While the theory of random isotropic scalar fields on the sphere is well established, it has not been fully extended to the case of vector fields yet. In this contribution, several theoretical results are thus generalized to random isotropic vector fields on the sphere, including an equivalent of the Wiener-Khinchin theorem, which relates the distance-dependent covariance of the field's components in a particular rotationally invariant basis with the covariance of the vector spherical harmonic coefficients of the field, i.e., its angular power spectrum. A parametric model, based on a stochastic partial differential equation, is proposed to represent the spatial covariance and angular power spectrum of such fields. Such a model is adjusted, with minor modifications, to empirical spatial correlations of the white noise and flicker noise components of 3D displacement time series of ground Global Navigation Satellite Systems (GNSS) tracking stations. The obtained spatial correlation model may find several applications such as enhanced detection of offsets in GNSS station position time series, enhanced estimation of long-term ground deformation (i.e., station velocities), enhanced isolation of station-specific displacements (i.e., spatial filtering), and more realistic assessment of uncertainties in all GNSS-network-based applications (e.g., estimation of crustal strain rates, of glacial isostatic adjustment models, or of tectonic plate motion models).

Precise knowledge of geocenter motion, i.e., the relative motion between the Earth's center of mass (CM) and the center of figure of the Earth's surface (CF), is crucial to high-stakes geodetic applications such as sea level rise monitoring with satellite altimetry or the establishment of regional and global mass budgets with satellite gravimetry. The computation of the latest release of the International Terrestrial Reference Frame, ITRF2020, involved the estimation of a field of seasonal motions for a global network of geodetic stations, expressed with respect to CM, as sensed by Satellite Laser Ranging, from which the translational part represents seasonal geocenter motion. This paper presents two different methods to isolate seasonal geocenter motion from the field of ITRF2020 seasonal station motions, among which a new method based on a direct weighted average of seasonal station motions, with station-specific weights chosen so as to provide a better approximation of CF than the standard network shift approach. The ITRF2020 annual geocenter motion model thus obtained is then compared with other recent geodetic and geophysical estimates. Although different subgroups of estimates with relatively good internal consistency may be identified, the overall scatter of recent geodetic estimates remains at the level of several mm, i.e., close to the amplitude of annual geocenter motion itself. Efforts toward reconciling seasonal geocenter motion estimates therefore still appear necessary. Meanwhile, it would seem safe to assume that seasonal geocenter motion models, in particular those currently used in satellite altimetry and satellite gravimetry, are still uncertain.

A document by Paul Rebischung. Click on the document to view its contents.

The continuous redistribution of water mass involved in the hydrologic cycle leads to deformation of The continuous redistribution of water involved in the hydrologic cycle leads to deformation of the solid Earth. On a global scale, this deformation is well explained by the loading imposed by hydrological mass variations and can be quantified to first order with space-based gravimetric and geodetic measurements. At the regional scale, however, aquifer systems also undergo poroelastic deformation in response to groundwater fluctuations. Disentangling these related but distinct 3D deformation fields from geodetic time series is essential to accurately invert for changes in continental water mass, to understand the mechanical response of aquifers to internal pressure changes as well as to correct time series for these known effects. Here, we demonstrate a methodology to accomplish this task by considering the example of the well-instrumented Ozark Plateaus Aquifer System (OPAS) in central United States. We begin by characterizing the most important sources of groundwater level variations in the spatially heterogeneous piezometer dataset using an Independent Component Analysis. Then, to estimate the associated poroelastic displacements, we project geodetic time series corrected for hydrological loading effects onto the dominant groundwater temporal functions. We interpret the extracted displacements in light of analytical solutions and a 2D model relating groundwater level variations to surface displacements. In particular, the relatively low estimates of elastic moduli inferred from the poroelastic displacements and groundwater fluctuations may be indicative of aquifer layers with a high fracture density. Our findings suggest that OPAS undergoes significant poroelastic deformation, including highly heterogeneous horizontal poroelastic displacements.

The IGS contribution to ITRF2020, based on the third IGS reprocessing campaign (repro3), comprises daily position estimates for 1905 globally distributed GNSS stations. As an essential step of the ITRF2020 preparation, a detailed analysis of the IGS repro3 station position time series has been carried out, including identification of offsets, modeling of post-seismic displacements, confrontation with deformation predicted by geophysical loading models, and characterization of systematic and random errors. This presentation covers the different aspects of this analysis, the methods used and the lessons learned.