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 Center of Space Research, GeoForschungsZentrum, Institute of Geodesy at Graz University of Technology, and Jet Propulsion Laboratory, 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 neighbors to further reduce residual spatially uncorrelated noise. Comparing GRACE/‐FO M‐SSA solution with Satellite Laser Ranging 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 (3.0 mm EWH), without requiring a priori information nor regularization. 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. Plain Language Summary The Gravity Recovery and Climate Experiment and Follow‐On (GRACE/‐FO) satellite global measurements of changes in the Earth gravity field uniquely observe mass variations within and between the atmosphere, oceans, continental hydrology and ice. Yet, monthly data are polluted by noise in a North/South striping pattern, likely related to systematic errors and imperfect correction models. Moreover, the gap between missions hinders quantifying mass changes rates, which is essential for measuring and understanding the impacts of climate change and human activity on the evolving ice and freshwater resources. To overcome both issue