VLBI antenna positions of a global network can be derived. In addition to the most common VLBI error sources, troposperic delays, clock inaccuracies, and white noise, we simulate satellite orbital errors. The observations to the GENESIS satellite are scheduled within regular, geodetic experiments. We compare different schedules varying the amount of time that is dedicated to observe the satellite. We analyse how incorporating these observations reduces the amount of time quasar sources are observed and, consequently, how fundamental geodetic VLBI observables, i.e. Earth Orientation Parameters (EOPs), are affected. We try to answer the question whether it would be necessary to include observations to the GENESIS satellite outside of regular geodetic experiments in the form of dedicated GENESIS sessions. The fact that VLBI is organized in sessions instead of continuous observing further raises the question to assess a minimal mission lifetime to achieve desired accuracies.

How to cite: Schunck, D., McCallum, L., and Molera Calvés, G.: Optimal observing strategy of GENESIS in geodetic VLBI experiments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3287, https://doi.org/10.5194/egusphere-egu24-3287, 2024.

The project for realizing the reference network for absolute gravity in the Italian area is presented. This fundamental infrastructure is the general frame for all the scientific and technological activities related to the gravity field in Italy. The project is in line with the actions promoted by the International Association of Geodesy that during its 2015 General Assembly approved a resolution on the establishment of the new global gravity network the so-called International Terrestrial Gravity Reference System/Frame that will replace IGSN71.

The selection of the absolute gravity station sites in Italy has been performed either taking into account the existing absolute gravity stations and to have a homogeneous distribution of points. An initial set of 30 stations has been defined over the peninsular part of Italy and the two islands of Sicily and Sardinia. Particularly, the GGOS core station of Matera (the Agenzia Spaziale Italiana Center for Space Geodesy “Bepi” Colombo) is one of the network points as required in the documents of the GGOS-Bureau of Networks and Observations. Thus, this station will provide one link between the Italian national absolute gravity network and the GGOS observation system of IAG.

The project is now ongoing and will close at the beginning of 2025.

As required by the international standards on gravity measurements (https://www.bipm.org/documents/20126/41442296/CCM++IAG+Strategy+for+Metrology+in+Absolute+Gravimetry/7f9bc651-a2b6-08cc-7bba-f63b0a7e9765), the absolute gravimeters used in the measurements have been compared with absolute gravimeters that participated into international comparison campaigns in order to ensure the measurements traceability.

Furthermore, absolute gravity measurements have been supplemented with direct measurements of the local value of the vertical gravity gradient, in order to reduce to the ground reference level the absolute values measured by different instruments at different heights. The gravity field campaigns will be assisted by topographic survey campaigns. This will allow a precise georeferencing (of the order of 12 cm) of the gravity stations that will be so framed to the current ITRF.

The collected data will be then validated and reduced following the internationally accepted standards and finally published through a dedicate web page of the project. These data will also be sent for storage to the absolute gravity database maintained by the Bureau Gravimétrique International/Bundesamt fuer Kartographie und Geodaesie where the absolute gravity data that will contribute to the new global absolute gravity reference system are collected.

How to cite: Barzaghi, R., Riguzzi, F., Greco, F., Berrino, G., Germak, A., and Mazzoni, A.: The Absolute Gravity Reference Network of Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5776, https://doi.org/10.5194/egusphere-egu24-5776, 2024.

This paper presents the next step of the recently undertaken comparison of the tropospheric parameters independently derived from the analysis of Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite Systems (GNSS) data. We established a concept of cross-validation of the tropospheric parameters which allows us to focus now on the investigation of the discrepancies detected in the cross-validation. In particular, the dataset and its parameterization are examined in view of their impact on the obtained biases. The most recent 5 years of observations are considered, during which the sufficiently dense time series of observations are available for the legacy VLBI stations as well as for the new generation antennas following the VLBI Global Observing System (VGOS) specifications. In our study the co-location sites are limited to those observatories, where the legacy VLBI antenna, VGOS antenna and GNSS receiver are present. Furthermore, the tropospheric parameters are compared only at the common epochs, which are chosen to include the independently conducted observations of the legacy VLBI antenna, VGOS antenna and GNSS receiver during a defined time span. The corresponding solution parametrization is varied between intervals of VGOS, legacy and GNSS solution parametrizations in order to identify the most optimal duration. While the parameterization of the GNSS solution considers the tropospheric variations stable over 2 hours, the parameterization of VLBI solutions for legacy antennas is known to be reliable over a shorter 1-hour interval. The highly dense VGOS observations allow us to reach up to 20-minute duration without losing performance.

How to cite: Walenta, A., Flohrer, C., Dach, R., Thaller, D., and Engelhardt, G.: Investigating the independently derived tropospheric parameters at GNSS-VLBI co-located sites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6267, https://doi.org/10.5194/egusphere-egu24-6267, 2024.

We estimated the true Length of Day (LOD) from DORIS observations. The DORIS RINEX data has been processed by the DORIS development version of the Bernese GPS Software 5.2. We performed single satellite and multisatellite (Sentinel-6A, Sentinel-3A, Sentinel-3B, Jason-3, HY-2C, HY-2D, SARAL, and Cryosat-2), and pre-eliminated solutions. The DORIS data has been processed separately for each satellite on the basis of daily arcs. The troposphere, frequency offset, and orbit parameters were pre-eliminated from the normal equation systems and the finalized matrices were combined on a weekly basis. Hence, individual satellite solutions as well as multi-satellite combined solutions were created. From this solution, we calculated the transformation parameters w.r.t. ITRF 2020 (more precisely w.r.t. the DORIS extension DPOD 2020,) and after applying the 7-parameter Helmert transformation we calculated the RMS w.r.t. DPOD 2020.

We performed solutions with different settings, in particular with different handling of cross-track once-per-revolution harmonic parameters (adjusted with different constraints or not adjusted), since the amplitude of the sine term correlates with LOD. For a comparison of our results, we used the IERS 20 C04 model as a reference.

How to cite: Kumar, V., Stepanek, P., Filler, V., Dikshit, O., and Balasubramanian, N.: Estimation of the Length of Day (LOD) from DORIS observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6362, https://doi.org/10.5194/egusphere-egu24-6362, 2024.

During the last decade, the next generation VLBI system, called the VLBI Global Observing System (VGOS), has been developed. It involves new, fast moving radio telescopes, that are equipped with dual-polarization broad-band receivers covering a broad frequency range from upper S-band to lower Ku-band. VGOS is planned to become the major VLBI contribution to the Global Geodetic Observing System (GGOS), surpassing the legacy S/X VLBI contribution by about one order of magnitude in terms of precision and accuracy of the geodetic products. While still being in its roll-out phase, VGOS started to become operational in 2020. The International VLBI Service for Geodesy and Astrometry (IVS) is now operating two VLBI networks in parallel, the legacy S/X VLBI network, and the new VGOS network. In early 2024, up to 13 VGOS stations are observing together in 24 h network sessions three times per month. These sessions are meant for the determination of the terrestrial reference frame (TRF), the earth orientation parameters (EOP), and the celestial reference frame (CRF). Additionally, several short (1 h) VGOS sessions are performed on several days each week, involving two or three VGOS stations only and focussing on the determination on earth rotation, i.e. the UT1-UTC parameter. There are several ongoing projects worldwide to establish further VGOS stations. It is expected that several of these will become operational in the near future, some even already in 2024, thus improving the geographical distribution of the VGOS network. In conjunction to an improved and densified VGOS network it is also planned to intensify the observation plan. In this presentation, the current status, challenges, and prospects of VGOS are reviewed and discussed. This includes a review of the expectations and obtained results of the geodetic parameters derived from the analysis of VGOS sessions.

How to cite: Haas, R.: VGOS – current status, challenges, and prospects, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7362, https://doi.org/10.5194/egusphere-egu24-7362, 2024.

GMV is developing FocusPOD, a suite of tools for POD and geodesy, powered by GMV MAORI, a new GMV in-house Flight Dynamics & Geodesy library, written from scratch in modern C++ and python. FocusPOD supports several projects led by GMV, including the operational provision of Precise Orbit Determination (POD) products of the Copernicus Sentinel satellites in the frame of the CPOD Service, the simulation of tracking data for Galileo 2^nd Generation (G2G) System Test Bed, and other space debris and flight dynamics activities.

FocusPOD is currently capable of processing GNSS data, with a focus on on-board receivers to support POD, and Satellite Laser Ranging (SLR), mostly used in CPOD as an external orbit validation source. Capabilities to process DORIS have been recently added, also targeting POD applications as a first step. GMV’s roadmap for FocusPOD include processing VLBI in addition to these 3 techniques, with the target of becoming a reference software for the geodesy community.

This contribution intends to present FocusPOD roadmap to incorporate VLBI processing capabilities, describing the modelling challenges that have been identified and the various steps that will be taken to finally be able to process VLBI and provide VLBI-based ITRF products. As part of this roadmap, other parallel activities to tie together the rest of the techniques will also be undertaken and described, mostly related to the ground-based processing of GNSS, SLR and DORIS data.

How to cite: Fernández, C., Fernández, M., and Fernández, J.: VLBI processing in FocusPOD, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7935, https://doi.org/10.5194/egusphere-egu24-7935, 2024.

ICGEM is one of the five services coordinated by the International Gravity Field Service (IGFS) of the International Association of Geodesy (IAG). The service has been actively responding to the needs of the scientific community for the last two decades with an archive of static, temporal, and topographic global gravity field models of the Earth in a standardized format with the possibility to assign DOIs. Furthermore, ICGEM provides interactive calculation and visualisation services of gravity field functionals. Maintenance of such a service and development of new “demand-based” tools are of utmost importance to provide state-of-the-art products.

In 2016, the ICGEM portal has been renewed to guarantee a smooth transition to future needs. Since then, the last remaining component of the previous ICGEM portal, the G3 Browser, has been upgraded and integrated into the present ICGEM portal. The G3 Browser (http://icgem.gfz-potsdam.de/g3) aims to compute time series of equivalent water height interactively and gives users the opportunity to compare different gravity model time series as well as impacts of corrections (e.g., GIA, C20) or filters. The G3 Browser is complementary to existing services such as GFZ’s GravIS portal which provides ready-to-use products based on GFZ and COST-G solutions with already applied corrections and filters. On the other hand, the ICGEM G3 Browser includes time series from further processing centres and institutions and different filtering options.

Recently, ICGEM has included simulated models (http://icgem.gfz-potsdam.de/sl/simulated) in its archive that are relevant to future gravity mission studies. Currently available simulated Level 2a models are from the ESA’s MAGIC simulation studies and they are the first of their kind on the ICGEM Service. Monthly and weekly series of different scenarios have been made available on the relevant pages together with the links to the publications provided by the authors.

As demanded by the users, ICGEM plans to include some practical tools in the service, such as comparison of the functionals computed based on two different models and interactive evaluation tools n spatial and spectral domains. Finally, a new project called SAMDAT (Service and Archive for Mass Distribution And mass Transport data) that is funded by the German Research Foundation will be realized during the next three years which aims to expand the ICGEM service based on FAIR (Findable, Accessible, Interoperable, Reusable) data and a sustainable data archive principles.

How to cite: Ince, E. S., Reißland, S., Förste, C., and Flechtner, F.: 20 years of the ICGEM Service and the new developments  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8196, https://doi.org/10.5194/egusphere-egu24-8196, 2024.

The VLBI Global Observing System (VGOS) was created to meet the ambitious requirements set by the Global Geodetic Observing System (GGOS). Its primary objective is achieving millimeter-level precision while maintaining continuous 24/7 observations. Currently, both aims remain unfulfilled. Simultaneously, new requirements, such as the development of a dedicated VGOS Celestial Reference Frame (CRF), have emerged. Thus, a reevaluation of our current VGOS observational framework is necessary to reach the VGOS goals.

This study addresses three pivotal challenges within VGOS: attaining millimeter precision, providing observations for a CRF, and achieving uninterrupted 24/7 observations. Each of these topics demand a readjustment of our current observation scheduling methodology.

Based on insight from VGOS R&D sessions, this work discusses potential approaches to meet the requisite precision through shorter, signal-to-noise-driven observations. Additionally, it explores the combination of this methodology with source-based scheduling to facilitate the creation of essential observations for establishing a dedicated VGOS CRF. Finally, it addresses the issue of reaching 24/7 observations, currently limited by data transfer and correlation capacities. To overcome this, a potential solution involves a significant reduction in the recorded data volume per session by temporarily thinning out the schedule. Thus, it comes with a trade-off in precision. This concept might be seen as a paradigm shift in VLBI observations, traditionally striving for the highest precision possible, which we believe is worth being discussed. Based on observation statistics and Monte-Carlo simulations, we will elaborate on the expected impact of this approach. 

How to cite: Schartner, M., Petrachenko, B., Charlot, P., Xu, M., Collioud, A., Krasna, H., and Benedikt, S.: Rethinking operational VGOS observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9519, https://doi.org/10.5194/egusphere-egu24-9519, 2024.

The GGOS Bureau of Products and Standards (BPS) supports the Global Geodetic Observing System (GGOS) of the International Association of Geodesy (IAG) in its goal to provide highly accurate, consistent, and long-term stable geodetic products needed to monitor, map, and understand changes in the Earth’s shape, rotation, and gravity field. A key objective of the BPS is to keep track and to foster homogenization of adopted geodetic standards and conventions across all IAG components for the generation of geodetic products. This includes the interaction with the IAG Services and other entities that deal with standards and conventions, such as the Conventions Center of the IERS (International Earth Rotation and Reference Systems Service), the Commission A3 ”Fundamental Standards” of the International Astronomical Union (IAU) and the Technical Committee 211 of the International Organization for Standardization (ISO/TC 211).

This contribution presents the structure and role of the BPS. It highlights some of the recent activities, which are focused on the updating the BPS inventory of standards and conventions used for the generation of IAG products, the revision of the IERS Conventions, mainly related to Chapter 1 ”General definitions and numerical standards” as well as the description and promotion of geodetic products published at the GGOS website (www.ggos.org). The BPS also contributes to the generation of GGOS outreach material, such as videos, brochures, social media posts, etc. to make other disciplines and society aware of geodesy and its beneficial products. Finally, planned activities of the BPS according to the new GGOS Strategic Plan 2024-2034 are provided.

How to cite: Angermann, D., Gruber, T., Michael, G., Robert, H., Urs, H., Laura, S., and Peter, S.: GGOS Bureau of Products and Standards: Recent activities and future plans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10228, https://doi.org/10.5194/egusphere-egu24-10228, 2024.

2024 marks the 162^nd anniversary of the founding of the International Association of Geodesy. In 1862, 15 States agreed to cooperate in a "Central European Arc Measurement" project to observe the shape of the Earth. Besides leading to an improved knowledge of the shape of the Earth, the measurements taken during this project also led to an improved reference frame and geoid for Central Europe. In the course of taking these measurements scientific issues were encountered that also had to be addressed. The spirit of cooperation in both taking operational measurements and conducting scientific research that exists today within the IAG has been a hallmark of the IAG ever since it was founded in 1862.

The dual nature of the IAG, in advancing both observations and science, is reflected in the organizational structure of the IAG. Services are responsible for advancing geodetic observations in their area of expertise; Commissions and Inter-commission Committees are responsible for advancing geodetic science; Projects are incubators for new initiatives; the Communications and Outreach Branch, together with GGOS, is responsible for promoting geodesy to the other sciences and to society at large; and GGOS, the Global Geodetic Observing System, is meant to integrate the diverse geodetic observations into a coherent geodetic imaging system of the Earth as a whole.

One of the strengths of geodesy is its interdisciplinary nature. Geodetic observations contribute to improving our understanding of many different Earth processes, from space weather to core dynamics. This affords the opportunity for geodesists to collaborate with scientists from many different disciplines. Besides the value to science, geodetic data and products are also of great value to society. The International Terrestrial Reference Frame (ITRF) provided by the International Earth Rotation and Reference Systems Service (IERS), one of the 12 Services of the IAG, underpins location-based services like navigation aids on cell phones. By forging collaborations with other organizations, the IAG can ensure that its data and products continue to meet the needs of both its scientific and societal customers.

How to cite: Gross, R.: The International Association of Geodesy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10339, https://doi.org/10.5194/egusphere-egu24-10339, 2024.

With planet Earth being a deformable body, any geodetic marker attached to its crust exhibts slight motions in response to various geophysical forces. Prominent examples are the diurnal and semi-diurnal tides of the solid Earth, but also other periodic and non-periodic forces induced by mass transport divergence in atmosphere, oceans and the terrestrially stored water are deforming the Earth's surface and therefore displace any geodetic instrument attached to it. Based on a suite of different numerical model data-sets, the Earth System Modelling group at GFZ is routinely calculating both tidal and non-tidal surface deformations that can be readily applied as a priori information for the processing of space geodetic data. The model data-sets are publicly available as global grids with 3-hourly temporal sampling covering almost five decades from 1975 until present time.

We present results from dedicated geodetic analysis experiments in order to demonstrate potential impact of such prior information on the GNSS-based coordinate estimates. We utilize data from 220 globally distributed IGS stations from 2005 until 2019 and apply the IGS repro3 strategies for the GPS daily precise orbit determination strategy. We apply non-tidal atmospheric and oceanic loading corrections from the ESMGFZ products on the (i) observation, (ii) normal equation, and (iii) parameter levels, and study the impact of such background models on the coordinate time-series of GNSS permanent stations and other associated parameters. 

How to cite: Dobslaw, H., Wang, J., Dill, R., and Balidakis, K.: Modelling Global Crustal Deformations Induced by Geophysical Fluid Loading for Space-Geodetic Applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10584, https://doi.org/10.5194/egusphere-egu24-10584, 2024.

Creating an absolute space-tie where all the geodetic methods are onboard is the key for an improved and stable terrestrial reference frame as well as with various scientific applications. Such satellite concepts  have already been proposed to achieve an accurate and stable terrestrial reference frame. Next generation  Galileo satellites can provide a single well-calibrated platform for the colocation of the space-based geodetic techniques establishing precise and stable ties between the key geodetic techniques.  One of the most crucial and novel aspect of such concepts is the VLBI transmitter (VT) which will emit quasar-like signals from the space to be observed by the VLBI ground stations. VT can directly link the terrestrial and celestial reference frames and bring the unique features of VLBI technique to an Earth orbiting satellite. In the context of call for future Galileo payloads a novel VT has been under development.  VT shall be compatible both with the legacy and VGOS antennas.  Here, we present the progress on ongoing ESA study for VT for Galileo as well as for other future  missions. 

How to cite: Karatekin, Ö., Sert, H., Vasseur, H., Dehant, V., Ritter, B., Hugentobler, U., Kodet, J., Jelić, Ž., Paternoster, K., Kronschnabl, G., Ploetz, C., Gunessee, A., Lopez Rosson, G., and Krishnan, A.: VLBI signal transmitter onboard Earth orbiting satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10637, https://doi.org/10.5194/egusphere-egu24-10637, 2024.

The Global Geodetic Observing System (GGOS) is the response of the international geodetic community, organised under the umbrella of the International Association of Geodesy (IAG), to the need to monitor changes in the Earth system continuously. GGOS is Geodesy’s contribution to the Global Earth Observation System of Systems (GEOSS) by providing the reference frames needed for all position-dependent observations, thus the foundation for most Earth observations, and measuring changes in the Earth's shape, size, gravity field and rotation over time and space. GGOS is built on the Scientific Services of the IAG (IGS, IVS, ILRS, IDS, IERS, IGFS, ISG, PSMSL, IGETS, IDEMS, ICGEM, BGI) and the products they derive on an operational basis for Earth monitoring using space- and ground-based geodetic techniques. A key objective of GGOS is to realise an integrating framework that moves from the provision of technique-specific products to a level of combined, integrated products as the basis for a consistent modelling and interpretation of Earth system processes and interactions. This is necessary to ensure a coherent Earth monitoring system that contributes significantly to a better understanding of global change and its impacts on the environment and society. This is being achieved through strong international and multidisciplinary cooperation, focusing on (1) bringing together different geodetic observing techniques, services and analysis methods to guarantee that the same standards, conventions, models and parameters are used in all data analysis and modelling of Earth system processes; (2) combining geometric, gravimetric, and Earth rotation observations in data analysis and data assimilation to jointly estimate and model all necessary parameters representing the different elements of the Earth system; (3) identifying science and societal needs that can be addressed by (new) geodetic products and define the requirements for accuracy, time resolution, and consistency of these products; (4) identifying service gaps and developing strategies to fill them; and (5) promoting and enhancing the visibility of Geodesy by improving the accessibility of geodetic observations, information and products to the widest range of users and their attribution. This contribution summarises recent achievements, ongoing activities, and main challenges for the near future.

How to cite: Sanchez, L., Pearlman, M., Angermann, D., Sehnal, M., Gruber, T., Soja, B., Riddell, A., Elger, K., Gross, R., Heki, K., Ferrandiz, J. M., Schmidt, M., Melbourne, T., Craddock, A., and Miyahara, B.: GGOS: Ensuring a Coherent Earth Observation System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11678, https://doi.org/10.5194/egusphere-egu24-11678, 2024.

Geometric and gravimetric measurements both have proven to be robust approaches to analyzing sea level changes. The relationship between them is also worth exploring. In this research, the geometric signal and gravimetric signal as well as volume variations in Baltic Sea are compared during the period from 2002.4 to 2016.12. Ideally, the geometric signal should be the sum of gravimetric signal and volume variations, which is so-called sea-level budget. Here, the geometric signal is selected as altimetric data from multi missions combined with in-situ observations from tide gauges. The Mascon solutions are used as gravimetric signals while the volume variations are represented by steric height derived from Baltic Sea Physics Reanalysis model. The Baltic Sea is divided into four regions and the sea level budget equation is applied in each region. The results reveal the gravimetric and volume components exhibit satisfying correlations with geometric signals, which are larger than 0.85 in all four regions. The north region has the least correlation due to the sea ice problem. Furthermore, the comparison underscores the dominance of the gravimetric signal, contributing to approximately 90% of the total sea level change. On the other hand, the sea level trend is estimated by both geometric signal and the sum of gravimetric and volume components. The difference between the two trends in each region is mainly caused by ocean bottom deformation, primarily influenced by glacier isostatic adjustment (GIA). In the processing of mason, the GIA effect is already removed but the geometric signal contains ocean bottom deformation. Therefore, it is necessary to consider the land deformation during the trend comparison. However, because of the sea level equation, the sea level does not change as much as ocean bottom changes, but much smaller. Here we employ the geoid change from NKG2016LU as a trend correction in ocean bottom deformation. The findings contribute valuable insights for predicting sea level changes in the Baltic Sea, emphasizing the significance of accounting for gravitational and volumetric factors along with ocean bottom deformation in sea-level research.

How to cite: Qiu, F., Gruber, T., and Pail, R.: Investigation of sea level variations and trends in the Baltic Sea from geometric and gravimetric observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12665, https://doi.org/10.5194/egusphere-egu24-12665, 2024.

For more than 20 years, Japan Coast Guard has been developing various technologies to improve the observation accuracy of GNSS-Acoustic ranging. In this presentation, I will introduce two topics that we have been paying attention to in recent years in GNSS-A observations.

The first is about instrument errors and angular characteristics that depend on the shape of the transducer (sea surface station) or transponder (seafloor station). In the long-term observations conducted by JCG, multiple types of oscillators are used on both transducers and transponders. It has become clear that this instrumental error has a non-negligible effect on the positioning accuracy of GNSS-A, so we develop a new method to read a received signal considering the instrumental and angle dependence.

The second is about a representation method of ocean structure. When we estimate positioning of seafloor station, it is very important to consider sound speed structure (SSS). We use the open-source software GARPOS which can estimate transponder position with SSS. Yokota et al., (in prep) introduce a new representation method of SSS with G-ellipse in GNSS-A analysis. I will talk about the meaning of G-ellipse and some applications of this method.

How to cite: Nagae, K., Ishikawa, T., Watanabe, S., Nakamura, Y., and Yokota, Y.: Device and angle dependent waveform and a new representation of ocean sound speed structure in GNSS-A, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13228, https://doi.org/10.5194/egusphere-egu24-13228, 2024.

The International Association of Geodesy (IAG) introduced the International Height Reference System (IHRS) in 2015 as the international standard for the accurate determination of physical heights worldwide. The primary vertical coordinates are geopotential numbers referenced to a conventional W_o value. The realization of the IHRS is the International Height Reference Frame (IHRF), which corresponds to a global network of reference stations with precise reference coordinates, geopotential numbers specified in the IHRS and (X, Y, Z) coordinates in the International Terrestrial Reference Frame (ITRF). In the framework of the IAG and after a strong international collaboration the scientific foundations of the IHRS have been outlined and a first realization of the IHRF has been computed. In that frame, it has been deemed necessary to ensure the long-term sustainability of the IHRF by establishing a new dedicated component within the IAG gravity field related services. Recently (December 2023), the IAG has accepted the establishment of the IHRF Computation Center (IHRF-CC) as a central coordinating body under the responsibility of the International Gravity Field Service (IGFS) with direct adherence to the IGFS Central Bureau (IGFS CB), composed of individual modules taking care of the main components of the IHRF. These modules are the IHRF Reference Network Coordination, the IHRF Conventions’ Coordination, the IHRF Associate Analysis Centers, and the IHRF Combination Coordination. In this work we outline the main components of the IHRF-CC, the objectives and goals foreseen, the main responsibilities of its component as well as their planned interrelation, complementarity and synergies in order to deliver its main products. The latter refer to the IHRS standards and conventions and their update; the update of the IHRF network and status; the IHRF computation cookbook; computation of offsets to national and regional vertical datums; and finally, IHRF station coordinates for the global core network and contributions to regional and national densifications. Finaly, we outline the first steps taken to formulate the IHRF-CC components and initiatives to begin normal operation.

How to cite: Vergos, G. S., Sánchez, L., and Barzaghi, R.: IHRF Coordination Center, a newly established IAG/IGFS component to ensure the sustainability of the IHRS/IHRF, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14602, https://doi.org/10.5194/egusphere-egu24-14602, 2024.

Classical Very Long Baseline Interferometry (VLBI) observations are unique for the estimation of UT1-UTC and Celestial Intermediate Poles (CIPs). The innovative feature of placing a VLBI transmitter on a satellite provides additional information about Earth’s origin, the geocenter. It also enables the VLBI technique to be combined with other satellite-based techniques such as GNSS via a space tie on the common satellite. This research aims to evaluate the long-term geodetic products estimated by VLBI observations to a next-generation Global Navigation Satellite System (NextGNSS) satellite. We simulate a VLBI network comprising 20 stations including 18 current and future VLBI Global Observing System (VGOS) stations that simultaneously observed a single VLBI transmitter on a Galileo-like satellite, in conjunction with extra-galactic radio sources. Terrestrial Reference Frame (TRF) including station positions and velocities, geocenter coordinates, and the full set of Earth Orientation Parameters (EOPs) are estimated for a long-term period of three years. Subsequently, we impose no-net rotation (NNR) and no-net translation (NNT) conditions, resulting in a minimum constraint solution. Satellite observations were assumed at a ratio of 30% of the total observations. The results show that the estimated corrections in the X and Y geocenter coordinates are on the mm-level and the Z coordinate on the cm-level. 

How to cite: Raut, S., Glaser, S., Schreiner, P., Neumayer, K. H., Mammadaliyev, N., and Schuh, H.: Long-term geodetic product assessment derived from a VLBI transmitter on Next-Generation GNSS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15350, https://doi.org/10.5194/egusphere-egu24-15350, 2024.

The Japanese Archipelago lies along subduction zones, where megathrust earthquakes are known to occur repeatedly (e.g., M_w 9.0 2011 Tohoku-oki Earthquake). The source regions of such megathrust earthquakes mainly lie beneath the seafloor, making it necessary to monitor the crustal deformation not only on land but also on the seafloor. The Japan Coast Guard (JCG) has been conducting seafloor geodetic observation using the GNSS-Acoustic ranging combination technique (GNSS-A) since the early 2000s. By combining GNSS and underwater acoustic ranging, GNSS-A measures the absolute position of a seafloor benchmark in the precision of centimeters, which provides us fruitful information on the crustal deformation under ocean. JCG currently deploys 27 seafloor sites along the Japan Trench and the Nankai Trough named the Seafloor Geodetic Observation Array (SGO-A).

JCG and the Univ. Tokyo group has been working on open data of GNSS-A. In the past, we have published the position time series data (Yokota et al. 2018), and the GARPOS software (Watanabe et al. 2020) which is used in our current routine analysis. Recently, we have been discussing on a standardized data format for GNSS-A in the task force of the Inter-Commission Committee on Marine Geodesy (ICCM) in the International Association of Geodesy. Currently, we are releasing the latest position time series data of each SGO-A site, and the GNSS-A observation dataset in the GARPOS data format (https://www1.kaiho.mlit.go.jp/chikaku/kaitei/sgs/datalist_e.html).

How to cite: Nakamura, Y., Ishikawa, T., Watanabe, S., Nagae, K., and Yokota, Y.: Overview of Japan Coast Guard’s GNSS-A seafloor geodetic observation and data management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15420, https://doi.org/10.5194/egusphere-egu24-15420, 2024.

The International DORIS Service (IDS) had its 20^th anniversary in 2023 and is now looking forward to new challenges.

Over a 30-year period, between 1990 and 2020, the DORIS constellation contributing to the IDS has totaled nine satellites with lifetimes ranging from 3 to 19 years (with the lifetime of SPOT-2, at 19 years, being a record). Since the launch of the SWOT mission in December 2022, nine satellites have been in simultaneous operation and are supplying DORIS data to the IDS. This large number of missions is a challenge for the Analysis Centers, which must integrate them into their processing, taking into account each mission’s special characteristics (w.r.t shape, altitude, attitude law). New missions are expected from 2025 including Sentinel-3C & 3D, Sentinel-6B & 6C, HY-2E & 2F, GENESIS. In addition, the DORIS system continues to evolve. The ground network is growing while providing a high level of service, 4G beacons are gradually replacing those of the previous generation, and a new more powerful DORIS instrument is under study.

New groups have approached IDS in recent years, bringing new processing capabilities, or wishing to become involved in DORIS processing in the medium term. These new strengths are also opening up new applications. Since 2021, DORIS data from the Jason-3 mission have been made available to IDS with a delay of less than three hours. This has enabled the IDS Working Group “NRT data” to demonstrate the value of these data for validating existing GNSS-based ionosphere models. In 2024, NRT DORIS data will be made available for additional missions (Sentinel-3A, Sentinel-3B, Sentinel-6A, Saral…). A new established IDS Working Group will focus on using these data for further ionospheric modelling applications.

In this presentation, we provide information on DORIS system developments and upcoming missions. We also present the recent achievements made by IDS and its components, and the future plans for the service.

How to cite: Soudarin, L., Lemoine, F., Boniface, C., Moreaux, G., and Saunier, J.: The International DORIS Service: new challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15590, https://doi.org/10.5194/egusphere-egu24-15590, 2024.

How to cite: Anastasiou, D., Papanikolaou, X., Serelis, G., and Tsakiri, M.: Velocity field estimated from HEPOS permanent GNSS network in Greece, preliminary results., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17390, https://doi.org/10.5194/egusphere-egu24-17390, 2024.

The geodetic services of the International Association for Geodesy (IAG) are international key players in the provision and distribution of geodetic data. Their operating institutions and funding agencies increasingly require the provision of tangible statistics on data use and access. In addition to the classical provision of data download statistics, data use can also be provided through citations in scholarly literature. Credit through citations, together with the desire for a coordinated approach across the geodetic services, was the motivation for the IAG’s Global Geodetic Observing System (GGOS) to establish a “Working Group on using DOI (digital object identifier) for Geodetic Data Sets” in 2019. The “GGOS Committee on DOIs for Geodetic Data Sets” is continuing the work since 2023 for the longer term.

Challenges for applying DOIs to geodetic data and products include the fact that they are mostly dynamic, often provided as real-time data streams (with hundreds of files per day) where the “classical” assignment (and citation) of DOIs to individual (daily) files provided in different product levels is not a practicable solution. In addition, many geodetic data products are based on contributions from hundreds of researchers and institutions that need to be acknowledged following “rules for good scientific practice”. Today, DOI-referenced research outputs are fully citable in scholarly literature and scientific journals are increasingly demanding that all sources underlying scientific results (data, code, models, samples) are made available/ published along with the article. Initial data citation metrics allows data providers to demonstrate the value of the data collected by institutes and individual scientists. 

An additional benefit of using DOIs for geodetic data is the associated standardised (e.g. DataCite) and machine-readable metadata for data discovery. DataCite is a DOI registration agency specialised for data, code and other publications beyond scholarly literature. Their constantly further-developed metadata schema is supporting the implementation of the FAIR Principles (to make data findable, accessible, interoperable and reusable for humans and machines) and complements discipline-specific metadata standards, like GeodesyML for GNSS station information.

Here we present the first version of our metadata recommendations for geodetic data, developed for the GNSS data use case. The recommendations guide through the DataCite metadata schema, identify recommended and optional properties and how they can be used for geodetic data supporting data discovery. Initial guidelines were: (1) to include persistent identifier, like ORCID, ROR, DOI for uniquely identifying persons, institutions in the DOI metadata and cross reference with related data/articles/code whenever possible. (2) to have a maximum alignment with existing metadata, like GeodesyML or Sitelogs for GNSS station data with automated processes for mapping DOI metadata generation from existing GNSS metadata bases to DataCite metadata.

How to cite: Elger, K. and the GGOS Committee on DOIs for Geodetic Data Sets: Metadata recommendations for geodetic data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19106, https://doi.org/10.5194/egusphere-egu24-19106, 2024.

In the complex geodynamic context of the central Mediterranean, squeezed between the two major tectonic plates of Africa and Eurasia, the Adria microplate plays a key role in modulating the deformation and seismicity of the region. The northern sector of Adria is of particular interest as it exhibits moderate seismicity despite low deformation rates.

The crustal deformation is monitored thanks to the various geodetic networks in the area, which can provide continuous and highly accurate daily data. The geodetic network with the most stations is the Friuli Regional Deformation Network - FReDNet (https://frednet.crs.ogs.it), set up by the National Institute of Oceanography and Applied Geophysics – OGS, to monitor the distribution of crustal deformation and provide complementary information for regional seismic hazard assessment (Zuliani et al., 2018). FReDNet currently counts 22 permanent GNSS stations homogeneously covering the Eastern Alps, the alluvial plain, and the coastal areas of northeastern Italy. Most of the time series are longer than 15 years. In addition to FReDNet, another geodetic network in northeastern Italy, the Marussi GNSS network, comprises ten stations and is managed by the Friuli Venezia Giulia (FVG) region. The geodetic network of the Veneto region in the west and the permanent GNSS networks of Austria and Slovenia in the North and East respectively, complete the puzzling northern border of the Adria microplate opposite the southern front of Eurasia.

We processed daily GNSS data from the different permanent geodetic networks using the GAMIT/GLOBK software package version 10.71 (Herring et al., 2018). Data processing was performed on the HPC cluster GALILEO100 of CINECA, which uses the SLURM system for job scheduling and workload management (Tunini et al., 2023).

This study shows the processing results, in the form of time-series and velocity field, as well as the different aspects taken into account to check the reliability of the processing procedure applied and the results obtained, such as the consideration or avoidance of tidal or non-tidal loads or the change of the reference stations, the influence of the type of GNSS monuments, or the location of the geodetic antenna on a roof or in an open field.

OGS and CINECA supported this research under the HPC-TRES program. We acknowledge the CINECA award under the ISCRA initiative for the availability of high performance computing resources and support (IscraC IsC96_GPSIT-2 and IsCa7_GPS-MAST).

References

Herring, T.A. and King, R., Floyd, M.A. and McClusky, S. C.; 2018: GAMIT Reference Manual: GPS Analysis at MIT, Release 10.7. Department of Earth. Tech. rep., Massachusetts Institute of Technology, Cambridge, Mass. URL: <http://geoweb.mit.edu/gg/Intro_GG.pdf>

Tunini, L., Magrin, A., Rossi, G., and Zuliani, D.;  2023: GNSS time series and velocities about a slow convergent margin processed on HPC clusters: products and robustness evaluation, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-131, in review.

Zuliani, D., Fabris, P., Rossi, G.; 2018: FReDNet: Evolution of permanent GNSS receiver system. In: New Advanced GNSS and 3D Spatial Techniques Applications to Civil and Environmental Engineering, Geophysics, Architecture, Archeology and Cultural Heritage, Lecture Notes in Geoinformation and Cartography; Cefalo, R., Zielinski, J., Barbarella, M., Eds.; Springer: Cham, Switzerland, pp.123–137.

How to cite: Tunini, L., Magrin, A., Rossi, G., and Zuliani, D.: Crust deformation in the Northern Adria plate from twenty years of geodetic monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19907, https://doi.org/10.5194/egusphere-egu24-19907, 2024.

As earth observing services and techniques have flourished, data and products have exponentially grown, like the proliferation of GNSS stations capable of providing real-time data, SLR stations shifting to kHz lasers, and VLBI’s implementation of VGOS telescopes.  The CDDIS is continually evolving to fulfill the new storage, quality check, and latency requirements that these changes bring, as well as meet new standards such as the shift toward FAIR and open science.  These have shaped how the CDDIS develops new software and resources.  Beginning next year, the CDDIS will begin to transition their data and products to the AWS cloud, beginning with DORIS data.  This poster will highlight the CDDIS’s recently updated processing system, new data and products available, and future work.

How to cite: Woo, J. and Yates, T.: The CDDIS – Updates and Future Developments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22454, https://doi.org/10.5194/egusphere-egu24-22454, 2024.

Reliable integer ambiguity resolution is the key to the global navigation satellite system (GNSS)-based precise positioning applications. In many scenarios, it is a common setup that more than one antenna is mounted on the moving platform. The integer ambiguity resolution can therefore be improved if the constant baseline information between the antennas is used reasonably. In this contribution, the baseline information, which can be measured a prior  as the constraint and has been successfully used to improve the GNSS-based attitude determination, is now extended to relative positioning. The baseline length is fully integrated into the ambiguity objective function of the relative positioning model, thus improving the reliability of relative positioning resolution. In experimental validation, both simulated and real datasets are tested to demonstrate the benefits brought by the baseline length constraint. The results show that the constraint ensures a higher ambiguity resolution success rate, and the improvement is more obvious when the observable condition is weaker.

How to cite: Wu, S., Fan, B., Ding, Y., Jiang, Z., Ran, Y., and Cao, K.: Improving ambiguity resolution success rate in GNSS-based relative positioning with moving-baseline length constraint, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-23, https://doi.org/10.5194/egusphere-egu24-23, 2024.

We present a neural network optimized for producing GNSS displacements to explore machine learning capabilities in forecasting ground motions for earthquake early warning and to improve our understanding of ground motion in real-time. For earthquake early warning, displacements at GNSS receivers are currently being incorporated into the ShakeAlert system, but displacements in real-time are problematic due to issues with phase ambiguity fixing, cycle slips, and loss of satellite lock as well as dilution of precision from satellite network geometry. These errors can lead to anomalous motions of up to a meter. Raw GNSS observations for velocities can utilize orbits (relative positions) and remove uncertainties caused by path errors, leading to a higher precision observation than the displacements. In general, peak ground velocity is diagnostic of earthquake damage and displacement is diagnostic of total moment release, so obtaining these observations at the highest fidelity is crucial for rapid earthquake source estimation. Since processing raw GNSS velocities gives a higher precision observation, we aim to derive displacements from velocities using a machine learning approach. We use a Long Short-Term Memory (LSTM) network, a recurrent neural network (RNN) with the ability to remember values for an arbitrary amount of time, for time series prediction of the GNSS displacements. The input variables for the model are three-component GNSS velocities derived from the SNIVEL software package, with the possibility of including signal to noise and phase observables, and the output variable is the GNSS displacement time series. The GNSS displacement is validated against the displacements computed with the precise point positioning code GipsyX. With over 2250 1-Hz observations from 82 different events ranging from M4.9 to M9, we have ample data to train, validate, and test the network on. We train several neural network instances on random selection of train/validation/test split for redundancy and shuffle data input order for each instance. By computing the GNSS displacement from GNSS velocities, we produce a higher precision observation, a low-cost method for monitoring deformation without the traditionally high overhead associated with real-time GNSS processing, and the possibility of direct onboard receiver transmission of displacements.

How to cite: DeGrande, J. and Crowell, B.: Evaluating a Long Short-Term Memory(LSTM) network for real-time high-rate GNSS time series analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-309, https://doi.org/10.5194/egusphere-egu24-309, 2024.

In Precise Point Positioning (PPP), independently estimating the receiver clock parameters at each observation epoch introduces heightened noise in the estimated station coordinates and troposphere parameters due to correlations. To address this issue, stochastic modeling is applied to the receiver clock parameter, thereby enhancing the stability of PPP solutions and minimizing clock noise for precise time transfer. Importantly, the feasibility of receiver clock modeling relies on GNSS receivers being connected to exceptionally stable atomic clocks, such as hydrogen maser clocks (HM), which exhibit significantly lower noise compared to other clock types.

The strategy proposed by our team involves introducing Markov stochastic process modeling for the receiver clock parameters through a random walk. We opted for this stochastic process because of its simplicity in both comprehension and implementation. We conducted tests with different levels of random walk constraints for GNSS stations equipped with various clock types, exploring both Galileo-only and multi-GNSS solutions in kinematic and static PPP modes. We compare the results against a reference solution without any additional constraints. In multi-GNSS solutions, a common clock parameter is determined alongside inter-system biases (ISBs), with the common clock parameter identified as the GPS clock.

Research outcomes demonstrate that comparable results can be achieved by imposing constraints solely on the common clock parameter while treating ISBs as constant parameters. Similarly, constraints on both the common clock parameter and ISBs, with a ratio of 1:100, yield the most favorable results. However, adopting other clock-to-ISB constraint ratios, such as 1:1 and 1:10, leads to suboptimal performance. In the static PPP, the introduced clock modeling significantly enhances the precision of time transfer by effectively reducing clock noise. In the kinematic PPP, stochastic clock modeling has a marginal impact on the North and East coordinate components, whereas the Up component exhibits substantial improvement, mainly for GNSS receivers equipped with HM. An examination of Zenith Total Delay (ZTD) in both Galileo-only kinematic and static PPP modes reveals the discernible impact of clock constraints, as evidenced by observed offsets in the respective outcomes. In the case of multi-GNSS solutions, this influence is less prominent, attributed to weaker correlations between ZTD and clock parameters in multi-GNSS solutions compared to Galileo-only.

How to cite: Mikoś, M., Sośnica, K., Kazmierski, K., and Hadas, T.: Impact of stochastic modeling applied to the receiver clock parameter for Galileo-only and multi-GNSS solutions     , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-334, https://doi.org/10.5194/egusphere-egu24-334, 2024.

GNSS real-time PPP have been demonstrated to be applicable to PWV retrieval effectively. The GNSS real-time satellite clock error is one of the essential factors that affects the accuracy of PPP, and its estimation method is usually considered to be white noise random processes which ignore the stable periodic variation characteristics of GNSS atomic clocks. In addition, the GNSS satellite clock offsets real-time service have low timeliness due to the time consumption of clock offsets series estimation and fitting of quadratic polynomial coefficients, even when communication is interrupted for a short period of time, RTS cannot be obtained. Thus, we developed a method that directly estimate satellite clock model coefficients simultaneously with tropospheric wet delay, receiver clock error, and phase ambiguity parameters from global GNSS code and phase measurements. The difference from traditional RTS is that the satellite clock model coefficients can be delivered to PPP users once estimated process is completed without the step of fitting. Several satellite clock models composed of polynomial and harmonic-based functions are applied to the parameter estimation of real-time satellite clock error, we compared the accuracy of estimated model parameters to IGS real-time satellite clock offsets, and discussed its performance of PPP positioning and PWV retrieval. Finally, we simulated the performance of proposed parameter estimation scheme of satellite clock error and PWV retrieval for different update interval in case of communication interruption for several minutes.

How to cite: Li, X. and Li, H.: Parameter estimation of GNSS real-time satellite clock error and its application in PWV retrieval, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1303, https://doi.org/10.5194/egusphere-egu24-1303, 2024.

As part of the International GNSS Service (IGS), several analysis centers (i.e., CODE, CNES/CLS, GFZ, WUHN) provide GNSS (GPS, Galileo, BeiDou) satellite phase bias products to support precise point positioning with ambiguity resolution (PPP-AR). Due to the high correlation with satellite orbits and clock offsets, it is difficult to assess directly the precision of satellite phase bias products. The commonly used approach is to check the positioning performance in PPP-AR applications. However, errors or outliers in phase bias of a specific satellite are not directly visible in this process but lumped into the overall observation residuals. This contribution presents a method independent of ground measurements to detect outliers in satellite phase biases by computing the total Difference of satellite Orbits, Clock offsets and narrow-lane Biases (DOCB) at the midnight epoch between two consecutive days. This method can be also used to assess the consistency of satellite products between two different analysis centers. It is convincing that after removing the detected outliers in individual analysis centers the number of large differences of satellite phase biases between two analysis centers is notably reduced. To show the impact on PPP-AR, we generate a list marking all the outliers in the phase bias products from individual analysis centers, and evaluate the performance in ground-station kinematic positioning and Sentinel-6 satellite orbit determination.

How to cite: Duan, B., Hugentobler, U., Parra, C., Liu, Y., and Montenbruck, O.: IGS GNSS satellite phase bias products: quality assessment and applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3486, https://doi.org/10.5194/egusphere-egu24-3486, 2024.

Abstract:In high-risk and complex mountainous environmental conditions, geological hazard bodies are often located in high mountain and canyon areas, with problems such as poor observation environment, large altitude differences, frequent extreme weather, and poor data communication. When deploying GNSS monitoring equipment using drones, they face difficulties in safe flight and precise navigation and positioning.This article studies the performance enhancement technology of multi-sensor integrated navigation. Based on the low-cost MEMS and GNSS tight combination algorithm, a comprehensive filtering technology integrating unscented Kalman filtering (UKF) and particle filtering (PF) is proposed. Two parallel running UKF and PF predictors are used to alternately use the information received by the fusion filter through input interaction, model filtering, and adaptive filtering optimization, in order to improve the reliability and accuracy of filtering, Simultaneously combining the advantages of Fuzzy Inference System (FIS) and Sparse Random Gaussian Model (SRG), the SRG model is used to estimate the initial state vector, and then FIS is used to update all current states to obtain the optimal predicted state vector. During the interruption of GNSS signal, the measurement data trained by FIS is jointly provided by INS and GNSS. In order to improve the required prediction accuracy, time sliding is used to control the data flow generated by INS and GNSS, Fully utilize the linear velocity and angular velocity increments output by INS to update the attitude, velocity, and position of unmanned aerial vehicles, improve the filtering convergence speed, navigation positioning accuracy, and reliability in the event of GNSS signal interruption. This article designs a multi-source fusion positioning system framework and integration scheme for unmanned aerial vehicle applications, and develops a high-precision fusion positioning software system, mainly including GNSS real-time data reception, GNSS data preprocessing, GNSS/INS integrated navigation data processing, real-time positioning result forwarding and other modules. The test results show that the positioning accuracy of complex environment integrated navigation is better than 0.5 meters.

Key words:Geological disasters;Unmanned Aerial Vehicle;Integrated navigation;High precision

How to cite: Wu, J., Wang, X., and Zhang, Q.: High precision positioning and navigation technology for unmanned aerial vehicles in high-risk environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3695, https://doi.org/10.5194/egusphere-egu24-3695, 2024.

Using LEO satellites for positioning and navigation has been a research hotspot in the GNSS community in recent years. As the LEO satellites are much closer to earth and move much faster relative to earth than GNSS, precise point positioning (PPP) convergence time can be substantially improved. Various simulation studies on LEO augmentation have been carried out, but its performance with real LEO observations in a real-world environment is rarely reported. The CENTISPACE^TM system, which is developed by Beijing Future Navigation Tech Co., Ltd., has launched four experimental satellites in the last two years, providing a good opportunity for studying LEO augmentation. We collect real LEO navigation observations from two CENTISPACE^TM satellites using a regional network. Before conducting LEO-augmented PPP, orbit determination and time synchronization (ODTS) for the experimental LEO satellites are first investigated, and a data-processing framework is established using the space-borne GNSS observations from LEO satellites and the LEO augmentation observations from ground stations. The LEO-augmented PPP algorithm is then derived, with a focus on the LEO relativistic effect. With these bases, we analyze the LEO-augmented PPP performance with different GNSS systems (including GPS, BDS, and Galileo) combined with LEO satellites. The static PPP tests using one (G/C/E), two (GC/GE/CE), and three (GCE) GNSS systems show that the average convergence time is significantly reduced with the participation of the two LEO satellites, from 32.7, 17.9, and 14.2 min to 16.7, 8.9, and 5.7 min, respectively. This indicates that adding only two LEO satellites improves the convergence times of static PPP with one, two, and three GNSS systems by 48.9%, 50.2%, and 59.8%, respectively. For PPP precision evaluation, the GNSS-only 3D positioning errors are 6.1, 4.6, and 4.6 cm with one, two, and three systems, respectively. They are reduced to 5.2, 3.9, and 3.8 cm by adding two LEO satellites, respectively. The corresponding improvements are 13.9%, 16.4%, and 18.8%, respectively. The above study not only validates the customized processing framework and strategy for LEO augmentation processing but also demonstrates the great potential of LEO augmentation. With more LEO satellites to be deployed in the future, much larger improvements can be achieved.

How to cite: Li, W., Li, M., Huang, T., Chang, C., and Zhao, Q.: Precise point positioning with LEO augmentation: results from two experimental satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3726, https://doi.org/10.5194/egusphere-egu24-3726, 2024.

The Earth deformation generally is divided into horizontal and vertical components. The vertical component has different behaviour and complexity caused by the tidal and non-tidal loading and other effects. In this contribution, the vertical motion rate of the Earth crust is considered and studied based on GNSS coordinate time series. The focus of this presentation is the signal processing algorithm applied to the time series. One of the challenges in signal extraction and data processing in the sequential steps is the error budget. The strategy which we considered in the uplift determination process after data-cleaning and outlier detection is to firstly detect the significant signals in the time series and the change points. Then, after removing the aforementioned signals e.g. annual, semi-annual, diurnal, tidal etc., the rate of the up component of the GNSS time series and the related uncertainty is estimated. To achieve this, we employed Monte-Carlo Singular Spectrum Analysis i.e. Monte-Carlo SSA for the signal detection process, Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) for the change point detection, variance estimation algorithm e.g. LS-VCE, Allan variance etc. for noise characteristic determination and Least-Squares estimation for the estimation of uplift rate and the related uncertainty. In an overall view, in this contribution, the importance of realistic errors is highlighted to estimate the uplift rate and the related uncertainty e.g. as geophysical boundary value for mantle convection models.

How to cite: Karimi, H., Heydari, M., and Hugentobler, U.: Determination of realistic uplift rate and noise assessment using GNSS coordinate time series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3956, https://doi.org/10.5194/egusphere-egu24-3956, 2024.

Before 2016, the users had access only to the position-velocity-time (PVT) information from the GNSS chipsets, and the raw GNSS observations were not available. The GNSS module's positioning accuracy on smartphones typically ranged from 3 to 5 meters under favorable multipath conditions, but over 10 meters in challenging environments. This level of accuracy was not sufficient for some applications. Fortunately, in May 2016, during the "Google I/O" conference, Google announced that the raw GNSS measurements, i.e., the pseudorange, carrier-phase, Doppler shift and carrier-to-noise density ratio (C/N0) observations, would be accessible through the Android Nougat (version 7) operating systems. Google has officially released Android 7 (Nougat) on August 22, 2016, marking a breakthrough for the GNSS community. Since then, research has been conducted to develop new algorithms to improve GNSS positioning performance using these mass-market devices. In 2021 and 2022, the Android GPS team of Google hosted two Google smartphone decimeter challenges (GSDC), where various smartphone GNSS datasets of real vehicular applications were used to determine smartphone positioning accuracies. As has been revealed, meter-level accuracy is generally achieved by the leading participants, which is still not enough to enable smartphone precise positioning. This indicates an ongoing demand to enhance the positioning accuracy with smartphones.

Different positioning algorithms, such as absolute or relative positioning methods can be applied to the smartphone observations as well. Precise point positioning (PPP) is a powerful method for conducting accurate real-time positioning using a single receiver. Research papers have reported PPP smartphone positioning accuracy ranging from decimeter to sub-meter accuracy, depending on different factors such as the environment and positioning mode (static and kinematic). Most studies have so far focused on utilizing the GNSS only observations obtained from the smartphone's API. However, incorporating additional information as constraints can enhance accuracy and overall stability (for example height information).

The Android operating system incorporates a set of functions known as APIs, allowing the users to use the system's features. Each Android version has distinct types of APIs. Among these, the android.location API is dedicated to the location-related services, with the "Location" class being one of them. This class consists of parameters such as latitude, longitude, altitude, timestamp, accuracy, bearing and velocity. The "AltitudeMeters" from this class provides the height above the WGS84 ellipsoid in meters, serving as supplementary information in this research. Although the vertical positioning accuracy of GNSS is generally lower than the horizontal accuracy, utilizing recorded height from the smartphone GNSS chipset can still be beneficial. This incorporation increases the degree of freedom and strengthens the geometry of the receiver and satellites. In this study, we assess the effectiveness of the uncombined PPP model in the presence of height constraints. We will utilize both pedestrian walking and vehicular datasets collected by a dual-frequency Xiaomi Mi8 device to evaluate the effect of adding height constraint to PPP model. We expect an improvement on the root-mean-square (RMS) of horizontal positioning, the 50th percentile error, and the convergence time when employing the height constraints.

How to cite: Zangenehnejad, F. and Gao, Y.: Height-constrained uncombined PPP for enhanced pedestrian and vehicular positioning with an Android smartphone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4153, https://doi.org/10.5194/egusphere-egu24-4153, 2024.

Time series related to a GNSS network at regional scale, or larger, generally show a spatially correlated component, called common-mode signal (CMS), related to both unmodelled geophysical processes, including environmental loading effects, and technique-dependent systematic errors that persist after data processing. The CMS estimation is very useful for two reasons: (i) untreated CMS leads to long-period noise in coordinate time series which induces bias and higher uncertainty in velocity estimates, so that many authors prefer the term CME (Common Mode Error) instead of CMS; (ii) whether it is possible to adequately discriminate between the components of the CMS, it is then possible to obtain relevant information regarding some geophysical phenomena.

Independent Component Analysis (ICA) is particularly useful for estimating the CMS because the ICA components show insightful correlations, e.g., with atmospheric and non-tidal ocean loading displacements. For this reason, we propose a small MATLAB toolbox, partially compatible with GNU Octave, for ICA-based CMS estimation and, if required, filtering.

The ICA is implemented using a FastICA algorithm in which data whitening is carried out using a Principal Component Analysis (PCA) modified in order to allow the use of incomplete time series. In this way, in the case of short periods of data loss (a few days or also some weeks), the ICA is obtained without use of data interpolation. The only used preprocessing technique is detrending. The spectral content of each ICA component can be studied by means of both frequency and time-frequency analysis and the filtering can be carried out either in the frequency domain or by means of Multiresolution analysis (MRA), according to the user’s choice. This filtering requires continuity of the time series and, therefore, in the case of short periods of data loss (a few days, at most a few weeks), interpolation is needed to build the required continuity; for non-short periods of data loss, no interpolation is implemented. The fact that the interpolation occurs after the CMS analysis, and therefore has no possible effect on its estimate, should be noted.

The toolbox, which is designed to be used both independently and together with the StaVel/GridStrain toolbox developed by the same authors, will soon be made available on the Harvard Dataverse. Development of the Python version is planned.

In order to test the validity of the proposed approach in the case of real data, it is applied to vertical data related to a network of some dozens of GNSS stations located in Southern Italy (Sicily and Calabria) and Greece (North-Western Greece and Peloponnese).

How to cite: Teza, G., Pesci, A., Elia, L., and Meschis, M.: Extracting and filtering the Common Mode Signal of GNSS coordinate time series via Independent Component Analysis and Multiresolution analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4188, https://doi.org/10.5194/egusphere-egu24-4188, 2024.

Satellite-Based Augmentation System (SBAS) relying on ground monitoring networks can only provide regional services in their own countries or regions. DFMC can expand service coverage and improve service quality, but it still needs the support of the ground monitoring network. Equipped with a spaceborne receiver, LEO has a unique advantage in space-based monitoring, which is expected to expand the service coverage and improve the service performance of SBAS. A global integrity monitoring method of SBAS combining LEO satellites and regional ground station data is proposed. Based on simulation data, the improvement effect of this method compared with that of relying solely on ground station data is verified from the aspects of correction sequence, monitoring arc integrity, enhanced satellite number and coverage. The results of global user positioning accuracy, integrity and usability evaluation are also given. The results show that this method can effectively extend the service coverage of SBAS and improve its performance. On a global scale, the average horizontal positioning accuracy of users is better than 0.5m and the average vertical positioning accuracy is better than 1 m. The pseudo-range residual reduction reached 21%, and the availability met the requirements of APV-I. The addition of LEO can effectively expand the monitoring and service scope of BDSBAS and improve its performance.

How to cite: Wang, L., Yang, W., Huang, G., and Wang, Z.: LEO constellation-augmented SBAS and performance simulation analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4474, https://doi.org/10.5194/egusphere-egu24-4474, 2024.

Global Navigation Satellite System (GNSS) plays an increasingly important role in all walks of life. In order to meet the demands of different users, it is crucial to establish corresponding correct mathematical models, especially in the field of precise positioning and navigation. However, due to the spatiotemporal complexity of and limited knowledge on GNSS errors, some residual errors would inevitably remain even after being corrected with differencing and linear combination, empirical model correction, and traditional parameterization. These residual observation errors are referred to as unmodeled errors. In fact, the unmodeled errors have adverse impacts on high-precision GNSS positioning. However, most existing studies mainly focus on handling the part of systematic errors that can be adequately modeled and then simply ignore the part of unmodeled errors that may actually exist. To make a breakthrough in the precision and reliability of GNSS applications currently, this study focuses on the theory and method for processing the GNSS unmodeled errors based on the mathematical model compensation, including resilient functional model adjustment and resilient stochastic model optimization. Specifically, according to the significance and properties of unmodeled errors, the unmodeled-error-ignored model, unmodeled-error-corrected model, unmodeled-error-fixed model, unmodeled-error-float model, and unmodeled-error-weighted model are proposed, where the approaches of significance testing, geometry-free/based/fixed models, hemispherical/hierarchy maps, composite stochastic model, multi-epoch partial parameterization, and inequality and equality constraints are adopted. Ultimately, an unmodeled error processing flow that can adaptively adjust as the external conditions change is proposed, then one can obtain high-precision GNSS positioning solutions.

How to cite: Zhang, Z. and Li, B.: GNSS unmodeled error processing based on the resilient mathematical model compensation in high-precision GNSS positioning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4504, https://doi.org/10.5194/egusphere-egu24-4504, 2024.

In 2017, Hurricane Harvey made two consecutive landfalls along the Gulf Coast of the United States on August 25th and August 30th, resulting in extreme precipitation and widespread flooding. The impacts were devastating, causing the loss of over 80 lives and significant economic damage. To understand the evolution of Hurricane Harvey, we utilized data from densely distributed permanent GPS stations for precipitable water vapour (PWV) and rainfall tracking from the Tropical Rainfall Measuring Mission (TRMM) 3B42 product, examining spatial and temporal characteristics. Applying the empirical orthogonal function (EOF) method, we analyzed typical spatial patterns of PWV before and after Harvey's passage, revealing an overall increasing trend in atmospheric moisture at various sites across the study area. This trend was particularly pronounced in regions affected by the first and second landfalls. By comparing the time series of PWV with the distance between GPS stations and the hurricane's eye, we calculated their correlation, finding a negative relationship, especially when the hurricane is gradually approaching the GPS stations, the correlation coefficient can reach -0.6 or a higher value. Notably, when the distance between GPS stations and the hurricane's eye was approximately 1000 km, PWV rapidly increased. As the water vapour values rose to around 55mm and maintained an upward trend, sustained precipitation occurred. During the passage of the hurricane, PWV remained at elevated levels, resulting in maximum rainfall. Once the hurricane moved away, PWV values rapidly decreased.

How to cite: Zhan, W. and Liu, X.: Hurricane Harvey evolution process monitoring based on PWV, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4900, https://doi.org/10.5194/egusphere-egu24-4900, 2024.

To achieve highest precision in positioning and navigation, frequency transfer, ionosphere monitoring, etc. with global navigation satellite system (GNSS) data, the technique of uncombined precise point positioning (PPP) ambiguity resolution holds great potential. Unified data processing and reliable ambiguity resolution for multi-GNSS are essential prerequisites. Consequently, we propose a refined approach for quality control of multi-GNSS uncombined PPP ambiguity resolution employing a sequential Kalman filter. In addition to addressing the implementation of uncombined PPP ambiguity resolution, our focus extends to the selection of the reliable ambiguity datum, computational efficiency and refining the ambiguity resolution strategy. When quad-constellation GNSS data are processed, the results demonstrate that employing the sequential Kalman filter with OpenMP can enhance computational efficiency by approximately three times compared to the standard filter. Additionally, the sequential Kalman filter is also convenient to incorporate the ambiguity datum or the fixed ambiguity as a strong constraint and complete the post-residuals check for further validating the ambiguity resolution. Following the consideration of a set of indicators, e.g., arc length, cutoff angle, ratio value and success rate, for controlling reliable ambiguity resolution, the incorrect ambiguity fixing rate can be effectively reduced, especially during the convergence stage.

How to cite: Cao, X. and Soja, B.: Quality control for multi-GNSS uncombined PPP ambiguity resolution using a sequential Kalman filter, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5099, https://doi.org/10.5194/egusphere-egu24-5099, 2024.

Troposphere mapping function plays a very important role in space geodetic tropospheric correction, estimation, and application in meteorology. In the past decades, a series of mapping functions, such as NMF, GMF, VMF1 and VMF3, were successively developed for pursuing better mapping function performance. However, limited by modeling data source of numerical weather model, the mapping functions temporal-spatial resolutions are stuck in 6 h and 1°×1°, existing problems of bad applicability in extreme weather and regions with complex terrains. As the release of the generally acknowledged best global reanalysis ERA5, we have a chance to improve current mapping function modeling by taking full advantage of accuracy and temporal-spatial resolution (1h and 0.25°×0.25) of ERA5. In this contribution, we used the ERA5 reanalysis to establish the hourly site-wise Wuhan University Mapping Function (WMF) covering 1583 GNSS, VLBI and DORIS stations, and compared the accuracy and GNSS PPP performance of WMF with VMF3 at globally distributed stations. We found the significant accuracy improvements of WMF to VMF3 as well as the non-negligible vertical coordinate and ZTD difference biases between the two mapping functions.

How to cite: Zhou, Y., Lou, Y., Zhang, W., Gong, X., and Chen, G.: Improving troposphere mapping function by ERA5 for better space geodetic estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5179, https://doi.org/10.5194/egusphere-egu24-5179, 2024.

Annual signals derived from GNSS position time series have become a very important source of information on a variety of geophysical phenomena occurring in the closer or farther vicinity of the antennas. In this research, we investigated the vertical position time series obtained from the International GNSS Service (IGS) third reprocessing (repro3) and PPP times series from the Nevada Geodetic Laboratory (NGL). We selected 1019 globally distributed stations with good quality data and time spans longer than 5 years. First, we pre-processed them for outliers and identified the epochs of offsets using manual inspection. Then, we inspected the differences between the annual signals contained in both sets of time series. We noticed from the map of annual signal differences that there is a worldwide annual common mode in the series of “IGS-NGL” station position differences, with a median amplitude of the order of 2 mm, and a maximum around August. We hypothesized that those differences could be explained by different strategies of alignment to the reference frame, and in particular by the alignment of the NGL series to the scale of the ITRF. To investigate this, we produced additional series by applying different types of constraints to the daily repro3 normal equations. Namely, we compared four sets of time series: “IGS” which are the official repro3 solutions, aligned by no-net-rotation nor translation (NNR+NNT) constraints to the IGSR3 reference frame via the well-distributed IGSR3 core network; “IGa” which are repro3 solutions aligned by no-net-rotation, translation nor scale (NNR+NNT+NNS) constraints to IGSR3 via the same core network; “IGb” which are repro3 solutions aligned by NNR+NNT+NNS constraints to IGSR3 via the same daily sets of reference stations as used by Jet Propulsion Laboratory (JPL) to align their orbit and clock products; and “IGc” which are repro3 solutions aligned by NNR+NNT+NNS constraints to the IGb14 reference frame via the same set of reference stations as used by JPL. A comparison of these four sets of time series with the NGL PPP time series reveals that c.a. half of the systematic differences in vertical annual signals between IGS and NGL comes from the alignment of the NGL solutions in scale to the reference frame, and another half comes from the use of different station networks for the alignment to the reference frame.

How to cite: Bogusz, J., Rebischung, P., and Klos, A.: Differences in annual signals between IGS- and NGL-derived position time series: testing different strategies of alignment to the reference frame, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5351, https://doi.org/10.5194/egusphere-egu24-5351, 2024.

Smartphone-based positioning, navigation, and timing applications have been among the most popular topics within the GNSS community since 2016 when Google announced that raw GNSS observations are publicly available. However, achieving high positioning accuracy with smartphones is troublesome because of their specific limitations, such as the high noise level of observations, low protection against multipath, and discontinuities in carrier phase observations. Due to considerable discontinuities in carrier phase observations, code observations still play a crucial role in smartphone-based positioning applications. Subsequently, a realistic stochastic approach is mandatory to obtain the utmost positioning performance. This is especially true since the stochastic behavior of code observations for geodetic receivers and Android smartphones are quite different i.e., GNSS observations obtained from a smartphone are much noisier. The signal strength of GNSS signals collected from Android smartphones is also not very stable and is significantly lower when compared with geodetic receivers. Unlike observations obtained from geodetic receivers, no significant dependency between observation noise and elevation angle can be observed in smartphone observations. Therefore, conventional stochastic models, mainly based on the satellite elevation angle, are not enough to represent the stochastic behavior of smartphone observations. In this context, this study provides an enhanced stochastic approach for code observations obtained from Android smartphones. The corresponding approach includes a weighting scheme based on carrier-to-noise ratio (C/N0) values representing the signal strength of GNSS code observations. Besides, depending on their observation noises, this approach assigns different model coefficients for each constellation, which means differences between the navigation systems can be considered in adequate observation weighting. This approach also uses a robust Kalman filter method based on the IGG (Institute of Geodesy and Geophysics) III function to compensate for the effects of outliers and incorrectly weighted observations on the filtering performance. In this study, GPS, GLONASS, Galileo, and BeiDou code observations collected from a Xiaomi Mi 8 are processed to evaluate the performance of the proposed stochastic model. Firstly, observation noises are analyzed utilizing code-minus-phase observations, and the results show that GLONASS observations are considerably noisier than observations from other systems. Following, probability distributions of observation noises are evaluated to determine a realistic stochastic model, and the SIGMA- model with different coefficients for each constellation is adopted in this study. The Standard Point Positioning (SPP) method is also used to analyze the positioning performance of the proposed model. The results indicate that the proposed model can provide a 3D positioning accuracy of 1.5 m with the smartphone in static mode, which means the model improves the positioning accuracy by 36.9% compared to the conventional elevation-dependent stochastic approach. From these results, it can be said that the enhanced stochastic approach, based on C/N0 values and computing model coefficients for each constellation differently, can better reflect the stochastic behavior of code observations collected by Android smartphones.

How to cite: Bahadur, B. and Schön, S.: An enhanced stochastic approach for code observations collected from Android smartphones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5540, https://doi.org/10.5194/egusphere-egu24-5540, 2024.

The current operational GLONASS constellation comprises three different types of spacecraft: GLONASS-M, GLONASS-M+ and GLONASS-K1. All satellites transmit legacy frequency division multiple access (FDMA) signals in the L1 and L2 frequency bands, where individual satellites make use of different transmit frequencies. For the M+ and K1 satellites, an L3 code division multiple access (CMDA) signal was added. CDMA signals are transmitted at the same frequency but with satellite-specific ranging codes.

K2 is the latest generation of GLONASS adding CDMA signals in the L1 and L2 frequency bands. The first GLONASS-K2 satellite was launched in August 2023 and started signal transmission in early September 2023. Unfortunately, as of early 2024, no commercial GNSS receiver is able to track the L1 and L2 CDMA signals. Thus, measurements of a 30 m high-gain antenna are used for the characterization of these signals.

The FDMA and CDMA signals of GLONASS-K2 are transmitted via dedicated antennas separated by about 1 m. The differential baseline vector between the phase centers of the two antennas is estimated from an ionosphere- and geometry-free linear combination of L1 and L2 FDMA and L3 CDMA signals observed by a global tracking network. Furthermore, the FDMA L1/L2 ionosphere-free phase center offsets (PCOs) w.r.t. the center of mass are estimated. Both types of estimates are compared to PCOs obtained from the FDMA and CDMA navigation message.

The spacecraft body size of GLONASS-K2 is twice as large as the previous K1 generation. Due to the increased size and the more stretched shape of the satellite body, a proper modeling of the solar radiation pressure is of particular importance for precise orbit determination. Based on approximate dimensions of the satellite and default optical properties, an initial box-wing model is constructed. The performance of this model is evaluated by day boundary discontinuities, clock residuals, and the magnitude of estimated empirical orbit parameters. Finally, the latter quantities are used for an empirical tuning of the box-wing model.

How to cite: Steigenberger, P., Thoelert, S., and Montenbruck, O.: GLONASS modernization: initial characterization of the first K2 spacecraft, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5714, https://doi.org/10.5194/egusphere-egu24-5714, 2024.

Global Navigation Satellite System (GNSS) products are an integral part of a wide range of scientific and commercial applications such as precise orbit determination for low Earth orbit satellites, earthquake monitoring, GNSS reflectometry, tropospheric and ionospheric research, surveying and many more. These products, consisting of GNSS orbits, clocks, phase biases and more, are generated by the International GNSS Service (IGS) analysis centres by processing observations from a global network of ground stations to one or more GNSS constellations. The processing consists of a combined station position and GNSS satellite orbit determination using a least squares approach donated as global multi-GNSS processing.

Within the IGS 3rd reprocessing (repro3) campaign for the new release of the International Terrestrial Reference Frame (ITRF), Graz University of Technology (TUG), Working Group Satellite Geodesy has contributed as an Analysis Centre (AC) for global multi-GNSS processing. TUG has demonstrated high quality results on par with other ACs using its self-developed geodetic processing software Gravity Recovery Object Oriented Programming System (GROOPS). Within GROOPS the global multi-GNSS processing uses the raw observation approach. The raw observation approach uses all measurements as observed by the receivers without explicitly creating any linear combinations or differences. This allows the information contained in each individual observation to be fully exploited.

GROOPS has been shown to be capable of global multi-GNSS processing using GPS, Galileo and GLONASS. With more publicly available metadata for the BeiDou system, GROOPS has been further developed to use BeiDou within global multi-GNSS processing using the raw observation approach. Therefore, in this contribution we present the improvements in GROOPS global multi-GNSS processing using BeiDou and discuss the quality of the resulting orbit, station position time series, clock and phase bias products.

How to cite: Dumitraschkewitz, P., Mayer-Guerr, T., Krauss, S., Oehlinger, F., Suesser-Rechberger, B., Strasser, A., and Tieber-Hubmann, C.: Analysis of the Global Multi-GNSS Network Processing Raw Observation Approach using BeiDou, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5987, https://doi.org/10.5194/egusphere-egu24-5987, 2024.

With the continuous development and updates of GNSS systems, an increasing number of satellites now emit triple-frequency signals. Currently, research on triple-frequency positioning is predominantly focused on the Asian region, with limited attention given to the multi-frequency positioning performance in Europe. This study utilizes triple-frequency signals from BDS-3/GPS/Galileo satellites and employs observations from EUREF Permanent GNSS Network (EPN) to evaluate the performance of Precise Point Positioning Ambiguity Resolution (PPP-AR) and Atmosphere-augmented Real-Time Kinematics (PPP-RTK) modes in the European region. We calculate Uncalibrated Phase Delay (UPD) using 46 EPN stations and perform PPP-AR on all 138 stations to derive ionospheric and tropospheric delays. The fixing residuals of EWL/WL/NL UPD achieve 99.9%/98.2%/84.9% for BDS, 100.0%/97.1%/89.9% for Galileo, and 99.9%/94.9%/88.7% for GPS satellites within 0.15 cycles, respectively. Double and triple-frequency PPP-AR 68^th percentile results achieve 7.0/3.5 and 7.0/6.0 minutes for horizontal and vertical components using GPS/Galileo/BDS constellations. Additionally, the ionospheric delays derived from double and triple frequencies show only slight differences, measured at the centimeter-level among GPS/Galileo/BDS constellations. Relying on atmospheric delay augmentation, i.e., PPP-RTK, we further analyze the positioning performance under varying inter-station distances from 100 km to 400 km. The triple-frequency brings about a 5% improvement in convergence for BDS and Galileo satellites with respect to double-frequency solutions, while only slightly enhancing GPS satellites. Combining GPS/BDS/Galileo achieves nearly instantaneous convergence even at distances up to 400 km. Overall, the European region using GPS/Galileo/BDS constellations can achieve rapid positioning by triple-frequency signals, and instantaneous convergence can be achieved for double and triple-frequency solutions when atmosphere delays are implemented.

How to cite: Cui, B., Du, S., Jiang, X., Wang, L., Huang, G., Ge, M., and Schuh, H.: Multi-GNSS double/triple-frequency PPP-AR/RTK performance evaluation in the European region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7066, https://doi.org/10.5194/egusphere-egu24-7066, 2024.

Global Navigation Satellite System (GNSS) can provide accurate absolute position but requires fine observational conditions. In urban canyons and indoor scenes, GNSS suffers from severe performance degradation and is even unavailable. Thereby, to provide ubiquitous positioning solutions, it is very necessary to incorporate other complementary sensors. A popular option is the 3D LiDAR sensor, which emits its own light and thus is robust to illumination variation. 3D LiDAR sensors can efficiently capture plentiful point patterns of the ambient environment. Besides perception tasks, the obtained point clouds are also used for relative localization in a registration fashion, known as the LiDAR odometry (LO). Generally, LO is based on structural information, where edge and planar features are extensively exploited.

In this work, we propose a novel method aiming to efficiently extract planes from the sparse and noisy 3D LiDAR point clouds. To fully exploit the scanning pattern of this sensor, our method follows a framework of point-to-line-to-plane. The point cloud is firstly projected onto a range image by investigating the azimuth and elevation of each point. In the point-to-line stage, consecutive flat points in a column are grouped into line segments, where a new flat point detector is introduced. In the line-to-plane stage, we extend the classical line extraction method, i.e., Douglas-Peucker algorithm, to find planes in line segments. Considering the over-segmentation caused by occlusion and deformation, we finally conduct region growing and merging to acquire more complete results. Most importantly, we bridge the measurement noise model and the parameter uncertainty via error propagation to determine reasonable thresholds throughout our method.

We test the proposed method on datasets collected by various LiDAR sensors. The experiments are conducted in indoor scenes and urban scenes, which contains abundant planar objects such as walls and building facades. Three point-level metrics, namely positive predictive value (PPV), true positive rate (TPR) and F1 score, are employed for quantitative evaluation. The average PPV, TPR, F1 of the proposed method are 89.92%, 86.38% and 88.11%, respectively. The results show that the proposed method is able to recover the dominant planar structure, which is valuable to LO. Moreover, compared to the rotation rate of the LiDAR sensor, which is generally set to 10 Hz, the average runtime of the proposed method is 15.6 ms/frame, so it is qualified for online operation.

How to cite: He, L. and Li, B.: Extracting planes from sparse 3D LiDAR data with measurement noise model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7274, https://doi.org/10.5194/egusphere-egu24-7274, 2024.

The last decade has brought us rapid developments in low-cost GNSS receiver technology. The performance level of such receivers can be unexpectedly close to that offered by high-grade geodetic ones. Nevertheless, the further growth of applicability for these types of devices still needs a more detailed analysis of observations.

Motivated by such developments, we focused on the quality of multi-GNSS pseudorange data acquired by three low-cost receivers: u-blox ZED-F9P, Septentrio Mosaic-X5, and Skytraq PX1122R and its comparison with reference values adopted from the geodetic receiver - Trimble Alloy. We investigated two main characteristics of code data – observation noise and correlation in the time domain. In contrast to a typical pseudorange data analysis based on a single-receiver scenario performed with multipath combinations, we extended our tests with data acquired at zero-baseline formed of homogeneous receiver pairs. Such an approach allowed us to analyze the quality of more realistic data, i.e., affected by multipath, and data with multipath removed through between-receiver differencing.

The investigations revealed significant discrepancies in data quality between selected low-cost receivers and recorded signals. Generally, the pseudorange noise was the lowest for Septentrio Mosaic-X5, whereas the noisiest observations were found for Skytraq PX1122R. More interestingly, considering the deviation of code data, Septentrio Mosaic-X5 outperformed the geodetic receiver. On the other, we noted that the low-cost receivers' measurements are noticeably correlated in the time domain, even for between-receiver differences of multipath combinations. 

How to cite: Sieradzki, R., Paziewski, J., Stepniak, K., and Banach, J.: Quality analysis of the low-cost GNSS receiver pseudorange data. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7563, https://doi.org/10.5194/egusphere-egu24-7563, 2024.

Satellite measurements are of great importance in monitoring the deformations of the Earth's crust, which have resulted from the impact of natural forces and human activities. The example of human activities that cause surface deformations is mining activity. Geodetic monitoring of deformations and displacements caused by mining activities is of key importance for ensuring the safety of people's activities on the surface. To perform geodetic surveys, the establishment and maintenance of a local control network with reference stations of the highest accuracy is essential. The stability of the reference network should be constantly monitored and controlled to assessed the quality of the actual solutions of the reference stations and the reliability of the entire network.

The goal of this study is to perform the stability analysis of the GNSS permanent stations located at a mining area where significant mining activity is observed. Daily coordinate solutions obtained from over 2 years of GNSS data processing performed in Bernese GNSS Software v.5.4 are collected and analyzed. The results show that the station coordinates are characterized by high stability over time. The standard deviation is below 2 mm for the horizontal components and below 3.5 mm for the height for all stations. The results also reveal a continuous movement of one station located near a mine shaft. In addition, a jump in the station coordinates was registered which was caused by a mining shock on the 7^th of April, 2022. As a results, the station moved 50 mm northwards, 48 mm in the eastwards and 88 mm downwards.

How to cite: Stepniak, K., Wielgosz, P., Kurpinski, G., Seta, M., Paziewski, J., and Sieradzki, R.: Stability analysis of the GNSS reference stations for displacement monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8163, https://doi.org/10.5194/egusphere-egu24-8163, 2024.

Precise positioning with Global Navigation Satellite Systems (GNSS) requires a significant amount of effort for deliberate modeling of various effects on transmitted signals. Among these error sources, the ionosphere has a special place with its daily variations of total electron content (TEC), disrupting signals and degrading positioning performance. Some attempts to remove ionospheric effects, such as forming ionosphere-free combinations in Precise Point Positioning (PPP) can cause the amplification of observation noise. Using data-driven Global Ionospheric Maps (GIM) from GNSS analysis centers could be an alternative solution. However, precise GIMs are available after 10-11 days from the observation campaign and are not accessible in real-time. Therefore, the demand for real-time and reliable solutions is a hotspot research field in precise positioning.

Real-time PPP (RT-PPP) with undifferenced and uncombined observables can convert the limitations of the ionosphere into an opportunity to make GNSS a versatile tool that is capable of monitoring the ionosphere and achieving high-accuracy positioning in real time. Our work focuses on the performance of ionospheric delay estimation with RT-PPP, using a newly developed real-time correction service, Galileo High Accuracy Service (Galileo HAS). First, International GNSS Service (IGS) stations from low, mid, and high latitudes for day-of-year 140,141, and 143 were selected for RT-PPP data processing. Next, we performed station-based Vertical TEC (VTEC) modeling and compared the results with GIM from the Centre for Orbit Determination in Europe (CODE). In addition, we processed the data from two receivers co-located on the rooftop of the University of Liège Sart Tilman campus B5a building. Finally, characteristics of the between-receiver single difference of estimated ionospheric delays are presented to assess the precision of slant ionospheric delays.

How to cite: Gul, C. and Warnant, R.: VTEC Estimation Performance of Real-Time PPP with Galileo High Accuracy Service, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8485, https://doi.org/10.5194/egusphere-egu24-8485, 2024.

To ensure an accurate and precise position in GNSS applications, phase center corrections (PCC) have to be taken into account. PCC are antenna and frequency dependent correction values. They describe the distance along the line-of-sight direction between the electrical phase center, where the GNSS signal is received, and the antenna reference point. In Melbourne-Wübenna or code-minus-carrier linear combinations, which are often used in highly precise GNSS applications, the codephase of the GNSS signal plays a key role. Similar to the PCC, also correction values for the codephase observation exist, called codephase center corrections (CPC), also known as group delay variations (GDV). The definition of CPC as well as their estimation process with a robot in the field is similar as for PCC.

The team at Institut für Erdmessung is optimizing the established absolute antenna calibration approach for estimating CPC and PCC for multi GNSS signals in terms of repeatability, noise reduction and multipath impact. In this calibration process, an antenna under test (AUT) is precisely tilted and rotated around a fixed point in space by using a robot. A nearby reference station allows the calculation of time differenced single differences (dSD), which are used to estimate absolute CPC and PCC with spherical harmonics of degree and order 8. The pattern quality and also the repeatability of this approach depends, among other effects, on the observation noise of the GNSS signals.

In this contribution, a detailed study about the influence of observation noise on the estimated patterns is presented. To this end, dSD are simulated based on an existing pattern and the robot positioning. The dSD are modified before the estimation process by polluting them with different kind and magnitude of noise. The estimated patterns are compared using e.g. the root-mean-square or the absolute difference between two runs. Our analysis shows, that 18% of the white noise magnitude is reflected in the repeatability of the pattern estimation in terms of absolute differences between two calibration runs. 

How to cite: Breva, Y., Kröger, J., Kersten, T., and Schön, S.: How Observation Noise impacts the Estimation of Codephase- and Phase-Center Correction with a Robot in the Field, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9080, https://doi.org/10.5194/egusphere-egu24-9080, 2024.

With the release of Android 7.0 in 2016, it became possible to retrieve raw GNSS measurement data from Android smartphones. The capability of directly accessing the GNSS measurements allows, among other things, a more reliable estimation of the user’s position by applying external correction data and sophisticated algorithms. However, the GNSS hardware in smartphones is simple and cost-effective, clearly impacting the quality of the measurements. This results in higher levels of noise and frequently occurring effects like outliers, cycle slips, and multipath.

Precise Point Positioning (PPP) is an excellent technique for smartphone positioning due to its features and flexibility. PPP is characterized by applying precise satellite products (orbits, clocks, and biases) and complex models and algorithms to estimate the user's position. Due to this concept, PPP does not require nearby reference stations because these precise satellite products are globally valid. Furthermore, the concept of PPP allows the development of resilient and flexible algorithms, providing a remarkable advantage considering the challenging nature of GNSS measurements from smartphones.

The Galileo High Accuracy Service (HAS) started its service in January 2023. This service is free of charge and provides real-time corrections to the broadcasted navigation message for GPS and Galileo over signal-in-space (E6 frequency) and the internet. The design of the Galileo HAS makes it particularly interesting for low-cost GNSS and smartphone applications. According to the system operator, the Galileo HAS enables position accuracies at the decimeter level, depending on the PPP processing algorithms and the GNSS hardware of the user.

This contribution addresses challenges originating from the ultra-low-cost smartphone equipment and suggests suitable solutions (e.g., data-cleaning algorithms). Furthermore, PPP results with state-of-the-art smartphones (e.g., Google Pixel 7) and the Galileo HAS are presented. The corresponding PPP calculations are performed with our open-source software raPPPid using the uncombined PPP model with ionospheric constraint in quasi-real-time settings. The results demonstrate that it is possible to achieve position accuracies down to the decimeter level with the Galileo HAS under good conditions.

How to cite: Wareyka-Glaner, M. F. and Möller, G.: Smartphone PPP with the Galileo High Accuracy Service, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9332, https://doi.org/10.5194/egusphere-egu24-9332, 2024.

The High-rate Global Navigation Satellite System (HR-GNSS) instruments are devices that can detect seismic wave arrivals and measure ground displacement generated by an earthquake with high precision. By integrating HR-GNSS data with other sensors and models, we can improve the accuracy of earthquake assessments and provide valuable information for early warning and disaster preparedness. Our focus lies in developing deep-learning models leveraging HR-GNSS waveform data. These models significantly empower our capacity to detect and estimate the magnitude of large earthquakes. Yet, the rapid analysis of HR-GNSS data using deep learning algorithms remains a current challenge. To overcome this challenge, it is crucial to have access to large and high-quality datasets. Since the presence of noise in GNSS recordings particularly impacts data quality, especially for earthquakes measuring below magnitude 7, our training of Deep Learning (DL) models primarily relies on the data available from the largest earthquakes. This comes with a trade-off as these events provide a limited dataset because they occur less frequently, making the data poorly representative for model training. To overcome this limitation, we have used both synthetic earthquake signals combined with synthetic and real noise for model training, validation, and testing. Our investigation explores how diverse factors, such as noise, earthquake magnitude, station density, distance from the epicenter, and duration of the signal, affect the performance of our models. We aim to generalize the detection methodology and magnitude estimation for real-time monitoring of large earthquakes across diverse tectonic regions. The DL models proposed in this work will be integrated as complementary algorithms to the open-source Python package SAIPy.

How to cite: Quinteros-Cartaya, C., Quintero-Arenas, J., Faber, J., Köhler, J., and Srivastava, N.: Large Earthquakes Monitoring using High-Rate Global Navigation Satellite System Data through a Deep Learning approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9416, https://doi.org/10.5194/egusphere-egu24-9416, 2024.

The integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) has been widely used in civilian vehicular navigation, which can provide precise position and attitude for vehicle control and trajectory planning. Nevertheless, the accuracy of the GNSS/INS combined system inevitably diminishes when GNSS signals are frequently obstructed or interrupted, leading to an insufficient number of GNSS observations to mitigate INS drift. To improve the performance of the GNSS/INS system in such GNSS-compromised environments, nowadays the mainstream solution is further combining other sensors, i.e., Lidar, Camera or odometer. The crux of these approaches lies in introducing external pose information to supplement the missing GNSS signals. However, more sensors mean more complex system, as well as higher cost.

In many so-called GNSS-compromised environments (i.e., routes under trees, urban canyons), GNSS signals do not completely vanish but are characterized by reduced quantity and weakened quality. This raises the question: Has the GNSS information under harsh environments been completely utilized? The answer obviously is no. To realize the fully utilization of the GNSS information, this work studied a Quality Control-based Multi-Antenna GNSS/INS Tightly Integrated Model (QC-MAINS). The tight integration of INS and the multiply antennas located at different positions on a vehicle can explore potential GNSS observations as much as possible. Furthermore, the low-quantity GNSS observations can be detected and isolated through cross-checks with multiple antennas. Experiment results show that the horizontal position root-mean-square errors (RMSEs) are reduced by about 42, 54, and 48% for three harsh routes, respectively.

How to cite: chen, G. and li, B.: QC-MAINS: A Quality Control-based Multi-Antenna GNSS/INS Tightly Integrated Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9540, https://doi.org/10.5194/egusphere-egu24-9540, 2024.

Low Earth Orbit (LEO) satellites have been discussed to augment the traditional GNSS-based Positioning Navigation and Timing (PNT) service in real-time, in which the high-precision real-time LEO satellite clock products are the prerequisite. As the complicated systematic effects contained in the LEO satellite clock estimates hamper high-precision mid- to long-term clock prediction, a typical and efficient method to obtain high-precision real-time LEO satellite clocks is Kalman-filter-based clock estimation with short-term prediction. The strong correlation between the LEO satellite clock and the radial orbital component, however, leads to poorer clock precision than needed. In this contribution, reduced-dynamic LEO satellite orbits are first estimated in batch least-squares adjustment with high accuracy in near real-time. The short-term predicted orbits are introduced and constrained during the Kalman-filter-based clock estimation process. The variance-covariance matrix of the introduced orbital errors is carefully considered and tested for different sets of values in the radial, along-track and cross-track directions. One week of GPS data from the Sentinel-3B onboard receiver in 2018 were used for the purpose of validation. When introducing high-accuracy predicted orbits at the first 5 min, i.e., with an accuracy of 3.33, 1.78 and 2.03 cm in the along-track, cross-track and radial direction, respectively, the precision of the estimated clocks can be improved from 0.268 ns to 0.233 ns, with an improvement of 13.06%. Moreover, the Signal-In-Space Range Error (SISRE) of the LEO satellite to the Earth can be improved from about 9.59 to 7.62 cm after introducing the predicted orbits. The results have demonstrated that the proposed method helps to improve the precision of the real-time LEO satellite clock estimates.

How to cite: Xie, W., Su, H., Wang, K., Zou, M., and Yang, X.: LEO satellite clock determination in real-time with predicted orbits introduced, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9735, https://doi.org/10.5194/egusphere-egu24-9735, 2024.

    The precise point positioning (PPP) timing service has been proposed in recent years and has been demonstrated to achieve sub-nanosecond accuracy. As an integral part of BDS-3, the PPP services via b2b signals (hereafter referred to as PPP-B2b) are provided by BDS-3 GEO satellites with better than decimeter-level accuracy for users around China. In this research, a high-precision timing receiver based on the BDS-3 PPP-B2b service has been established, which obtains the local clock offset with respect to Beidou Time (BDT) through PPP time transfer, and controls the local clock of the receiver according to the clock offset. The 1PPS output of the receiver clock is adjusted to the BDT.

    First, the performance of the time transfer is evaluated. The experimental results of two links show that a level of 0.20 ns can be achieved with a convergence time of 20 minutes. Second, the timing performance of the receiver is examined using UTC(NTSC) as a reference. The timing accuracy is better than 0.2 ns in terms of standard daily deviation, and the frequency stability could reach about 2 × 10-14 in 1 day. Third, we compare the time synchronization performance between two receivers using rubidium and a crystal oscillator as the external clock, respectively. When the average time exceeds 10s, the time variance and the Allan variance of the scheme using rubidium clocks is better than the scheme using crystal oscillators.

How to cite: Lyu, D., Pan, J., Liu, F., and Wang, J.: Performance evaluation of a high-precision timing receiver based on the BDS-3 PPP-B2b service, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9955, https://doi.org/10.5194/egusphere-egu24-9955, 2024.

The spatial gradient of Total Electron Content (TEC) demonstrates a notable correlation with occurrences of ionospheric scintillation and degradation in Global Navigation Satellite Systems (GNSS) performance. To assess the degree of the ionosphere perturbation over China, we utilize two ionospheric disturbance indices, i.e., the Gradient Ionospheric Index (GIX) and Rate of Total Electron Content Index (ROTI). Analyzing data from over 230 GNSS stations in China, we investigated the consistency and differences in ionospheric irregularities as indicated by GIX and ROTI, focusing particularly on the impact of horizontal gradients of vertical TEC (VTEC) on GNSS positioning. Experimental results indicate that the occurrence of ionospheric irregularities and the degradation of kinematic Precise Point Positioning (PPP) performance are more closely aligned with the strong horizontal VTEC gradients indicated by GIX, both in evolutionary characteristics and temporal variations, compared to ROTI. Notably, severe position errors and strong horizontal gradient of VTEC were consistently observed throughout the entire duration of St. Patrick's Day storm, including the prestorm period (06-16 UT on 16th March), the main phase (06-13 UT on 17th March), and the late recovery phase (05-14 UT on March 19th). Furthermore, significant PPP degradation (3-D PPP>1m) occurred not only preceded the intensification of ROTI but also in periods of stable ionospheric conditions indicated by ROTI values below 0.4 TECU/min, particularly on March 19th. The degradation primarily occurred in regions with significant irregularities, especially when the horizontal VTEC gradient surpassed a specific threshold (GIXx, P95+ > 55 mTECU/km, 1 mTECU/km = 10⁻³ TECU/km). These findings indicate the potential of GIX in effectively capturing the effects of ionospheric disturbances and enhancing the safety and accuracy of GNSS navigation and positioning.

How to cite: Fu, Z., Shen, X., Wang, N., and Li, A.: Ionospheric Disturbance Index for GNSS Precise Positioning: Comparison between GIX and ROTI over China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10125, https://doi.org/10.5194/egusphere-egu24-10125, 2024.

The Global Navigation Satellite System (GNSS) carrier phase and pseudorange measurements exhibit different levels of accuracy, and there are differences in observation accuracy among different observation systems, satellites and receivers. A reasonable stochastic model is crucial for improving Precise Point Positioning (PPP) results. We propose a method for constructing stochastic models in PPP using triple-frequency observations. By forming geometry-free and ionosphere-free (GFIF) combinations and subtracting inter-frequency clock biases (IFCB), the accuracies of phase and pseudorange observations are evaluated using residuals to determine weights. Data from 278 International GNSS Service (IGS) stations on September 1, 2023 are processed to calculate the accuracies of carrier phase and pseudorange observations in GPS, Galileo, and BDS-3 systems. The results show that the accuracies of carrier phase observations among different receivers are relatively close, while there are obvious disparities in pseudorange observation accuracies. The average Root Mean Square (RMS) of Galileo carrier phase and pseudorange observations is the smallest among three systems. In BDS-3 system, there are certain disparities in carrier phase observation accuracies among different types of satellites. Specifically, for LEICA and SEPT receivers, the average RMS of IGSO satellites is greater than that of MEO satellites. To validate the effectiveness of the proposed model, the static and dynamic PPP solutions are conducted using 80 IGS stations. The results demonstrate that compared to the empirical model, the proposed model shortens the average convergence time by 22.5%, 31.5%, and 24.1% in static PPP for GPS, Galileo, and BDS-3 systems, respectively. At 0.5h, the average three dimensions (3D) positioning accuracies improve by 2.1, 3.5 and 2.9 cm, respectively. For dynamic PPP within the range of 0.5 to 1 hour, the average RMS of the 3D positioning are reduced by 2.6, 4.7 and 3.0 cm, respectively. Furthermore, for multi-system PPP of GPS+Galileo+BDS-3, the average convergence time and positioning accuracy are also improved with the proposed stochastic model.

How to cite: Xiao, J. and Li, H.: Construction and Evaluation of the Stochastic Model in Precise Point Positioning based on Triple-Frequency Geometry-Free Combination, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10780, https://doi.org/10.5194/egusphere-egu24-10780, 2024.

To achieve the 1 mm accuracy and 0.1 mm/year stability of Terrestrial Reference Frame (TRF) required by Global Geodetic Observing System (GGOS), various surface displacements have to be precisely modeled. Non-tidal atmosphere, ocean, and hydrology loading displacements are major sources causing stochastic and systematic effects in station coordinates estimated by space geodetic techniques such as Global Navigation Satellite Systems (GNSS). Studies show that the correction of non-tidal loading displacements in GNSS station coordinate time series reduces coordinate repeatability and thereby improves stability. Currently, non-tidal loading displacements are corrected on the observation level in Very Long Baseline Interferometry (VLBI) data analysis for standard IVS (International VLBI Service for Geodesy and Astrometry) products, but not in GNSS data analysis. We applied the ESMGFZ non-tidal atmosphere and ocean loading displacements (NTAOL) on the observation, normal equation, and parameter levels for global GNSS network solutions in 2005-2019. We demonstrate that the station coordinate repeatability can be significantly improved when correcting NTAOL displacements, especially in the up component where a reduction of 20-30% can be observed at middle and high latitudes. Whereas for other geodetic parameters, such as satellite orbits, Earth Rotation Parameters (ERP), and geocenter motion, however, the impact of NTAOL displacements is insignificant. The difference between applying NTAOL displacements on the observation to that on the normal equation level is on sub-daily scales and we show that most of these differences are absorbed by receiver clocks. As for the differences of applying NTAOL on the observation and on the parameter levels, small but systematic effects on the horizontal components of station coordinates appear, which are mainly due to network alignment. We also demonstrate that the a priori tropospheric delay modeling affects the non-tidal atmosphere loading signals in station coordinates, i.e., when applying empirical tropospheric delay models, e.g., GPT3, NTAL correction introduces a significantly smaller improvement of station coordinate repeatabilities (below 5% in up component). Hence, we recommend always using discrete tropospheric delay products from Numerical Weather Model (NWM) as a priori values when NTAL corrections are applied.

How to cite: Wang, J., Dobslaw, H., Balidakis, K., Glaser, S., Männel, B., Ge, M., Heinkelmann, R., and Schuh, H.: Applying non-tidal atmosphere and ocean loading corrections on the observation, normal equation, and parameter levels in GNSS data analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12883, https://doi.org/10.5194/egusphere-egu24-12883, 2024.

The Galileo for Science Project (G4S_2.0) is an ongoing project funded by the Italian Space Agency that has several goals in the field of Fundamental Physics by exploiting the Galileo-FOC Constellation and, in particular, GSAT0201 (E18) and GSAT0202 (E14), the two satellite in elliptical orbit. By exploiting the accuracy of the atomic clocks on board the satellites, in particular of the clock-bias estimated in the process of data reduction of the tracking observations during a Precise Orbit Determination (POD), it allows on the one hand to measure the gravitational redshift, constraining the Local Position Invariance (LPI) and, on the other hand, to place constraints on the possible presence of dark matter in our galaxy in the form of Domain Walls. A fundamental point is obtaining a suitable satellite orbit solution by performing an accurate POD. In this context, the activities carried out with the Bernese code will be presented.

How to cite: Cinelli, M., Sapio, F., Lucchesi, D., Visco, M., Di Marco, A., Fiorenza, E., Lefevre, C., Loffredo, P., Lucente, M., Magnafico, C., Peron, R., Santoli, F., and Vespe, F.: The Galileo for Science project: Bernese GNSS Software applications for Fundamental Physics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13425, https://doi.org/10.5194/egusphere-egu24-13425, 2024.

With more continuous Global Navigation Satellite System (cGNSS) network stations becoming available around the world and with improved data processing techniques, it is possible to observe and model subtle motions in the Earth’s crust that were previously undetectable. Critical to studying these subtle motions is understanding the contributions of various signals mixed into the cGNSS time-series, for example non-tidal atmospheric and ocean loading (NTAOL) and hydrologic loading. We investigate the effect that atmospheric and surface mass loading has on the stochastic properties of GPS time series around the Great Lakes (GL) region of the U.S. and Canada. This region is ideal for studying these effects because it is covered by a dense network of GPS stations and it is known to be affected by significant hydrological loading due to water level changes in the GL. We use readily available NTAOL and hydrologic loading models to remove these signals from the cGNSS time-series and track the variance changes in the residual time-series in order to quantify the effect of each loading component. In order to assess whether the loading models fully capture the full magnitude of displacement we also perform common mode filtering in order to extract the remaining spatially correlated signal. We estimate the stochastic parameters (white noise amplitude, power law amplitude and spectral index) and compare between the raw, loading corrected, and filtered loading corrected time series in order to evaluate the contribution of the different loading signals to the time series noise properties. The outcomes of this study will help validate existing loading models and where improvement may be needed. Results will also support GNSS data providers in assessing the quality of available GNSS stations for use in scientific and surveying applications.

How to cite: Krcmaric, J. and Kreemer, C.: Evaluating the effect of atmospheric and surface mass loading on the stochastic properties of GPS time series in the Great Lakes region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13929, https://doi.org/10.5194/egusphere-egu24-13929, 2024.

Multi-GNSS techniques are continuously improving themselves and strengthening their infrastructures. In response, companies producing GNSS hardware and software are enhancing their products with refined techniques, leading to improved positioning precision. In line with these advancements, efforts are being made to enhance algorithms which predict the accuracy of GNSS positioning. Following these improvements, users who conduct field works or optimize networks would want to know about the improved accuracy of GNSS positioning. After GPS, the new members of the GNSS are continually improving their infrastructure. To achieve the desired coordination in terms of a common processing ground among these techniques remains a challenge. This is particularly crucial for organizations developing GNSS software. For instance, NASA JPL has been making efforts to produce a PPP-AR solution. The transition from GIPSY OASIS II to GIPSY-X allowed for the processing of combined GPS, GLONASS, and GALILEO data in a JPL experiment in 2019. However, today, there is yet no AR infrastructure for the combined solution of all techniques beyond the float solution. In this study, we investigated whether JPL's 2019 AR solution is still applicable to current research. Initial attempts yielded promising results as we conducted experiments with 50 points from the IGS MGEX network's shared data. By comparing the differences between the 2019 PPP-AR solution and the 2019 and 2023 float solutions, we found that the discrepancies are not significantly large in today's GNSS accuracy. We concluded that an accuracy model generated from the 2019 AR solution could provide a satisfactory estimate for today's users. Initial findings indicate that GPS performs well with the combination of three techniques, while the synergy of techniques significantly contributes to the improvement of the vertical positioning. The accuracy produced by the combination is witnessed to be dependent on the observation period, emphasizing the need for attention from those conducting campaign measurements in this context.

How to cite: Çetin, D., Sanli, D. U., and Ogutcu, S.: An attempt to model the accuracy of GNSS Positioning from GIPSY-X PPP-AR Referring to  JPL’s 2019 Experiment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14958, https://doi.org/10.5194/egusphere-egu24-14958, 2024.

During 2010 and 2011, BORTAS (Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites) experiment has been carried out over northern America and Canada with the aim of studying the air masses which contain emission products from boreal wildfires [1]. In this study, the goal is to understand the potential of ground-based GNSS sensors in monitoring fire plumes. In relatively stable weather condition, strong correlation between GNSS-ZTD (Zenith Total Delay) and PM (Particulate Matter) can be found [2]; reflecting ZHD (Zenith Hydrostatic Delay) the delay caused by the standard dry atmosphere, the delay caused by PM is included in ZWD (Zenith Wet Delay) [3]. Referring to the 2011 BORTAS experiment, data from GNSS ground-based sensors located in the plume trajectories have been analysed. To evaluate the GNSS approach sensitivity, fresh plumes, aged plumes, and background plumes have been studied considering different flights.

[1] Palmer, P. I., Parrington, M., Lee, J. D., Lewis, A. C., Rickard, A. R., Bernath, P. F., ... & Young, J. C. (2013). Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) experiment: design, execution and science overview. Atmospheric Chemistry and Physics, 13(13), 6239-6261.

[2] Guo, M., Zhang, H., & Xia, P. (2020). A method for predicting short‐time changes in fine particulate matter (PM2. 5) mass concentration based on the global navigation satellite system zenith tropospheric delay. Meteorological Applications, 27(1), e1866.

[3] Guo, J., Hou, R., Zhou, M., Jin, X., Li, C., Liu, X., & Gao, H. (2021). Monitoring 2019 forest fires in southeastern australia with GNSS technique. Remote sensing, 13(3), 386.

How to cite: Mascitelli, A., Aruffo, E., Chiacchiaretta, P., Realini, E., Gatti, A., Fumagalli, A., and Di Carlo, P.: Boreal forest fire monitoring by GNSS, referring to the 2011 BORTAS experiment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17605, https://doi.org/10.5194/egusphere-egu24-17605, 2024.

PPP-RTK is a GNSS-based method for absolute positioning in real-time that has gained importance in recent years. Unlike network RTK, correction data can be provided as broadcast to an unlimited number of users.

In Germany, a nationwide PPP-RTK service is about to be implemented. The service is being set up in cooperation with the surveying authorities of the federal states and the Bundesamt für Kartographie und Geodäsie (BKG). Two independent computing facilities at BKG and Zentrale Stelle SAPOS® guarantee a high level of reliability of the service. Although both facilities use the GNSS processing software GNSMART, the analysed reference station networks differ. The service is currently being tested with various scenarios and setups. Due to its redundancy, different configurations can be analysed in parallel.  The service performance is monitored in a nationwide monitoring network. Three of these monitoring stations are equipped with two identical receivers that are connected to the same antenna and can be used for detailed comparisons of the two service setups. We present results how the network configuration impacts the service performance with a special focus on the present ionosphere activity.

How to cite: Rost, C., Riedel, F., Freitag, M., Vennebusch, M., Winter, V., Stockhaus, T., Trautvetter, C., and Knöfel, C.: The influence of the PPP-RTK reference station network configuration on position accuracy and real-time performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18577, https://doi.org/10.5194/egusphere-egu24-18577, 2024.

The RING (Rete Integrata Nazionale GNSS) is a research infrastructure developed for accurately measuring deformations at different spatial and temporal scales in the Eurasia-Africa plate boundary region (Avallone et al., 2010). Currently, the RING network (http://ring.gm.ingv.it/) is composed of 280 real-time transmitting remote sites, 70% of which are now equipped with full-GNSS (GPS, Galileo, Glonass and Beidou) instrumentation. The data streaming, in standard RTCM v.3 format, from these sites to the acquisition centre in southern Italy (Sezione Irpinia, Grottaminarda, AV) is managed by a tuned Ntrip Caster (https://igs.bkg.bund.de/ntrip/bkgcaster).

The typical magnitude of the strongest events that occurred in the last century in this region (5.5-7) should require high accuracy (2-3 cm) GPS/GNSS time series to properly observe both static and dynamic coseismic displacements and, then, to properly model the earthquake source. Furthermore, the detection of any afterslip or, in general, any transient deformation should require even better accuracy (< 2 cm). The real-time GPS/GNSS data analysis has been implemented by means of the RTPPP software developed by GFZ (Ge et al., 2012). This software allows the determination of various Precise Point Positioning products with increasing accuracy (standard PPP, PPP with ambiguity resolution [PPP-AR], and PPP with regional augmentation [PPP-RA]). We performed some preliminary investigations on different (limited in time) datasets and we compared GPS-only and full-GNSS results. In the case of GPS-only PPP-RA solutions, the accuracies estimated on 24-hour data for the whole network amount up to 1.7 cm and 6 cm for the horizontal and vertical components, respectively. In the case of full-GNSS solutions, the same approach (PPP-RA) allowed an improvement of about 22% on both horizontal and vertical components (1.3 cm and 4.6 cm). Furthermore, we compared both GPS-only and full-GNSS solutions with another method, i.e. by using a short-term accuracy analysis. Using 60-s or 120-s sliding windows, that should better simulate the time span for detecting coseismic displacements, we can achieve 0.5 cm and 1 cm for horizontal and vertical components, respectively, for GPS-only solutions, and 0.3 cm and 0.5 cm for full-GNSS ones.

Finally, for a few examples of earthquakes that recently occurred in Italy, we will show comparisons between post processed high-rate solutions carried out by Gipsy-Oasis II solutions and those obtained by RTPPP simulating real-time time series. The obtained accuracies will demonstrate the reliability of the RING infrastructure real-time GNSS solutions for early warning and rapid response applications.  

How to cite: Miele, P., Avallone, A., D'Ambrosio, C., Falco, L., Shi, D., Maorong, G., Xinyuan, J., Roberto, D., Nicola Angelo, F., Carmine, G., Grazia, P., and Annamaria, V.: Real-time GPS vs full-GNSS time series accuracies estimations at RING INGV research infrastructure, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19312, https://doi.org/10.5194/egusphere-egu24-19312, 2024.

In Precise Point Positioning (PPP) GNSS processing strategy, understanding the stochastic noise, particularly white plus power-law and white plus flicker noise, is essential for improving precision of coordinate estimates. In this study, we present an analysis of single-system and multi-GNSS PPP AR solutions using observations from the GPS, GLONASS and Galileo constellations. We employed three independent software packages, the Bernese GNSS Software v5.4 (BSW5.4), PRIDE-PPPAR (PRIDE) and GipsyX v2.1 (GX2.1), each employing their recommended set of products and processing settings, while attempting to keep settings as consistent as possible between the software packages and processing runs. We processed data from 50 globally distributed IGS stations, carefully selected for known quality and network geometry, for 2019.0 – 2023.5. In our recently showed that Galileo's single-GNSS solutions outperform GPS and GLONASS in precision (AGU2023 conference poster presentation). Combining GPS with Galileo provide the highest precision in coordinate estimates. This was confirmed through consistent results from three PPP-AR tools. We highlight the advantage of combining GPS and Galileo for superior GNSS positioning precision. In this study, we conduct a comprehensive noise and power spectra analysis of the position time series, focusing on solutions from GPS, GLONASS, and Galileo constellations and combinations. A comprehensive exploration was performed, initially delving into the precision of coordinate estimates through both single system (GPS, GLONASS, and Galileo) solutions and multi-GNSS solutions (including all possible binary and triple combinations) followed by an investigation of the noise characterstics using the Hector v2.0 software. We employed both power-law plus white noise and flicker plus white noise models to assess the behavior and amplitude of the different noises. Moreover, this research uses spectral analysis to emphasize power reduction strategies for periodic signals in single-system and multi-GNSS solutions. 

How to cite: Erkihune, E., Teferle, F., Hunegnaw, A., and Duman, H.: Noise Characteristics in Single and Multi-GNSS Precise Point Positioning with Ambiguity Resolution: A Comparative Analysis of GPS, GLONASS, and Galileo, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20505, https://doi.org/10.5194/egusphere-egu24-20505, 2024.

The era of tracking artificial Earth satellites using Global Navigation Satellite Systems (GNSS) began in the early 1980s when a GPS receiver was launched onboard the Landsat-4 mission. Since then, a large number of Low Earth Orbiters has utilized constantly improved GPS receivers for timing and positioning. GNSS has become a key technique not only for satellite orbit determination but also for atmosphere sounding. With the increasing popularity of miniaturized satellites in recent years, the need for an adapted GNSS payload for nanosatellites arose. Therefore, we developed a small-size, versatile payload board using commercial-off-the-shelf (COTS) low-cost multi-GNSS receivers with extremely small weight, size, and power consumption.

The receiver firmware enables multi-constellation navigation solutions and GNSS raw data output in space with a sampling rate of up to 20 Hz. With this configuration, we can retrieve the required GNSS code and carrier phase measurements, e.g. for precise orbit and attitude determination, to monitor the total air density from drag, the distribution of the electron content, or scintillation effects. The high demands on GNSS receiver performance lead to particular requirements for hardware, payload software, onboard computing, data downlink, and remote control, which will be briefly discussed in the presentation. The resulting low-cost GNSS board fits into a 0.25U form factor, and the modular design makes it a scalable and adaptable payload for CubeSat missions.

In this presentation, we will provide insight into the performance of the GNSS payload under simulated orbit conditions and highlight the necessary modifications that allow us to transform a COTS GNSS receiver into a scientific instrument for space applications.

How to cite: Moeller, G., Wolf, A., Sonnenberg, F., Bauer, G., Soja, B., and Rothacher, M.: A low-cost commercial off-the-shelf GNSS receiver for space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3806, https://doi.org/10.5194/egusphere-egu24-3806, 2024.

Real-Time Single-Frequency Precise Point Positioning (PPP) is a cost-effective and promising method for achieving highly accurate navigation at sub-meter or centimeter levels. However, its success heavily relies on real-time ionospheric state estimations to correct delays in Global Navigation Satellite System (GNSS) signals. This research employs the Dynamic Mode Decomposition (DMD) model in conjunction with global ionospheric vertical total electron content (vTEC) Root Mean Square (RMS) maps to create 24-hour forecasts of global ionospheric vTEC RMS maps. These forecasts are integrated with C1P forecast products, and the performance of L1 single-frequency positioning solutions is compared across various ionospheric correction models. The study assesses the impact of assimilating predicted RMS data and evaluates the practicality of the proposed approach using the IGRG product. The results demonstrate that the IGSG RMS prediction-based model significantly enhances positioning accuracy for up to five hours ahead, yielding results comparable to alternative models. This approach holds promise for achieving high precision navigation.

How to cite: Reuveni, Y. and Landa, V.: Advancing Real-Time GNSS Single-Frequency Precise Point Positioning through Ionospheric Corrections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5282, https://doi.org/10.5194/egusphere-egu24-5282, 2024.

In the ongoing project ESPRIT, a goal is to investigate the contribution of the chemical composition and associated chemical reactions to the Earth’s upper atmosphere. This is realized through a combined analysis of thermospheric neutral density estimates and the exploration of external parameters of the interplanetary space, including variations in the magnetic field and the merged electric field. Regarding changes in the chemical composition of the Earth’s atmosphere, which might cause heating and cooling effects, we investigated TIMED/SABER measurements in conjunction with findings from the 1D first-principles hydrodynamic upper atmosphere model Kompot code, which shows some significant expansion in the density profile mainly based on the increased XUV flux from the Sun. The neutral mass densities were processed based on accelerometer measurements as well as on kinematic orbit information (Süsser-Rechberger et al. 2022). This allowed us to successfully process kinematic orbits for 19 different satellites at an altitude range of approximately 400 to 1300 km. Both approaches are realized using the in-house software package GROOPS. During the evaluation, significant improvements in the processing and parametrization could be achieved compared to previous solutions, especially through refined models for solar radiation pressure, the Earth’s re-radiation, the thermal radiation of the satellite itself and the consideration of the chemical composition of the atmosphere. Based on these new neutral density estimates, investigations regarding the effects of solar eruptions on the various satellites are performed and used for attempting to forecast the orbital decay of LEO satellites.

How to cite: Strasser, A., Krauss, S., Scherf, M., Suesser-Rechberger, B., and Lammer, H.: New neutral density estimates and forecasts in the framework of project ESPRIT, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5511, https://doi.org/10.5194/egusphere-egu24-5511, 2024.

Estimating global and multi-level variations of the atmospheric variables and being able to predict them are very important for studying coupling processes within the atmosphere, and for various geodetic and space weather applications. These variables include the thermosphere neutral density, the ionospheric electron density, and the tropospheric water vapour, which are relevant to applications such as orbit determination, satellite navigation, and weather/climate monitoring. Available models have difficulties in realistic prediction of these variables due to the simplicity of their structure or sampling limitations. In this study, we present an ensemble-based simultaneous Calibration and Data Assimilation (C/DA) algorithm to integrate freely available satellite geodetic data (e.g., CHAMP, GRACE(-FO), Swarm, and GNSS) into empirical models with the focus on improving the predictability of atmospheric variables. The improved model, called `C/DA-model' will be assessed in relevant geodetic and space weather applications. For demonstration, the CDA-NRLMSISE-00 is examined during seven periods with relatively high geomagnetic activity and CDA-IRI-ZWD during extensive rainy events.

How to cite: Forootan, E., Farzaneh, S., Dehvari, M., Retegui-Schiettekatte, L., and Schumacher, M.: Sequential calibration and data assimilation for predicting atmospheric variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5567, https://doi.org/10.5194/egusphere-egu24-5567, 2024.

The upcoming low earth orbit (LEO) constellations can bring new opportunities for ionospheric sounding below the LEO satellite altitude. The CENTISPACE^TM LEO satellites working with an altitude of 700 km broadcasting navigation augmentation signals to the ground stations. This study established a regional bottomside ionospheric map (RBIM) using navigation augmentation signals from two CENTISPACE^TM satellites on April 1, 2023, under moderate solar activity and quiet geomagnetic conditions. The RBIM accuracy was subsequently validated through comparison with multiple datasets, including Global and Regional Ionospheric Maps (GIMs and RIMs) constructed from ground-based GNSS observations, as well as the differential Slant Bottomside Electron Content (dSBEC) derived from LEO observations. To build the RBIM, the vertical bottomside electron content (VBEC) is fitted by two distinct methods, which are grid map and polynomial methods. The root mean square (RMS) values of the RBIM fitting residuals are 1.2 TECU and 0.7 TECU for the two methods, respectively. The RBIM precision evaluated by LEO dSBEC is better than 1.0 TECU. Comparing the VBEC from established RBIM to the GIM/RIM indicates that the RMS values mostly within 3-8 TECU, which can attribute to the limited modelling precision of the latter two models. What’s more, the RBIM facilitates the probe of the proportional variation of the VBEC over the total electron content using experimental data. The results derived from LEO observations indicate that the VBEC proportion is 83% at noon and 53% at night in the north mid-latitude region, presenting a reduction of 35.36%, which is more realistic than that calculated values from the empirical International Reference Ionosphere (IRI-2020) model (4.65%). Thus, the RBIM can not only benefit LEO navigation augmentation but also provide significant observations on the vertical distribution of ionospheric electron content.

How to cite: He, R., Li, M., Li, W., and Zhang, Q.: Estimating bottomside ionosphere electron content using navigation augmentation observations from two CENTISPACETM LEO satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8540, https://doi.org/10.5194/egusphere-egu24-8540, 2024.

In the last few decades, GNSS observations have frequently been used in Structural Health Monitoring (SHM) and Earthquake Early Warning (EEW) systems. The primary advantage of high-frequency GNSS techniques is to detect displacements directly in a terrestrial reference frame compared to conventional geotechnical sensors. Among GNSS techniques, the real-time kinematic (RTK) has predominantly been employed in dynamic displacement monitoring because it provides high accuracy simultaneously. Nevertheless, an external GNSS infrastructure is essential in RTK applications to achieve high positioning accuracy, which restricts its use in possible mega earthquake events. On the other hand, Precise Point Positioning (PPP), which can provide high positioning accuracy with a standalone GNSS receiver on a global scale, emerged as an alternative to traditional GNSS techniques. However, the requirement of an external internet connection for real-time PPP applications is the main restriction of this technique in the employment of possible mega earthquake events like the RTK technique. Instead, the variometric approach (VA) can provide high accuracy in determining dynamic behaviors with a standalone GNSS receiver and broadcast ephemeris only, which means it doesn't require any external infrastructure and connection. Furthermore, the emergence of new navigation systems, such as Galileo and BeiDou, brings considerable opportunities to improve the performance of the VA technique in detecting dynamic behaviors. Thanks to progress in GNSS receiver technology, low-cost GNSS receivers have been introduced and taken considerable attention from the GNSS community. Their more compact design makes low-cost GNSS receivers very usable for establishing monitoring networks in harsh environments, such as high-rise buildings and bridges. In this context, this study aims to evaluate the capability of the VA technique with a low-cost GNSS receiver in detecting horizontal dynamic motion simultaneously. For this purpose, this study employs single-frequency (SF) observations of GPS, GLONASS, Galileo, and BeiDou satellites from the u-blox ZED-F9P receiver for the VA technique. Harmonic motions from 5 to 20 mm with frequencies between 0.3 and 5.0 Hz were generated by a single-axis shake table to analyze the capability of the SF-VA technique in detecting structural motion. Also, a simulation of Mw 6.9 Kobe, 1995 earthquake was performed using the shake table to understand the feasibility of the SF-VA technique in possible EEW systems. In the evaluation, displacements from the Linear Variable Differential Transformer (LVDT) were selected as the reference to assess the capability of the SF-VA technique. The results indicated that the peak frequency value of short-term harmonic oscillations up to 5 Hz can be detected with the SF-VA technique adopting GNSS observations from the low-cost receiver. Besides, the results demonstrated that the SF-VA technique can determine the strong ground motions resulting from mega earthquakes at mm-level.

How to cite: Bezcioglu, M., Bahadur, B., Dindar, A. A., and Yigit, C. O.: Monitoring short-term dynamic motion with single-frequency observations from a low-cost GNSS receiver, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9779, https://doi.org/10.5194/egusphere-egu24-9779, 2024.

There is a continuous need for integrating multi-parameter instrumental observations and measurements with Satellite Earth observation data towards continuous monitoring of the environment and infrastructures. This task attains more importance within the tectonic and seismically active area of the Greek "Supersite" (Corinth Gulf, Ionian Islands, etc.). The significant level of geohazards in this region have made necessary the implementation of new technological approaches that could offer reliable augmentation to permanent networks (both geodetic and seismological). In this contribution, we demonstrate the design, construction and installation of a new technological infrastructure that is based on the collaboration of a multidisciplinary research team and on low-cost equipment. Our low-cost instrumentation includes a multi-GNSS dual-frequency chip (Ublox ZED F9P module) mounted on a Raspberry-Pi 4 compute module IO board together with an industry-standard MEMS accelerometer. It provides signal tracking for most of GNSS systems (GPS, GLONASS, Galileo and BeiDou). The GNSS data are collected 24/7/365, quality-checked and processed by use of open-source software. The combined-synergistic use of these new sensors is compatible with ground motion data provided by GNSS reference stations and accelerometers used by seismic agencies. Current work includes the collection, homogenization, processing and archiving of daily data from three test sites using 4G telemetry. The GNSS data support the on-going, pre-operational monitoring of three test sites together with InSAR Copernicus data (Tsironi et al. 2022).

Tsironi, V., Ganas, A., Karamitros, I., Efstathiou, E., Koukouvelas, I., Sokos, E. 2022. Kinematics of Active Landslides in Achaia (Peloponnese, Greece) through InSAR Time Series Analysis and Relation to Rainfall Patterns. Remote Sens., 14(4), 844. https://doi.org/10.3390/rs14040844

How to cite: Ganas, A., Mavropoulos, G., Karamitros, I., Nikolakopoulos, K., Charalampopoulou, V., Anastasiou, D., Athanassopoulos, T., Kyriou, A., and Tsironi, V.: A new low-cost GNSS instrument for monitoring of ground motions and critical infrastructures within the Greek “Supersite”, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10188, https://doi.org/10.5194/egusphere-egu24-10188, 2024.

The sensitivity of Global Navigation Satellite Systems (GNSS)  receivers to ionospheric disturbances and their constant growth is nowadays resulting in an increased concern of GNSS-users about the impacts of ionospheric disturbances at mid-latitudes. The geomagnetic storm of June 22-23, 2015, is an example of a rare phenomenon of a spill-over of equatorial plasma bubbles well north from their habitual region of ~+/- 20º around the magnetic equator.

We study the occurrence of small- and medium-scale irregularities in Southern Europe by analysing the behaviour of the amplitude scintillation index S4 and of the Rate Of Total Electron Content Index (ROTI) during the geomagnetic storm of June 22-23, 2015. To the scope, we leverage data obtained by local GNSS receivers for scintillation monitoring located in Lisbon (Portugal) and Lampedusa (Italy). Data is complemented with total electron content (TEC) data both from the local GNSS receivers and from global ionospheric maps.

The multi-source data allows for a better understanding of the ionospheric dynamic during the studied event.

How to cite: Morozova, A., Estaço, D., Spogli, L., and Barata, T.: Scintillations in the Southern Europe during the geomagnetic storm of June 2015: analysis of a plasma bubbles spill-off using local data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10792, https://doi.org/10.5194/egusphere-egu24-10792, 2024.

Since its launch in 2013, ESA’s Swarm satellite constellation has pushed the frontiers of space weather research and monitoring by means of its broad spectrum of high-quality experiments on-board. Particularly, Swarm observations are being used to globally characterize small- to mid-scale perturbations in the topside ionosphere that may cause severe amplitude and phase scintillations of trans-ionospheric radio signals. Ionospheric scintillation can cause radio signal outage, as well as disruption of modern technological systems used for telecommunication, navigation and remote sensing.

While performing the Swarm DISC project “Monitoring of Ionospheric Gradients at Swarm (MIGRAS)”, the MIGRAS team has profited from the close orbits and synchronization of Swarm satellites Alpha (A) and Charlie (C) to develop two new products that focus on the monitoring of small- to mid-scale plasma density irregularities with horizontal spatial scales in the order of about 100 km - the electron density (Ne) Gradient Ionospheric indeX (NeGIX), and the Total Electron Content (TEC) Gradient Ionospheric indeX (TEGIX). NeGIX estimates spatial Ne gradients using Langmuir probe measurements, and TEGIX estimates spatial TEC gradients using GNSS Precise Orbit Determination (POD) data of Swarm.

In this work, we provide a comprehensive analysis of the capability of these two novel Swarm data products to characterize the perturbation state of the ionosphere at different geographic locations and conditions of geomagnetic activity. Our analysis covers the whole period of available Swarm observations to quantitively describe expected signatures of ionospheric variability, e.g. gradients at sunrise and sunset time, or equatorial crests. The analysis concentrates also on events of perturbed geomagnetic conditions to compare the performance of NeGIX and TEGIX with existing ground-based indices (e.g. GIX) and Swarm products (e.g. IPIR). Moreover, these indices have been developed technically compatible with Swarm’s and DLR’s operational data services. Therefore, our analysis validates and discusses their applicability for space weather science and purposes.

Acknowledgement: The work is funded by the MIGRAS (Monitoring of Ionospheric Gradients At Swarm) project under the Swarm DISC Subcontract Doc. no: SW‐CO‐DTU‐GS‐133, Rev: 1, 13 September 2022.

How to cite: Cahuasquí, J. A., Hoque, M. M., Jakowski, N., Vasylyev, D., Buchert, S., Nykiel, G., Kriegel, M., David, P., Tagargouste, Y., and Berdermann, J.: NeGIX and TEGIX: two new indices to characterize the topside ionosphere with Swarm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12608, https://doi.org/10.5194/egusphere-egu24-12608, 2024.

In 2023, we began installing a low-cost tectonic multi-GNSS network in Greece, funded by the European Research Council. We have installed a total of 45 permanent/continuous-mode stations, with another 15-20 to be installed in 2024. Installations so far have been mainly on the Peloponnese peninsula, with the strategy of increasing spatial resolution in between the existing research and privately operated GNSS networks. Station maintenance is funded by the project (ERC StG: TectoVision) until 2027, but it is the intention that as many as possible of these stations can stay installed (as permanent installations).

The scientific purpose of the new stations is to increase spatial resolution of microplate motions in Greece but these data will also be of use to other research fields needing single- or multi-GNSS observables. Accordingly, these data are being released without embargo subject to completion of quality control checks (with the data publication and link to download to be finalized before EGU 2024).

We consider this installation campaign to be a pilot project in affordable, rapid densification of tectonic-grade GNSS stations. Part of our strategy has been to use relatively low-cost monumentation for the geodetic marker onto which the low-cost installations are installed. Most stations are connected to mains electricity supplies of public buildings, with the monumentation being installed on flat roofs of these buildings. In higher altitude areas where flat roofs are rare, we have made 3 special installations at bedrock sites, with radio telemetry linking to a radio-receiving station in the nearby villages. We use a range of telemetry solutions, with the most common being the transfer of the 30s sampling data via a router containing a Machine-to-Machine (M2M) sim card.

In this presentation, we will show data quality metrics from the initial analysis of 6-11 months of observations and compare to the time series that can be processed from more expensive receiver-antenna combinations. We will also discuss what the team has learned practically (on-site) and logistically about installing low-cost GNSS stations at scale.

How to cite: Bedford, J., Chousianitis, K., Ganas, A., Mouslopoulou, V., Sokos, E., Roumelioti, Z., Nikolakopoulos, K., Pappas, C., Ramatschi, M., Falck, C., Männel, B., Garcia, C., Peña, C., Cökerim, K., Latypova, E., Gianniou, M., Ioannidi, P. I., Pikridas, C., Lazos, I., and Saltogianni, V.: Tectonic monitoring with low-cost multi-GNSS installations in Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14279, https://doi.org/10.5194/egusphere-egu24-14279, 2024.

Observations from the Global Navigation Satellite System (GNSS) play a crucial role in numerous applications, but are prone to measurement noise, especially when utilizing low-cost receivers and antennas. These measurement noises are crucial as they significantly impact the accuracy and reliability of positional data. This study investigates the characteristics and implications of measurement noises in low-cost GNSS systems, with a particular focus on the effects of receiver and antenna quality, environmental factors, and satellite dynamics. It employs a geometry-free approach to GNSS measurement analysis, aiming to identify and quantify the various noise sources in code-pseudorange and carrier phase observations. The analysis utilized data from two low-cost GNSS stations, each equipped with a u-blox dual-frequency receiver. These stations are equipped with survey-grade and navigational antennas. Additionally, data from the IGS station IITK has been used for comparative analysis.

How to cite: Anwar, I. and Devaraju, B.: Assessing measurement noises from low-cost GNSS receivers and antennas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14661, https://doi.org/10.5194/egusphere-egu24-14661, 2024.

GNSS Interferometric Reflectometry (GNSS-IR) is redefining its role as an innovative technique in environmental sensing. However, geodetic-quality GNSS receivers and antennas are still very expensive instruments which limits their use as dedicated environmental sensors. Recently, low-cost GNSS-IR sensors have been developed for monitoring surface changes such as water level, snow depth and soil moisture. Real-time signal-to-noise ratio (SNR) observation, the key observable of ground-based GNSS-IR, can open up a range of possibilities for environmental monitoring with low cost sensors that can operate unattended for long periods of time. We have recently successfully developed a low-cost water-level sensor called Raspberry Pi Reflector (RPR) based on GNSS-IR technique (Karegar et al. 2022, Water Resources Research, 58). In spring and summer 2023, a network of eight RPRs was installed along the Rhine, the largest river in Germany, from Petersau to Sankt Goar. We installed some of these RPRs in a relatively steep and narrow middle Rhine valley, where the terrain relief around the instrument can influence the effectiveness of the GNSS-IR approach. The water level measurements provided by these sensors are used to validate the SWOT observations of surface water levels. In this presentation, we will present the results of the deployment of the RPRs and discuss the challenges associated with these low-cost sensors.

How to cite: A. Karegar, M., Fenoglio-Marc, L., M. Larson, K., Kusche, J., and Uyanik, H.: The emergence of low-cost GNSS-IR sensors for surface change monitoring: a case study of the RPR network for measuring the Rhine River level, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14817, https://doi.org/10.5194/egusphere-egu24-14817, 2024.

GFZRNX-QC software is designed to streamline the processing of Receiver Independent Exchange Format (RINEX) observations and the generation of overall information by providing a robust and efficient solution for data cleaning and quality control. With a focus on multiple Global Navigation Satellite System (multi-GNSS) observations, GFZRNX-QC offers a comprehensive approach to ensuring data accuracy and reliability. GFZRNX-QC can allow users to efficiently manage and analyze data from various GNSS receivers, especially for low-cost GNSS receivers. The software incorporates advanced algorithms for data cleaning, helping users to eliminate inconsistencies and enhance the overall quality of GNSS observations. GFZRNX-QC conducts comprehensive quality control assessments on GNSS observations. This ensures that the processed data meets the highest standards of accuracy. The software generates detailed statistical results, offering insights into the performance and reliability of observations across the five major GNSS systems. This information aids researchers and analysts in making informed decisions. GFZRNX-QC produces various outputs that can be e.g. compatible to former processing tools like teqc. This can enhance user convenience and interoperability with other geodetic processing tools.

GFZRNX-QC has been extensively tested by utilizing multi-year data from IGS stations to enable comprehensive long-term statistical analysis. By combining efficient data processing, advanced cleaning algorithms, and extensive quality control measures, GFZRNX-QC serves as a valuable tool for researchers, geodesists, and GNSS professionals seeking reliable and accurate observations and overall information from multiple satellite systems.

How to cite: Chen, X., Nischan, T., Deng, Z., Männel, B., and Wickert, J.: GFZRNX-QC: Advanced GNSS Data Processing and Quality Control for Multi-System Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14867, https://doi.org/10.5194/egusphere-egu24-14867, 2024.

The Meteorological Assimilation from Galileo and Drones for Agriculture (MAGDA) project aims to advance the integrated use of satellite-borne, drone-borne, and in-situ sensors, enhancing irrigation optimisation and weather hazard mitigation in agriculture. At its core, MAGDA employs low-cost Galileo-enabled GNSS ground stations for retrieving atmospheric water vapour and soil moisture. This data, combined with information from other technologies, is intended for assimilation into numerical weather prediction and hydrological models.

MAGDA’s demonstration sites are strategically located in three diverse agricultural regions of Europe: fruit plantations in Italy’s Piedmont, vineyards in France’s Burgundy, and mixed crops in Romania’s Braila county. Each of these sites is equipped with three low-cost GNSS stations, operational since mid-2023, providing valuable data for testing the efficacy and adaptability of GNSS technology in different agricultural and climatic conditions.

In addition to the three demonstration sites, MAGDA leverages data from pre-existing GNSS permanent stations across these countries. A comprehensive dataset from 397 stations in the Italy-France domain and 74 stations in the Romania domain has been downloaded. This data is specifically designed for the assimilation of GNSS-derived water vapour data, covering the entire weather model domains, complementing the localised information from the project’s targeted low-cost stations.

GNSS data processing utilises GReD’s proprietary Breva software, capable of analysing multi-frequency and multi-constellation observations. Atmospheric water vapour estimates are obtained through an undifferenced and uncombined batch least squares Precise Point Positioning (PPP) approach. This method has been employed to analyse six weather events that significantly impacted agricultural operations at the demonstration sites, two events per site.

Soil moisture results have been obtained by a newly developed module of Breva software that applies GNSS reflectometry based on the analysis of SNR measurements influenced by the humidity of the superficial soil. The methodology has been tested and validated at various previously studied sites, as well as directly at the low-cost GNSS stations established by the MAGDA project.

This work presents the preliminary results achieved in the first half of the MAGDA project, outlining encountered limitations and future development plans related to the analysis of MAGDA’s GNSS stations.

How to cite: Gatti, A., Fumagalli, A., Barindelli, S., and Realini, E.: Atmospheric and Soil Moisture Monitoring in Agriculture Using GNSS: First Results from the MAGDA Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17705, https://doi.org/10.5194/egusphere-egu24-17705, 2024.

Ship navigation data records are proposed to be complementary information for monitoring offshore tsunami currents following great earthquakes. Offshore GPS measurements on the research vessel Kilo Moana of the University of Hawaii following the 2010 Mw 8.8 Maule earthquake have illustrated the potential of GPS kinematic positioning solutions, together with a filtering approach, for detecting the ship's vertical displacement promoted by the tsunami travel velocity. However, kinematic positioning of GPS observations on ships is challenging due to the load, ship speed, and wavefield changes on the open ocean that might produce fast changes in the ship's drift and vertical motion. Wavefield could also introduce additional noise frequencies to the GPS positioning, thus decreasing its precision. Herein, we present a dual-frequency Global Satellite Navigation System (GNSS) low-cost prototype based on the Septentrio Mosaic-X5 card and a low-cost AS-ANT2BCAL antenna. Such a low-cost GNSS station has been installed on a non-commercial ship fleet in order to assess the precision and noise content of offshore GNSS positioning and ionosphere Total Electron Content measurements. We discuss our preliminary results by comparing the precision of the multi-GNSS solution (GPS, GLONASS, Galileo) relative to the one from only the GPS solution using both long-baselines and Precise Point Positioning approaches in post-processing mode. In the second step, we simulate a real-time multi-GNSS positioning solution to evaluate their ability to catch wavefield changes. We finally discuss the detectability of tsunamis with the newly developed GNSS low-cost prototype under various conditions.

How to cite: Jarrin, P., Rolland, L., Vidal, M., Sakic, P., Leclerc, F., Dessa, J.-X., and Palagonia, S.: GNSS low-cost prototype on ship for caching tsunami wave propagation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17764, https://doi.org/10.5194/egusphere-egu24-17764, 2024.

The distribution of atmospheric, hydrological, and oceanic mass loads on the lithosphere affects the deformation of the Earth's surface over time. Monitoring of the relative displacements of the dense global network of permanent Global Navigation Satellite System (GNSS) stations enables the direct measurement of these loads on a global scale. The application of inverse GNSS methods provides an independent tool to retrieve the time variable gravity (TVG) models of the Earth system and to support hydrogeodesy studies, including the monitoring of the water storage cycle or polar ice mass loss.

The goal of this study is to investigate the effectiveness of using inverse GNSS methods to provide independent C₂₀ and C₃₀ coefficients. These coefficients are essential for deriving highly accurate Gravity Recovery and Climate Experiment (GRACE)-based TVG models. In this study, surface mass variations of low-degree TVG coefficients are derived from the displacements of continuously tracking GNSS sites based on the 21 years (2000-2021) of the Center for Orbit Determination in Europe solutions of the 3rd data reprocessing campaign of the International GNSS Service in the framework of the preparation of the International Terrestrial Reference Frame 2020. The geometrical displacements of the GNSS stations calculated by inverse methods are compared with changes in the gravity field based on independent estimates obtained from the GRACE and GRACE Follow-On (GRACE-FO) satellite missions and the Satellite Laser Ranging (SLR).

As an alternative to the solutions provided by SLR, it is shown that the C₂₀ and C₃₀ coefficients can be derived based on GNSS station displacements. The challenge of the inverse GNSS approach is to properly choose the maximum degree of TVG expansion. Compared with the SLR-based solution, the most consistent GNSS estimate of the temporal gravity variation rate of the C₂₀ coefficient (−1.73 ± 0.10 × 10⁻¹¹/year) and annual variation (4.7 ± 0.6 × 10⁻¹¹/43.9° ± 7.5°) was obtained by expansion of the spherical harmonics to degree and order of 8. The GNSS-based C₃₀ series is superior to the SLR-based estimates before the launch of the Laser Relativity Satellite. From August 2016, when the C₃₀ estimates are essential for correcting the GRACE solutions, the root mean square between GNSS and SLR solutions is 4.2 × 10⁻¹¹. GNSS could potentially support GRACE/GRACE-FO solutions that face problems in deriving C₂₀ and C₃₀, which are fundamental to estimates of ice mass changes in the polar regions. Recovery of mass change in the Antarctic ice sheet from April 2002 to December 2020 based on the coefficients replaced by GNSS estimates results in a linear trend of −111 ± 3 Gt/year. In comparison, the trend for the SLR-based replacement from Technical Note 14 shows a trend of −114 ± 2 Gt/year.

How to cite: Nowak, A., Zajdel, R., Gałdyn, F., and Sośnica, K.: Using Inverse GNSS Methods for the Determination of C20 and C30 Gravity Field Coefficients for the Support of GRACE Solutions   , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-327, https://doi.org/10.5194/egusphere-egu24-327, 2024.

In recent two decades, monitoring of changes in the Earth’s gravity field has been carried out mainly by the Gravity Recovery And Climate Experiment (GRACE) and its successor GRACE Follow-On. However, before the GRACE era,  very little information is available on the temporal evolution of the Earth's gravity field prior to that date. Moreover, through these missions, we have many gaps between 2010 and 2019. Fortunately, GRACE and GRACE Follow-On are not the only missions that can be used to recover variations in the Earth's gravity field. For the recovery of the mass redistribution processes on a large scale, we may employ precise Satellite Laser Ranging (SLR) observations.

We propose a set of long-term, continuous solutions based on SLR data. In our solutions, we use observations from spherical geodetic satellites. The gravity field is expanded up to a degree and order 10 with a monthly resolution from 1/1995 to 10/2021. The main solution has been decomposed into solutions expanded to degree and order 4, 6, 8, and 10 and stacked, taking advantage of the stability of the low-degree expansion and the better resolution of the high-degree expansion. The results show the reduction of the correlations between obtained parameters, stabilization of the ice mass estimates in polar regions – in Greenland and Antarctica, and a reduction of the noise over oceans by a factor of four.

In the GRACE and GRACE Follow-On datasets, the replacement of the spherical harmonics C₂₀ and C₃₀ with SLR-derived data is necessitated by suboptimal quality resulting from thermal effects impacting satellites and accelerometer malfunctions. In both SLR and GRACE solutions, coefficients of the same order and parity exhibit strong correlations. Merely replacing two specific coefficients could introduce bias into the solution. Therefore, we propose a comprehensive approach, combining GRACE with SLR solutions up to a degree and order of 10x10. This strategy ensures a proper consideration of the sensitivity of each technique to gravity field coefficients. The combined solution exhibits reduced noise compared to standard GRACE COST-G solution and effectively address the distinct sensitivities of SLR and GRACE techniques to low-degree time-variable gravity field coefficients.

How to cite: Gałdyn, F., Sośnica, K., and Zajdel, R.: Long-term gravity field changes from SLR data and the combination with GRACE  for improving low-degree coefficients, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-385, https://doi.org/10.5194/egusphere-egu24-385, 2024.

Vertical Land Motion (VLM) estimation involves various methods such as Global Navigation Satellite Systems (GNSS), Very Long Baseline Interferometry (VLBI), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), and Satellite Laser Ranging (SLR). However, satellite altimetry presents an alternative approach for estimating VLM independently. This study compares altimetry-based VLM estimates with those obtained from Tide Gauge (TG) devices. The VLM is determined by calculating the difference between the linear trends of sea-level time series derived from altimetry-based data Instantaneous Sea Surface Height (ISSH) and TG data. Additionally, VLM can be estimated by comparing the linear trends of altimetry-based Sea Level Anomalies (SLA) and TG SLA time series.
 To estimate VLM, absolute ISSH measurements from satellite altimetry, unaffected by the Earth's crust, are contrasted with relative sea level measurements recorded by TG stations with respect to a fixed land point. By differentiating and aligning temporal pairs of TG and altimetry data, only the linear trend remains, representing the vertical displacement of the TG station relative to the reference surface. Removing satellite altimetry instrumentation drifts enables the extraction of VLM from the difference in linear trends. 
The VLM estimate obtained for the Hadera TG station, covering 1992-2016, shows a positive trend of 0.24 ± 0.07 mm/year. This finding aligns with GNSS-based VLM estimations at the same station, indicating land uplifting in the region. Consequently, the study suggests that there is no immediate concern about the rise of sea level. These findings enhance our understanding of regional geodetic processes and their implications for assessing sea level changes. By providing valuable information on VLM estimation, this research contributes to our knowledge of vertical displacement on land and its significance for future studies.

How to cite: Murshan, M., Devaraju, B., Balasubramanian, N., and Dikshit, O.: Vertical Land Motion Detection Using Satellite Altimetry Data at the Hadera Tide Gauge Station, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-891, https://doi.org/10.5194/egusphere-egu24-891, 2024.

During periods of drought, quantifying the intensity of water loss within hydrologic reservoirs, both on and below the surface, is critical to sustain water resources. Drought intensity is typically characterized using drought indices which are driven by meteorologic observations, such as precipitation. These drought indices provide good insight into the quantity of water entering the hydrologic system, however, they are unable to quantify the amount of water retained in a watershed or the amount lost due to runoff and evapotranspiration. We address this by leveraging the sensitivity of three-dimensional Global Positioning System displacements to local and regional hydrologic-storage fluctuations, and produce a new geodetic drought index (GDI), derived from estimated hydrologic-storage deviations, to directly characterize hydrologic storage anomalies. The GDI is derived comparably to the Standardized Precipitation Evapotranspiration Index such that it may be easily incorporated into current drought management workflows. We directly compare the GDI to hydrologic observations within California and find strong associations between specific time scales of the GDI and groundwater well, artificial-reservoir storage, and stream discharge observations. The GDI is most sensitive to groundwater, exhibiting a correlation coefficient of 0.87 at the 3-month time scale. Both artificial-reservoir storage and stream discharge exhibit peak correlation coefficients when considering the 1-month GDI, at 0.81 and 0.47 respectively. No relationship is observed with soil moisture observations. The correlation coefficients decline rapidly away from the optimal time scale, indicating the 1- and 3-month GDI are strong predictors of hydrologic variation within California. In addition to capturing long-term trends, rapid changes in the GDI initiate during clusters of large atmospheric-river events that closely mirror fluctuations in the hydrologic observations. The GDI provides an opportunity to improve hydrologic models for drought-management and to advance our understanding of the water cycle.

How to cite: Young, Z. M., Martens, H. R., Hoylman, Z. H., and Gardner, W. P.: A Geodetic Drought Index Driven by Hydrologic Loading Estimates Calculated from Three-Dimensional GPS Displacements  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3658, https://doi.org/10.5194/egusphere-egu24-3658, 2024.

The total mass change of the Earth's land surface precisely offsets the combined changes in the atmosphere and oceans, resulting in a net-zero change for the entire system (land+ocean+atmosphere).

Closing the ocean mass budget is crucial for understanding current and future sea-level changes. Recent efforts to reconcile ocean mass observations from GRACE and GRACE-Follow On satellites (hereafter unitedly referred to as ‘GRACE’) with both steric-corrected altimetry and and land/ice to ocean estimates have revealed a discrepancy in the mass budget (Wang et al, 2022; Barnoud et al, 2022). This finding indicates a concerning misalignment in our global observation system or understanding of earth mass transport.

This study uses GRACE-independent estimates/models of land surface mass changes to validate 20 years of GRACE observations. By calculating the monthly Gravitational, Rotational, and Deformational (GRD) response to 20 years of land mass changes, we reconstruct the global, regional, and seasonal ocean mass changes observed by GRACE from 2003 to 2022.

Over the 20-year period, the ocean mass reconstruction aligns well with the GRACE observations. However, a significant deviation emerges after 2020, with the reconstruction showing a larger ocean mass change than GRACE. We demonstrate that this deviation is likely caused by an underestimation of Western Africa precipitation in the ERA5 reanalysis, commonly used by hydrological models to estimate changes in land water storage. Land mass observations from GRACE further confirmvthis underestimation and shows great alignment between models and observations when excluding sub-Saharan Africa.

Our results show a global agreement between GRACE and GRD-induced ocean mass changes, suggesting that the misalignment between GRACE and steric-corrected altimetry is likely due to errors in the ARGO observing system. A reported 'salinity-drift' is the primary source of error, and together with an error in the wet path delay originating from drift in the radiometer of the Jason-3 satellite explains most of the post-2016 difference between GRACE and steric-corrected altimetry is identified. The remaining differences likely originate from GIA and/or Argo-biases.

How to cite: Ludwigsen, C. B., Andersen, O. B., Watson, C., and King, M.: Reconciling ocean mass changes from 20 years of GRACE and GRACE Follow On observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5267, https://doi.org/10.5194/egusphere-egu24-5267, 2024.

Dynamic and accurate assessment of terrestrial water and groundwater is crucial for water resources management, agriculture and ecological environment protection. While the GRACE/GRACE-FO satellites have proven effective in monitoring large-scale terrestrial water storage anomaly (TWSA) and groundwater storage anomaly (GWSA), this study aims to enhance their spatial and temporal resolution for detecting the small-scale TWSA and GWSA in the Yangtze River Basin (YRB).

The main objectives and contents are as follows: 1) The daily/0.25° TWSA and GWSA are modeled using the W3RA hydrological model, incorporating factors such as rainfall, temperature, wind speed, and radiation; 2) The spatial correlation errors of GRACE/GRACE-FO data from 2002 to 2022 are considered, the uncertainties in the GRACE data over the YRB are obtained by the JPL; 3) The monthly and daily/0.25° TWSA and GWSA during 2002-2022 in the YRB is derived by accounting for the spatial correlation errors in the assimilation of GRACE/GRACE-FO into W3RA through the Ensemble Kalman Filter (EnKF) method; 4) Utilizing the results to quantify the spatiotemporal distribution of water resources in the YRB, the GRACE/GRACE-FO data signal serves as a valuable supplement to the model data. 5) The monthly and daily/0.25° GWSA will be further compared with in-situ well data to validate their accuracy. Furthermore, a quantitative analysis of the temporal and spatial evolution characteristics of GWSA in the YRB will be conducted.This study will provide valuable data and technical references for the dynamic monitoring of a small-scale TWSA and GWSA in the YRB and applications worldwide.

How to cite: Wan, X., Chao, N., and Yin, W.: A monthly and daily 0.25° groundwater storage dataset in the Yangtze River Basin by assimilating GRACE/GRACE-FO data into the W3RA hydrological model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5690, https://doi.org/10.5194/egusphere-egu24-5690, 2024.

The accuracy of global ocean tide models (OTMs) in shallow waters and along coasts impacts on their numerous applications. For example, the use of OTMs to provide tide corrections (‘de-tiding’) for satellite altimetry observations is required for, e.g., sea level studies and marine gravity field recovery. OTMs are also indispensable in the mitigation of striping errors in GRACE and GRACE-FO time-variable gravity field solutions. It therefore follows that OTM errors in coastal and shelf ocean may then introduce biases into the ‘corrected’ satellite altimetry and gravimetry observations with the potential to impact models using these data. The purpose of our study is to assess the accuracy of two high resolution assimilated OTMs (TPXO9v5, and FES2014b) using an updated set of >100 coastal and shelf tide gauges across the northern Australia and Papua New Guinea region. TPXO9v5 and FES2014b are used here because they have previously compared better than other tidal models in adjacent coastal and shelf areas. This study will also provide insight into the tides in this region which contain a mix of shallow and medium depth waters adjacent to the coast, in addition to land and island barriers that result in a complex tidal regime. This study takes advantage of the large number of short-term tide gauges situated on the coast or offshore islands in Northern Australia and Papua New Guinea. This set of tide gauges have observation periods of >30 days, with a number being more than 90 days long which allows the resolution of the major semidiurnal and diurnal tidal constituents. We use harmonic analysis to estimate tidal constants of major diurnal and semi-diurnal constituents from tide gauges then compare them with corresponding values from TPXO9v5 and FES2014b at the tide gauge location. This comparison identifies improvements and also limitations in these OTMs in this region, and their potential impact on tide corrections provided for satellite altimetry products that may propagate into coastal sea surface, and gravity at the coast. The results also provide additional insight into the local tidal patterns in this region, with particular interest in the Torres Strait and surrounding area.

How to cite: Filmer, M., Seifi, F., and Claessens, S.: Evaluation of ocean tide models in coastal ocean regions of northern Australia and Papua New Guinea using an updated set of short term tide gauges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7127, https://doi.org/10.5194/egusphere-egu24-7127, 2024.

Water mass changes at and below the surface of the Earth cause changes in the Earth’s gravity field which can be observed by at least three geodetic observation techniques: ground-based point measurements using terrestrial gravimeters, space-borne gravimetric satellite missions (GRACE and GRACE-FO) and geometrical deformations of the Earth’s crust observed by GNSS. Combining these techniques promises the opportunity to compute the most accurate (regional) water mass change time series with the highest possible spatial and temporal resolution, which is the goal of a joint project with the interdisciplinary DFG Collaborative Research Centre (SFB 1464) "TerraQ – Relativistic and Quantum-based Geodesy".

A method well suited for data combination of time-variable quantities is the Kalman filter algorithm, which sequentially updates water storage changes by combining a prediction step with observations from the next time step. As opposed to the standard way of describing gravity field variations by global spherical harmonics, we introduce space-localizing radial basis functions as a more suitable parameterisation of high-resolution regional water storage change. An estimation environment has been set up for the combination of GRACE/-FO satellite gravimetry with GNSS station displacements. The feasibility and stability of the approach is first demonstrated in a closed-loop simulation to test the setup and tune the algorithm. Subsequently, it is applied to real GRACE and GNSS observations to sequentially update the parameters of a regional gravity field model for Central Europe. The implementation was designed to flexibly include further observation techniques (e.g. terrestrial gravimetry) at a later stage. This presentation will outline the Kalman filter framework and regional parameterisation approach, and addresses challenges such as the relative weighting between the GRACE and GNSS data, and the appropriate choice of the Kalman filter process model and radial basis function parameterisation.

How to cite: Wöhnke, V., Eicker, A., Weigelt, M., Reich, M., and Güntner, A.: Regional modelling of water storage variations from combined GRACE/-FO and GNSS data in a Kalman filter framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7430, https://doi.org/10.5194/egusphere-egu24-7430, 2024.

Under the assumption that a warming climate leads to an intensification of the global water cycle, it can be hypothesized that also the occurrence frequency and severity of extreme events such as droughts or floods will increase in the upcoming decades to centuries. Global coupled climate models, which project the future evolution of various variables of the Earth's climate system are important tools for the analysis of such expected changes. To assess the reliability of the models and to identify possible systematic discrepancies, it is essential to evaluate the model output against observations.

In this study, present and future occurrences of extreme events are analysed in water storage time series simulated by coupled global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and compared against spatio-temporal changes in water mass derived from GRACE and GRACE-FO. This comparison is based on Extreme Value Theory, as the exact timing of modelled extreme events cannot be assessed by observations due to the stochastic behavior of climate variability in unconstrained model experiments. From estimated extreme value distributions return levels are calculated, a quantity describing the magnitude or frequency of extreme values.  Challenges that have to be overcome in the analysis are the non-stationary data and the relatively short time span of the GRACE observations. The latter issue is addressed by additionally assessing GRACE-based water storage reconstructions available over many decades.

This study provides insights into the ability of global climate models to model the occurrence of TWS extremes, namely unusual dry and wet phases. It also examines whether the climate model projections predict an increasing intensity of extreme events.

How to cite: Middendorf, K., Eicker, A., Jensen, L., and Dobslaw, H.: Evaluation of extreme events in global coupled climate models by satellite gravimetry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7669, https://doi.org/10.5194/egusphere-egu24-7669, 2024.

Changes in soil water storage can be studied on a global scale using a variety of satellite observations. With active or passive microwave remote sensing, we can study the upper few centimeters of the soil, while satellite gravimetry allows us to detect changes in the entire column of terrestrial water storage (TWS). The combination of both types of data can provide valuable insight into hydrological dynamics in different soil depths towards a better understanding of changes in subsurface water storage.

We use daily Gravity Recovery and Climate Experiment (GRACE) data and satellite soil moisture data to identify extreme hydroclimatic events, focusing on prolonged droughts. To enhance our comprehension of the subsurface, we utilize not just surface soil moisture data but also integrate information on root zone soil moisture. Original level-3 surface soil moisture data sets of SMAP and SMOS are compared to post-processed level-4 data products (both surface and root zone soil moisture) and a multi-satellite product provided by the ESA CCI.

We analyse the correspondence between high and low percentiles in TWS and soil moisture time series, which allows us to identify extreme events in different integration depths and storage compartments. Furthermore, we compute the rate of change of anomalies to assess how quickly the system accumulates storage deficits during drought conditions and recovers from them for different soil depths. Our investigation focuses on the temporal dynamics of near-surface soil moisture and TWS, highlighting the cascading effects that propagate from the surface into the subsurface. The results we obtained indicate characteristic patterns of the temporal dynamics of drought recovery in varying soil depths. Specifically, our analysis shows that surface soil moisture recovers faster than TWS, and that this recovery process slows down as soil integration depth increases.

How to cite: Blank, D., Eicker, A., and Güntner, A.: From surface to subsurface: Investigating drought cascades and recovery patterns with (daily) satellite observations of soil moisture and terrestrial water storage, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7888, https://doi.org/10.5194/egusphere-egu24-7888, 2024.

In the context of the ESA Climate Change Initiative (CCI) Coastal Sea Level project, a complete reprocessing (including retracking of the radar waveforms) of high resolution (20 Hz, i.e. 350 m) along-track altimetry data of the Jason-1, Jason-2 and Jason-3 missions since January 2002 was performed along the world coastal zones. Different versions have been provided so far. The latest release (SL_cci+ coastal altimeter sea level dataset, v2.3) is now available to users. It is an extension in time of the previous data set (v2.2) which covers the period January 2002 to June 2021. A new improved processing for the waveform retracking and computation of the coastal sea level anomalies was developed and a new editing procedure for the coastal sea level trend computation was implemented. This new data set shows spectacular reduction of the data noise compared to previous versions, both in terms of sea level anomaly time series and trends. As a consequence, compared to the previous versions we now obtain an important increase of the number of virtual coastal stations (i.e., the location of the first valid point along the satellite track, with about 1200 sites at an average distance from the coast of about 3 km, including more than 200 stations at less than 2 km from the coast). The coastal sea level anomalies and trends of the altimetry-based virtual stations have been validated with tide gauge data where possible. An example in the Mississippi Delta is presented.

How to cite: Cazenave, A., Leclercq, L., Leger, F., Birol, F., Nino, F., Passaro, M., and Legeais, J. F.: 20-year-long sea level changes along the world’s coastlines from satellite altimetry: a new data set of coastal virtual stations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8727, https://doi.org/10.5194/egusphere-egu24-8727, 2024.

NASA and DLR will launch in 2028 GRACE-C (Gravity Recovery and Climate Experiment – Continuation). This mission will again be launched into a polar orbit at 500 km initial altitude and extend the observation of the time-variable Earth’s gravity field from GRACE (2002-2017) and GRACE-FO (GRACE Follow-On, 2018-today). ESA plans to launch a Next Generation Gravity Mission (NGGM) in 2032, which shall fly in a lower and inclined orbit and be based on improved instrumentation. GRACE-C and NGGM will then form the double-pair Mass-Change and Geosciences International Constellation (MAGIC) to significantly increase the spatial and temporal resolution of mass transport products and deduce water mass redistribution over the oceans, ice sheets and continents.

Thanks to the 20+ years period of GRACE and GRACE-FO observations, scientists are able to analyse extreme hydrological events, such as flooding and droughts. However, due to the rather coarse spatial resolution of the GRACE and GRACE-FO data sets of approximately 350 km, finer spatial details of such extreme events are kept hidden. Further, spatial leakage limits the value of these data for smaller-scale regional investigations.

In this contribution, we will employ five years of simulated data for both a single polar pair (GRACE-FO-like) and a MAGIC baseline scenario. Thanks to the simulation, we can also assess the true values of the hydrological input models. Both simulated data sets are filtered with the same DDK filters for comparison. The filter strength can be reduced for the MAGIC baseline scenario without introducing more striping errors.

With these simulated data sets, we investigate extreme hydrological events. For example, the localisation of extreme wet events along the northern coast of Australia is much improved, with less signal leakage into the surrounding ocean.

How to cite: Boergens, E., Wilms, J., Hauk, M., Dahle, C., Dobslaw, H., and Flechtner, F.: Will the MAGIC mission improve the observability of extreme hydrological events?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8830, https://doi.org/10.5194/egusphere-egu24-8830, 2024.

After more than 15 years of experience with GRACE/-FO data assimilation (DA) into hydrological models, numerous studies have conducted various tests on GRACE product and preprocessing options as well as DA strategies. However, a commonly accepted standard procedure has yet to emerge. This contribution comprises (1) a review on the prevalence of GRACE-DA options based on existing studies together with (2) insights from applying two GRACE assimilating frameworks: the high-resolution CLM-DA framework over Europe and the global WGHM-based calibration and data assimilation framework.

We discuss the selection of different GRACE/-FO products for DA into hydrological models, including spherical harmonics, MASCONS, level 3 products, and the recently evolved along-orbit line-of-sight gravity differences. Additionally, we explore processing choices such as filtering and rescaling, possible corrections for phenomena like glacial isostatic adjustment, large lakes and reservoirs or earthquakes, observation grid representation, and various approaches to handle observation errors. We evaluate the impact of these processing strategies on simulated water storage trends and the representation of selected extreme events.

Through this research, we contribute to understanding optimal strategies in assimilating GRACE/-FO data, addressing critical aspects influencing hydrological model reliability.

How to cite: Springer, A., Ewerdwalbesloh, Y., Gerdener, H., Schulze, K., and Kusche, J.: Strategies for assimilating GRACE/-FO terrestrial water storage anomalies into hydrological models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8938, https://doi.org/10.5194/egusphere-egu24-8938, 2024.

The continuously distributed changes in water storage in hydrosphere deform the shape of the Earth’s surface, which can be recorded by GPS position time series. However, GPS stations that exhibit poroelastic behavior located on aquifer systems are excluded from largely previous studies so that terrestrial water storage variations can be estimated by elastic loading, which leads to biases in results. We proposed a novel approach to classify GPS stations whose vertical displacement time series are significantly correlated with hydrological loading variations to construct hydro-geodesy datasets, including elastic response (positive), poroelastic response (negative), and aquifer compaction. Using the wavelet analysis method, we further identified 569 GPS vertical displacement time series from California provided by the Nevada Geodetic Laboratory between 2007 and 2017 into the pre-defined temporal-scales of long-term, seasonal, and short-term. We calculated and evaluated elastic deformation induced by hydrological loading variations, including GRACE, WaterGAP, GLDAS, NLDAS, ERA5, and ERA5-land, and the HYDL product provided by GFZ. The results show that most/several GPS stations located outside/within the Central Valley are under the control of the elastic response. We also used a poroelastic half-space model to validate that most GPS stations located within the Central Valley are simultaneously affected by surface subsidence and controlled by poroelastic response. Our results show that the hydro-geodetic datasets we constructed enable the use of previously and widely neglected GPS stations, such as those that may observe poroelastic response and those affected by surface subsidence, to accurately monitor changes in terrestrial water storage during droughts and floods.

How to cite: Ding, X., Li, Z., and Jiang, W.: A GPS hydro-geodesy dataset for monitoring changes in terrestrial water storage during drought and flood periods in California: assessment, validation, and application, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8961, https://doi.org/10.5194/egusphere-egu24-8961, 2024.

The Gravity Recovery And Climate Experiment (GRACE) and its follow-on mission, GRACE-FO, have observed global mass changes and transports, expressed as total water storage anomalies (TWSA), for over two decades. However, for climate change attribution and other applications, multi-decadal TWSA time series are required. This need has triggered several studies on reconstructing TWSA via regression approaches or machine learning techniques, with the help of predictor variables such as rainfall or sea surface temperature. Here, we combine such an approach, for the first time, with low-resolution information from geodetic satellite laser ranging (SLR). The reconstruction is formulated on a GRACE-derived empirical orthogonal functions (EOFs) basis and complemented with the Löcher and Kusche (2021) approach, in which global gravity fields are solved from SLR ranges in a GRACE EOF basis for the pre-GRACE time frame. Although our technique works globally, we focus mainly on European basins and reconstruct water storage anomalies from 1992 onward.

How to cite: Hacker, C., Kusche, J., Löcher, A., and Li, F.: Reconstructing GRACE-like TWSA maps from 1992 on by combining data-driven methods with time-variable gravity fields from SLR range analyses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10138, https://doi.org/10.5194/egusphere-egu24-10138, 2024.

GRACE and GRACE-FO have proven valuable for monitoring the health of ice sheets by showing seasonal mass changes along with the decadal trends of mass loss. Two years stand out in the Greenland ice sheet mass loss record with record melt: 2012 and 2019. On the West coast of Greenland, the ice mass fluctuations act on remarkably short time scales during these events, as evident at Ilulissat isbræ, which nearly doubled its ice speed in just one week. Here, we study if these sub-monthly ice mass change variations can be measured using GRACE-FO line-of-sight measurements.

It has been shown several times that using dynamic orbits and Laser Ranging Interferometer (LRI) data, one can calculate residual Line-of-sight gravity signals. This method was primarily used to study hydrological signals such as storm surges or heavy rainfall. In this study, we focus on ice mass changes in Greenland, and we compare these GRACE-FO measurements to the expectation based on the monthly gravity field and the signal from mass change based on IceSat2 data for 2019-2021.

How to cite: Jenny, B., Jepsen, M., Simonsen, S. B., Forsberg, R., and Jensen, T. E.: Sub-Monthly Mass Change Signal in Greenland from GRACE-FO Laser Interferometry Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10234, https://doi.org/10.5194/egusphere-egu24-10234, 2024.

Ocean Heat Content (OHC) is an essential indicator of Earth’s climate state. Climate change is driven by the disequilibrium of Earth’s radiation budget. This abundant energy in the system is the Earth Energy Imbalance (EEI), which is challenging to measure globally. About 90% of EEI is accumulated in the oceans, resulting in an increase in ocean heat content. Therefore, OHC is a suitable proxy for EEI and can be measured globally using a combination of geodetic satellite techniques. By combining satellite altimetry and satellite gravimetry, it is possible to measure the change in global ocean heat content over the mission’s lifetime. While the altimeter record covers several decades, satellite gravity missions have been observing global mass transports for two decades. To steadily estimate the system’s long-term behavior, an extended observation period of the satellite systems is needed. The upcoming satellite gravity mission Grace-C, planned to be launched in 2028 by NASA, is meant to ensure continuity and extension of the data record. At the beginning of the 2030s, an additional inclined pair will be launched by ESA to form together with GRACE-C the Mass change And Geosciences International Constellation (MAGIC), for which higher spatial and temporal resolutions are expected.

This contribution presents the results of multi-decadal closed-loop simulations of current and future satellite gravity observations. It shows the benefit of an increased duration of the observation and an improved observational system while comparing processing strategies for long-term trends in ocean mass changes. The observed climate signal is based on projections of mass change signals of oceans, ice sheets, and glaciers derived from CMIP6 climate projection under a shared socio-economic pathway scenario without drastic reduction of Greenhouse gases emissions (SSP5-8.5). A particular focus here is on the accuracy of long-term ocean trends. The direct estimation of long-term trends benefits from an increasing observation period and allows improved spatial resolution compared to trends estimated from monthly temporal gravity fields. The global ocean heat content is estimated from the steric sea-level change which is derived by subtracting the observed ocean mass change from the overall sea level change. The resulting long-term trends in ocean heat content are then compared to initial inputs to the simulation to illustrate the difference in performance between current and future satellite gravity constellations.

How to cite: Schlaak, M., Pail, R., Blazquez, A., Meyssignac, B., and Lemoine, J.-M.: Resolving the interannual to multi-decadal variability in ocean heat content, a simulation study of current and future satellite gravity missions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10483, https://doi.org/10.5194/egusphere-egu24-10483, 2024.

Simulated terrestrial water storage (TWS) data from global hydrological models are indispensable for various geodetic applications, e.g., for simulating Earth orientation parameters, deriving time series of deformations of the Earth’s surface needed for the realization of global reference systems, or de-aliasing purposes of GRACE/-FO gravity products. So far, the Land Surface Discharge Model (LSDM) has been routinely used for such tasks at the GFZ. However, the current standard experiment of LSDM is already several years old, and many limitations are known, in particular a limited spatial resolution of 0.5°, which limits the accuracy of crustal deformation predictions close to rivers and lakes. In this contribution, we evaluate the suitability of LISFLOOD (https://ec-jrc.github.io/lisflood/), an open source, high-resolution hydrological rainfall-runoff-routing model, for geodetic purposes.

We compare the performance of various global LISFLOOD model runs for the time period 2000 – 2022 against the current LSDM configuration. In addition to two LISFLOOD model generations, which differ in their spatial resolution (0.1° and 0.05°) and their input land surface parameter data set, we also explore a number of high-resolution (0.05°) model runs with respect to the influence of the soil depth on simulated TWS. Model results are validated against mass anomalies from the satellite gravimetry missions GRACE and GRACE-FO on different spatial and temporal scales. Furthermore, to demonstrate the benefit of the higher spatial resolution of LISFLOOD, we utilize data from selected ground based GNSS stations to validate the models’ performance regarding mass-induced loading.

We find that LISFLOOD significantly outperforms LSDM in many regions, especially on interannual time scales, in terms of various validation metrics (i.e., correlation, root mean squared deviation, and explained variance). Analyzing the different LISFLOOD runs reveals advantages of the new (0.05°) over the old (0.1°) model version, and a large impact of the choice of soil depth on simulated TWS.

How to cite: Dobslaw, H., Jensen, L., Dill, R., and Balidakis, K.: Assessment of global high-resolution water storage simulations from the LISFLOOD hydrological model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10526, https://doi.org/10.5194/egusphere-egu24-10526, 2024.

Aside from main geodetic applications, the Global Navigation Satellite System (GNSS) is now an established observing system for atmospheric water vapour which is the most important greenhouse gas as it is responsible for around 60% of the natural greenhouse effect. Water vapour is under-sampled in the current climate-observing systems. Obtaining and exploiting more high-quality humidity observations is essential for climate research.

Established in 2006, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN), is an international reference observing network of sites measuring essential climate variables above the Earth's surface. Currently, this network comprises more than 30 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity in the upper atmosphere. GRUAN observations are required to be of reference quality, with known biases removed and with an associated uncertainty value, based on thorough characterization of all sources of measurement.

A complementary small scale regional climate station network is the Austrian WegenerNet, which provides since 2007 measurements of hydrometeorological variables with very high spatial and temporal resolution. GNSS precipitable water vapour (GNSS-PWV) measurement has been included as a priority one measurement of the essential climate variable water vapour to both GRUAN and WegenerNet climate station networks.

GFZ contributes to climate research within GRUAN and WegenerNet with its expertise in processing of ground-based GNSS network data to generate precise PWV products. GFZ is responsible for the installation of GNSS hardware, data transfer, processing and archiving, derivation of GNSS-PWV data products according to GRUAN and WegenerNet requirements including PWV uncertainty estimation. GNSS-PWV products and results of selected validation studies will be presented.

How to cite: Dick, G., Zus, F., Wickert, J., Männel, B., and Ramatschi, M.: GNSS Precipitable Water Vapour for Climate Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11213, https://doi.org/10.5194/egusphere-egu24-11213, 2024.

The Svalbard archipelago in the Arctic is extremely sensitive to climate change. The resulting redistribution of mass, including recent and past ice melt, induces deformations of the Earth's surface and temporal variations in its gravity field, which can be detected by space geodesy. The cross-comparison of different techniques takes advantage of their complementary temporal and spatial resolutions, helping to distinguish between local, regional and global signals. We analyse more than 20 years of GNSS (Global Navigation Satellite System) satellite 3D positionning solutions at 17 permanent sites. The results are compared with deformations computed from time gravity field variations observed by the space gravimetry missions GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On. The mean vertical motion is of about 9 mm/year and can reach 15 mm/year. We then compare these GNSS and GRACE datasets with Little Ice Age (LIA) and Global Isostatic Adjustment (GIA) models as well as with satellite altimetry observations from Cryosat-2 and IceSat-2. We infer the various contributions and quantify the impact of the current climate change on Svalbard. In addition to better estimate the acceleration of the current ice melting we apply an innovative seasonal adjustment method. The results are then discussed in relation to in situ observations.

How to cite: Tafflet, A., Nicolas, J., Boy, J.-P., Lemoine, J.-M., Perosanz, F., Durand, F., Koulali, A., Gourillon, L., Baltzer, A., and Verdun, J.: Solid Earth’s response to climate change in Svalbard monitored by space geodesy , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11433, https://doi.org/10.5194/egusphere-egu24-11433, 2024.

Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite and its following mission GRACE-FO have measured the time-variable gravity fields of the Earth, by which water shifts around Earth can be captured. Among its innovations, GRACE has monitored the change of ice mass from Earth's ice sheets and glaciers, which is essential for the better understanding of the changing climate system. Over the past few decades, glacier mass loss has been significant across the globe. The European Alps are among the regions experiencing the greatest shrinkage of glaciers, which becomes the main focus of this work.
In this work, we will challenge the information of satellite gravimetry, hydrological models, and satellite geodesy to monitor the ice mass loss in the Alps in central Europe. The temporal variations of total water storage (TWS) in the Alpine region are determined from GRACE- and GRACE-FO-based Level-2 products provided by COST-G and Mascon surface mass change fields calculated by JPL. Furthermore, the correction of GIA effects and hydrological signals in the study area is indispensable to isolate the estimate of glacier melting. For the GIA correction, the GIA model ICE-6G_D and the regional dataset of surface displacements obtained from geodetic observation techniques are applied to GRACE data respectively, resulting in obvious different results. For the hydrological correction, the WaterGAP Global Hydrology Model (WGHM) model and the Global Land Data Assimilation System (GLDAS) model are used to estimate the mass change of the liquid part. In addition, the ice mass loss in the Alps between 2000 and 2014 based on glacier inventory was estimated in another publication, which can be a reference (-1.34 Gt/yr). Glaciers in the Alps lost mass at a rate of around -1.4 Gt/yr and around -2.2 Gt/yr depending on different ways of GIA correction during the 21-year period, which have similar magnitudes with the reference value.

How to cite: Liu, S. and Pail, R.: The estimation of glacier changes in the Alps in 2002-2022 with the use of satellite gravimetry data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11913, https://doi.org/10.5194/egusphere-egu24-11913, 2024.