Regional modelling of water storage variations from combined GRACE/-FO and GNSS data in a Kalman filter framework

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.