Data-driven gap filling and spatio-temporal filtering of the GRACE and GRACE-FO records

Gravity Recovery And Climate Experiment and Follow On (GRACE/-FO) global monthly measurements of Earth's gravity field have led to significant advances in quantifying mass transfer. However, a significant temporal gap between missions hinders evaluating long-term mass variations. Moreover, instrumental and processing errors translate into large non-physical North-South stripes polluting geophysical signals. We use Multichannel Singular Spectrum Analysis (M-SSA) to overcome both issues by exploiting spatio-temporal information of Level-2 GRACE/-FO solutions, filtered using the DDK7 decorrelation and a new complementary filter, built  based on the residual noise between fully processed data and a parametric fit to observations. Using an iterative M-SSA on Equivalent Water Height (EWH) time series processed by CSR, GFZ, GRAZ, and JPL, we replace missing data and outliers to obtain a combined evenly sampled solution. Then, we apply M-SSA to retrieve common signals between each EWH time series and its same-latitude neighbours to further reduce residual spatially uncorrelated noise. Comparing GRACE/-FO M-SSA solution with SLR and SWARM low-degree Earth’s gravity field and hydrological model demonstrates its ability to satisfyingly fill missing observations. Our solution achieves a noise level comparable to mass concentration (mascon) solutions over oceans (3.2 mm EWH), without requiring \textit{a priori} information nor regularisation. While short-wavelength signals are challenging to capture using highly filtered spherical harmonics or mascons solutions, we show that our technique efficiently recovers localized mass variations using well-documented mass transfers associated with reservoir impoundments.