AI-Infused Strategies for Mitigating Uncertainty in Continental-Scale Surface Mass Change Analysis Through GPS and GRACE-FO

Understanding mass movements at spatial scales below 300 km requires screening and analysing GPS data in combination with GRACE-FO gravity products, which is summed up in this abstract. Based on prior research recommendations, this study establishes a thorough procedure for GPS VLD estimations, with an emphasis on corrections for surface mass separation. It also sets processing standards and uncertainty quantification approaches. In order to fix mistakes that might cause different responses from different observation platforms, it stresses the need of better background modelling. Locating troublesome locations and outlining required repairs, such as fixing missing antenna offsets, is made possible by screening metrics. When considering hydrological signals, several uncertainty quantification approaches (e.g. root mean square (RMSE), random walk, white noise, flicker noise, and noise) provide insight into noise levels and spectrum distributions. Improving our understanding of surface mass fluctuations across North America and related uncertainties, this suggested approach sets the basis for formal data combining of GPS-GRACE-FO and provides a framework for future research to refine GPS station errors internationally.