Regional modeling of water storage variations 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 will introduce space-localizing radial basis functions as a more suitable parameterization of high-resolution regional water storage change. A closed-loop simulation environment has been set up to allow the testing of the setup and the tuning of the algorithm. In a first step only simulated GRACE data together with realistic correlated observation errors will be used in the Kalman filter to sequentially update the parameters of a regional gravity field model. However, the implementation was designed to flexibly include further observation techniques (GNSS, terrestrial gravimetry) at a later stage. This presentation will outline the Kalman filter framework, introduce the regional parameterization approach, and address challenges related to, e.g., ill-conditioned matrices and the proper choice of the radial basis function parameterization.