Improved regional mass change estimation using Slepian functions in a Kalman filter framework

Kurzfassung

Satellite gravimetry, such as data from the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO, provides insights into temporal variations in Earth’s gravity field, which are linked to mass redistribution processes like ice sheet melting and groundwater depletion. However, measurement noise and limited spatial resolution require filtering techniques to extract meaningful signals. In this context, Slepian functions—optimal basis functions for spatially localized signal representation—offer a way to mitigate signal leakage and enhance regional mass change estimates. Here, we present the integration of Slepian functions within a Kalman filter framework to improve signal estimation using a simulated dataset following Wöhnke et al. (2025). We use data from the European Space Agency (ESA) Earth System Model (ESM) for a region in central Europe, combined with realistic noise from GRACE. The fit of the simulated observations is compared to the signal obtained from the Kalman filter. Additionally, we compare our results to the implementation of radial basis functions within a Kalman filter framework, as presented in Wöhnke et al. (2015). We find that Slepian functions perform similarly to radial basis functions.